Jurnal Teknik Informatika dan Teknologi Informasi
Journal Title:Jurnal Teknik Informatika dan Teknologi Informasi (JUTITI) Initials: JUTITI Frequency: April, Agustus, dan Desember. Print ISSN: 2827-9379 Online ISSN:2827-9387 Editor in Chief: Dr. Eng. Ir. Wahyul Amien Syafei, S.T., M.T., IPM. DOI: 10.55606 Publisher: Lembaga Pengembangan Kinerja Dosen Jurnal Teknik Informatika dan Teknologi Informasi (JUTITI) : ISSN: 2827-9379 (cetak), 2827-9387 (Online) berfokus pada penerbitan artikel berkualitas tinggi yang didedikasikan untuk semua aspek penelitian, masalah, dan perkembangan terbaru di bidang Teknologi Informatika dan Teknologi Informasi.Jurnal ini terakreditasi SINTA 5 (Surat Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi Nomor 10/C/C3/DT.05.00/2025 tanggal 21 Maret 2025 tentang Peringkat Akreditasi Jurnal Ilmiah Periode I Tahun 2025) dimulai dari Volume 2 Nomor 2 Tahun 2022 sampai Volume 7 Nomor 1 Tahun 2027. Artikel dapat ditulis dalam bahasa Inggris atau bahasa Indonesia. Indexed by: Journal Sponsors and Partners :
Pengembangan Sistem Informasi Geografis Penerangan Jalan Umum Kota Pontianak dengan Integrasi Telegram Bot API
Public Street Lighting (PJU) plays a crucial role in supporting community activities at night while ensuring the safety, comfort, and aesthetics of urban environments. However, in many regions, including Pontianak City, numerous streetlights remain non-functional for extended periods due to delayed damage reporting and the lack of an efficient monitoring system. This research aims to create a Public Street Lighting information system based on Geographic Information System (GIS) technology, integrated with the Telegram Bot API, to enhance the efficiency of damage reporting and response management. The system allows both the public to submit reports of malfunctioning lights through a web-based application, where the backend automatically sends notifications to administrators and technicians via Telegram. Data were collected through spatial data mapping, documentation, and interviews with the Head of the Road Equipment Division at the Pontianak City Transportation Department, followed by system design using the Waterfall model and testing through blackbox testing. The system visualizes the distribution and condition of streetlights on an interactive WebGIS map and records damage reports in real-time. The findings indicate that the integration of WebGIS and Telegram Bot API enhances the responsiveness and transparency of streetlight maintenance management. This innovation contributes to the realization of a smart city ecosystem by promoting community participation, improving infrastructure service efficiency, and supporting data-driven decision-making in local governance.
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Kesenjangan Etika Profesional TIK dan Perangkat Lunak Bajakan: Kajian Literatur Kontekstual Indonesia
This conceptual-analytical literature review examines the gap between ethics and practice in Indonesian corporate settings regarding unlicensed software. Three patterns stand out. First, license compliance is frequently overshadowed by short term cost and benefit considerations, subjective norms, and delivery pressure at the unit level. Second, professional codes of ethics are weakly embedded in policies, processes, and role accountability, producing uneven decision paths across functions. Third, unlicensed software creates tangible exposure to malware, legal liability, loss of vendor support, and service disruption that accumulate into operational and reputational risk. To close this gap, the review outlines governance instruments tuned to local practice. COBIT 2019 aligns enterprise objectives with control design. ITIL 4, particularly Service Configuration Management (SCM) and Change Enablement, supports pre-deployment checks and traceable configuration baselines so unauthorized installations are blocked at the source. Complementary measures include curated legal substitutions (notably open-source options with clear policy and vulnerability monitoring), periodic audits that reconcile entitlements against installations, documented handling of detected violations, ethics education using Indonesian cases with post-training evaluation, and routine reporting of unit-level compliance metrics to executives. The contribution is a practical blueprint linking ethical principles honesty, respect for intellectual property and due care to concrete controls in ICT functions in Indonesia.
Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Jumlah Penduduk Menurut Jenis Kelamin dan Kabupaten di Sumatera Utara
North Sumatra Province has a large population and is spread across various districts, so an effective system is needed to manage and analyze population data. This research aims to implement the Learning Vector Quantization (LVQ) method in classifying population based on gender and district in North Sumatra. The LVQ method was chosen because of its ability to perform classification based on supervised learning that utilizes vector prototypes. The data used is sourced from the Central Bureau of Statistics (BPS) of North Sumatra in 2022 and analyzed using customized parameters in RapidMiner software. This research involves several stages, starting from data collection, UML-based system design, variable selection, to the application and testing of classification models. The results showed that the LVQ method was able to classify the population based on gender and district accurately and efficiently. It is expected that this classification system can be the basis for decision-making in regional development planning and accelerate government programs related to population distribution.
Penerapan Data mining untuk Clustering Kondisi Sosial Ekonomi Berdasarkan Kepemilikan Jaminan Kesehatan Menggunakan Algoritma K-Means
This study discusses the application of the K-Means Clustering algorithm in analyzing the socioeconomic conditions of communities in North Sumatra Province based on health insurance coverage. The background of this study stems from the continuing gap in access to and coverage of health insurance, which is influenced by differences in socioeconomic conditions between regions. The purpose of this study is to identify community groups with different patterns of health insurance ownership, analyze their influence on socioeconomic conditions, and explain the application of the K-Means algorithm as a data clustering method. The data used was obtained from the North Sumatra Provincial Statistics Agency (BPS) for the 2021–2023 period, with variables including BPJS Health Insurance Premium Assistance Recipients, BPJS Health Insurance Non-Premium Assistance Recipients, Private Insurance, and Insurance from Companies/Offices. The analysis process was carried out through the stages of variable selection, initial centroid determination, distance calculation using Euclidean Distance, and iteration. The results of the study show that there are two main clusters, namely clusters with high health insurance ownership rates and clusters with low ownership rates. In terms of the BPJS Health Insurance Variable for Contribution Assistance Recipients, the cities of Medan, Samosir, Tanjungbalai, Asahan, Batu Bara, Binjai, and Toba are included in the high cluster, while Deli Serdang is in the low cluster. These findings are expected to provide an overview for local governments in formulating policies to improve access to health insurance in a more targeted and data-driven manner.
Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Klasifikasi Tingkat Literasi Siswa
Literacy is a fundamental skill in reading, thinking, and writing that plays an important role in improving the quality of education. Indonesia faces serious challenges in literacy, and according to 2020 data from Statistics Indonesia, only 10% of Indonesians have an interest in reading. This study aims to implement the K-Nearest Neighbor (KNN) algorithm to classify students' literacy levels objectively and accurately. The research method used a quantitative approach with data collection through questionnaires administered to fourth, fifth, and sixth grade students at UPT SD Negeri 22 Barung-Barung. The variables used were reading books, reading duration, internet duration, and library visits. The data was divided into training data and 20 testing data. The classification process was carried out through the stages of data normalization, determining the value of K=5, calculating the Euclidean distance, and determining categories based on the majority of the nearest neighbors. The system was designed web-based using PHP and MySQL to support the automatic simulation process. The results showed that the KNN algorithm was able to classify students' literacy levels into three categories, namely low, medium, and high, with an accuracy rate of 55.00%. From the 20 testing data, there were 11 correct predictions and 9 incorrect predictions. The implementation of this system is expected to assist and serve as a basis for schools in designing more effective literacy interest improvement programs and is expected to increase
Pengembangan Sistem Informasi Berbasis Website untuk Manajemen Perjalanan Dinas dengan Metodologi System Development Life Cycle (SDLC) pada PT Mahanusa Hijau Selaras
PT Mahanusa Hijau Selaras is a consulting company that handles official business trip administration. The current process is still carried out manually using Microsoft Excel, including the submission of Travel Orders (SPD) and Travel Reports (LPD). This results in the accumulation of physical documents that need to be stored and risks causing delays, data duplication, and lack of real-time information transparency. This study aims to develop a web-based information system that simplifies the management process of business trip data, from submission, verification, to reporting, digitally and integrated. The methodology used is the System Development Life Cycle (SDLC) with a waterfall model approach, which includes stages of needs analysis, system design, implementation, and testing. This system is developed using PHP programming language with MySQL database. The results of the system development show improvements in efficiency, data accuracy, and ease of access to information for all parties involved. The system also significantly reduces the use of physical documents and simplifies the business trip administration process digitally and in real-time.
Determination of Preventive Maintenance Electrocardiogram Using the Analytical Hierarchy Process (AHP) Method
Abstract Electrocardiogram is a medium-tech diagnostic electromedical device that is widely available in healthcare facilities. Electrocardiogram maintenance is very important to maintain the performance and accuracy of reliable results for medical diagnosis. Suboptimal Electrocardiogram maintenance can be caused by various factors, including user negligence, improper use, component damage, and lack of preventive maintenance. Therefore, this study aims to determine the selection of preventive maintenance strategies on electrocardiograms that are right on target in order to minimize damage, maintain the reliability of the electrocardiogram, and provide optimal maintenance. The research method used is the Analytical Hierarchy Process (AHP). The results obtained from the AHP method are the Warehouse Backup criteria have the highest priority weight of 0.184 and the alternative with the highest priority weight is Periodic Maintenance of 0.3744, then followed by Repair Maintenance (Schedule Overhaul) of 0.3184 and the last is Condition Based Maintenance of 0.3072. The periodic maintenance strategy has the greatest weight, so it is chosen as the most appropriate strategy to be used as optimal maintenance and is expected to be an effective solution so that it can successfully reduce damage and maintain the reliability of the Electrocardiogram. Keywords: analytical hierarchy process (AHP), maintenance, maintenance strategy
Analisis Etika Profesi dan Pertanggungjawaban Pidana Influencer dalam Fenomena Penyebaran Judi Online di Media Sosial
The development of information and communication technology has brought about major changes in the way society interacts and conducts activities. Social media has now become a limitless public space that makes it easy for anyone to share information, express opinions, and even carry out various economic activities. This study aims to analyze the forms and limits of criminal liability for influencers who promote online gambling by referring to the provisions in the Criminal Code (KUHP) and the Electronic Information and Transactions Law (UU ITE). Furthermore, it examines the application of digital professional ethics principles and deontology principles in promotional activities carried out by influencers, in order to assess the extent to which these actions violate moral and professional obligations as public figures in the digital space. This study uses a normative legal method (normative juridical) with a qualitative-descriptive approach. This approach is used to examine and understand the positive legal norms stipulated in laws and regulations, as well as legal principles related to professional ethics and criminal liability. This study finds that the phenomenon of online gambling promotion by influencers on social media has negative social, economic, and moral impacts, especially for the younger generation. Based on a normative legal analysis, influencers who promote online gambling may be subject to criminal liability under the Electronic Information and Transactions Law (ITE) and the Criminal Code, particularly if proven to meet the elements of participation and intent.
Analisis Perbandingan Algoritma Random Forest, SVM, dan Logistic Regression untuk Menentukan Model Terbaik Prediksi Penyakit Diabetes
Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels caused by the body’s inability to produce or effectively respond to insulin. The increasing prevalence of diabetes in Indonesia requires accurate data-driven early detection systems to assist the diagnostic process. This study aims to compare the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest, and Logistic Regression—in predicting diabetes disease based on patient clinical data. The dataset used was obtained from the Kaggle repository titled 100,000 Diabetes Clinical Dataset. The research process was conducted using the Orange Data Mining software through several stages, including data preprocessing, One-Hot Encoding transformation, model training, and evaluation using the 10-Fold Cross Validation method. The results show that the Random Forest algorithm achieved the best performance with an accuracy of 97.1%, followed by Logistic Regression at 96.0% and SVM at 92.3%. These findings indicate that ensemble-based methods such as Random Forest outperform others in producing stable and accurate predictions for diabetes diagnosis
Komparasi Algoritma Machine Learning (Random Forest, Gradient Boosting, dan Ada Boosting) untuk Prediksi Tingkat Penyakit Alzheimer
Alzheimer’s disease is one of the most common forms of progressive dementia and has become a major global health challenge as the aging population continues to increase. Early detection of this disease is crucial to mitigate its social, economic, and health impacts. In this context, data-driven approaches using machine learning algorithms can be utilized to predict Alzheimer’s risk more accurately. This study aims to compare the performance of three ensemble learning algorithms—Gradient Boosting, Random Forest, and AdaBoost—in predicting the risk level of Alzheimer’s disease using the public Alzheimer’s Disease Dataset, which includes demographic, clinical, and lifestyle data. The research process involved several stages, including data preprocessing, splitting data into training and testing sets, model training using cross-validation, and performance evaluation based on accuracy, precision, recall, F1-score, and AUC metrics. The experimental results show that the Gradient Boosting algorithm achieved the best performance with an accuracy of 0.956, an F1-score of 0.956, and an AUC of 0.985, demonstrating its ability to capture complex non-linear relationships among features such as age, MMSE score, and lifestyle factors. Meanwhile, Random Forest and AdaBoost achieved competitive yet slightly lower performance. These findings indicate that ensemble boosting approaches, particularly Gradient Boosting, hold great potential for medical decision-support systems in the early detection of Alzheimer’s disease and can serve as a foundation for developing more accurate and adaptive predictive models in the future.
Analisis Pola Pergerakan dan Prediksi Harga Emas Menggunakan Regresi Linear serta Model Time Series ARIMA dan VAR
Gold is one of the most popular investment instruments due to its stable value and ability to protect assets against inflation. However, its price tends to fluctuate significantly, influenced by macroeconomic factors such as exchange rates, interest rates, and global geopolitical conditions. This study aims to analyze the movement patterns and predict gold prices based on historical data from 2019 to 2024 using the Linear Regression method and Time Series models, namely ARIMA and VAR. The analysis process was carried out using Orange Data Mining software, which enables the application of machine learning algorithms through a visual and interactive interface without manual coding. The dataset used consists of daily gold closing prices, processed and tested to evaluate model accuracy using Root Mean Square Error (RMSE) and Correlation Coefficient (R) indicators. The results indicate that the Linear Regression model effectively captures the general trend of gold prices, while ARIMA and VAR models produce more accurate forecasts based on historical fluctuations. The integration of regression and time series approaches improves prediction reliability. Overall, this research contributes to the development of financial data analysis and provides insights for investors in making more informed and data-driven investment decisions.
Pengenalan Masakan Tradisional Indonesia Menggunakan Game Edukasi Roblox
This research aims to develop an educational game for cooking traditional Indonesian cuisine based on Roblox Studio as an interactive learning medium that is able to attract the attention and interest of the younger generation. This game is designed not only as a means of entertainment, but also as an educational medium that introduces the culinary richness of the archipelago through a simulation of the cooking process that collapses and resembles real cooking steps. Three traditional dishes were chosen as the main focus in this game, namely Rawon Ponorogo, Soto Makassar, and Minangkabau Rendang. The three dishes were chosen because they have different tastes, cooking techniques, and cultural characteristics, so that players can understand that traditional Indonesian food is rich in variety and historical value. The development method used is the Waterfall method, which includes the stages of analyzing user and system needs, designing the interface and game mechanics, implementing using the Lua programming language in Roblox Studio, testing the game's functionality, and evaluating the results through the distribution of questionnaires to users. This game makes use of various interactive elements such as ProximityPrompt to trigger player actions, GUI Progress Bar as an indicator of the cooking process, Timer to set a time limit according to the difficulty level, and a Teleport system between stages to guide the gameplay. The existence of a different time limit system at each stage (10 seconds, 5 seconds, and 3 seconds) provides a challenge that can increase the player's concentration in following the correct cooking steps. The test results show that the game can run well and can be played easily by novice users. Based on the results of the questionnaire, users stated that this game is visually appealing, easy to understand, and effective in introducing Indonesian culinary culture. Therefore, this game functions as a creative digital learning medium in preserving the culinary of the archipelago.
Penerapan Algoritma K-Means untuk Pengelompokan Kerentanan Wilayah terhadap Kasus DBD di Kota Bandung
Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through bites of the Aedes aegypti mosquito. This illness remains a major public health concern in Indonesia, particularly in urban regions like Bandung City, where population density and environmental variations contribute to disease transmission. The purpose of this study is to apply the K-Means Clustering algorithm to group areas based on their level of vulnerability to DHF spread in Bandung City. The dataset, obtained from the Bandung Open Data portal covering the 2016–2024 period, was processed using the Orange Data Mining application. The analysis began with data preprocessing, which included cleaning, attribute selection, and normalization to ensure optimal clustering performance. The data were then grouped into three primary clusters representing high, medium, and low risk zones. The findings indicate that the K-Means algorithm effectively detects the spatial and temporal distribution of DHF cases and presents it through scatter plot visualizations that illustrate yearly patterns. High-risk regions are typically characterized by dense population, poor sanitation, and limited environmental management. These findings provide essential insight for local health authorities to design more targeted prevention and control strategies. Furthermore, this research can serve as a foundation for developing a decision support system that aids in monitoring, evaluating prevention efforts, and optimizing health resource allocation to reduce the incidence of DHF in the future.
Efektivitas Pelayanan Pendaftaran Diklat Berbasis AIRIS (Aplication Integrated Registration System) terhadap Kepuasan Peserta Diklat di Poltekpel Banten
Digital transformation in public services is an effort to improve efficiency, transparency, and quality of service. Banten Maritime Polytechnic (Poltekpel Banten) as a Public Service Agency (BLU) has implemented the AIRIS (Application Integrated Registration System) online registration system to support the recruitment process of training participants, both new training participants and those upgrading their diplomas or certificates of seafaring skills training. The research method used is descriptive quantitative with data collection through questionnaires distributed to training participants who have used the AIRIS system. Data analysis was carried out using a Likert scale approach and effectiveness tests based on indicators of system quality, information quality, and service quality. The results of the study show that the implementation of AIRIS has a positive impact on increasing the efficiency of the registration process, data transparency, and ease of access to information for participants. Most respondents expressed a high level of satisfaction with the ease of use and speed of service provided by the system. However, several technical challenges, such as network disruptions and limited user guides for new users, were still encountered. Overall, AIRIS was deemed effective in improving the quality of training registration services and contributing to increased participant satisfaction. Therefore, it is recommended that it be further developed through feature optimization and increased digital infrastructure capacity.
Perancangan dan Implementasi Pelayanan Puskesmas Pembantu (Pustu) di Desa Kali Cinta Berbasis web
Improving the quality of basic health services is a priority, driving innovation at the Kali Cinta Village Community Health Center (Pustu). This Pustu still faces significant operational challenges, including long queues, inaccessible service information, and manual registration and medical record processes. These conditions result in suboptimal patient data management, frequent data duplication, and slow service delivery, ultimately reducing the efficiency and quality of Pustu services. This study aims to design and implement a computerized web-based Pustu service system. Data collection was conducted through direct observation of service flows, interviews with Pustu staff and the community to understand needs, and a literature review as a theoretical basis. Software development was conducted systematically using the Waterfall method. The expected findings from the implementation of this system are better access to information, accelerated registration processes, and efficient patient data management. The implications of this system are a significant improvement in the quality of health services and community satisfaction in Kali Cinta Village.
Analisis dan Perancangan Sistem Informasi Pelayanan Pelanggan Berbasis Web pada King Kuphi
The development of information technology has had a significant impact on improving service quality in various sectors, including the culinary industry. KingKuphi, as a culinary business, still uses a manual system for ordering and managing customer data, resulting in various obstacles such as service delays and recording errors. This research aims to design a web-based customer service information system that can automate the ordering process, menu management, and payment transactions in an integrated manner. The development method used is the System Development Life Cycle (SDLC) with the Waterfall model, which includes the stages of requirements analysis, design, implementation, verification, and maintenance. The results show that the designed system can improve efficiency and accuracy in service management by providing digital menu features, automatic ordering, order notifications, and digital receipt printing. With the implementation of this system, the service process becomes faster, more structured, and more integrated between customers, waiters, the kitchen, and the cashier. This information system is expected to improve service quality and customer satisfaction, while supporting KingKuphi's operational management in a more modern and efficient manner.
Sistem Informasi Inventaris Barang Berbasis Web pada Kelurahan Kotabumi Pasar Lampung Utara
This research aims to design and implement a Web-Based Goods Inventory Information System at Kotabumi Pasar Village, North Lampung, to address the challenges associated with manual data collection and asset management. The system was developed using the SDLC Waterfall method, with the PHP programming language and MySQL database. It is designed to provide an intuitive interface and robust functionality, including features for adding, editing, deleting, and searching inventory data, as well as generating automatic reports. By automating these processes, the system seeks to improve the efficiency and accuracy of goods inventory management, reducing the risk of human error and increasing transparency in asset tracking. Additionally, the system is expected to enhance the village administration’s ability to monitor and make informed decisions about asset management, ultimately supporting better organizational performance and resource allocation. This study demonstrates the value of adopting digital solutions to modernize administrative tasks in local government settings, fostering more effective and sustainable governance practices.
Penerapan Metode Logistic Regression untuk Memprediksi Potensi Penyakit Liver pada Pasien
Liver disease is a major global health concern that often goes undiagnosed in its early stages due to the absence of specific symptoms. Implementing data-driven approaches for early detection can significantly enhance diagnostic accuracy and improve clinical outcomes. This study aims to develop a predictive model using the Logistic Regression algorithm to identify individuals at high risk of liver disease. The data analysis process was conducted visually through data mining software, encompassing several stages such as data loading, feature selection, exploratory data analysis, and model evaluation. The dataset includes various clinical and laboratory attributes of patients, such as blood test results, liver function indicators, and demographic factors. The model’s performance was assessed using multiple evaluation metrics, with a focus on Classification Accuracy (CA) and the Area Under the ROC Curve (AUC) to measure predictive precision and classification ability. The results show that the Logistic Regression model achieved an accuracy of 71.8% and an AUC score of 0.746. These findings indicate that the model demonstrates good predictive performance and effectively identifies early-stage liver disease cases. However, further optimization is necessary to improve overall model efficiency and ensure more robust predictive capabilities in clinical applications.
Klastering Penyakit Diabetes Melitus dengan Algoritma K-Means berdasarkan Karakteristik Klinis
Diabetes Mellitus is a complex and progressive chronic metabolic disorder that requires a personalized management strategy tailored to each individual’s clinical, physiological, and lifestyle characteristics. Addressing this challenge, the present study aims to apply the K-Means algorithm to identify clustering patterns among diabetic patients using the Knowledge Discovery in Databases (KDD) framework. The dataset was obtained from the Kaggle repository, consisting of 769 patient medical records with key variables such as glucose levels, body mass index (BMI), blood pressure, age, and other metabolic parameters relevant to the diagnosis of Diabetes Mellitus. The research methodology includes several stages: data selection, preprocessing to handle missing values, duplication, and normalization to ensure the dataset is properly structured for analysis. The implementation of the K-Means algorithm was carried out using Orange Data Mining software to produce optimal clustering patterns. The analysis identified three primary clusters (C1, C2, C3) that demonstrated significant differences, particularly based on glucose levels as the dominant variable in cluster formation. The scatter plot visualization revealed clear separations among clusters, with high intra-cluster homogeneity and strong inter-cluster heterogeneity. These findings confirm the effectiveness of the K-Means algorithm as an unsupervised learning method capable of uncovering hidden patterns within clinical diabetes data. The results are expected to serve as a foundation for developing more adaptive and precise clinical decision support systems, assisting healthcare professionals in designing targeted management and intervention strategies aligned with each patient’s risk profile.
Penerapan dan Perbandingan Algoritma SVM, Naive Bayes, dan Gradient Boosting dalam Prediksi Stroke
Stroke is a major cardiovascular disease that significantly contributes to global mortality and disability rates. Early detection through stroke risk prediction is essential in reducing its impact. This study focuses on evaluating and comparing the performance of three machine learning algorithms—Support Vector Machine (SVM), Naive Bayes (NB), and Gradient Boosting (GB)—in predicting stroke occurrence. The research utilizes the Healthcare Stroke Dataset, which contains 5,109 records and 11 predictor variables. Modeling was performed using Orange Data Mining software, with 70% of the data allocated for training and 30% for testing. The results show that the SVM algorithm achieved the highest performance, obtaining an AUC score of 0.919 and an accuracy of 96.0%, followed by Gradient Boosting with an AUC of 0.885 and accuracy of 95.2%, and Naive Bayes with an AUC of 0.803 and accuracy of 88.2%. Therefore, SVM is identified as the most effective algorithm for predicting stroke risk within this dataset.
Unauthorized access incidents often occur stealthily, with password spraying attacks resulting in the misuse of legitimate credentials. This study reconstructs a real-world incident using system logs from Identity Provider/Single Sign-On (IdP/SSO), Security Information and Event Management/Endpoint Detection and Response (SIEM/EDR), and application-level sources. The attack techniques were mapped to the MITRE ATT&CK framework, focusing on T1110 (Brute Force) and T1078 (Valid Accounts). A Data Protection Impact Assessment (DPIA) was conducted based on the Indonesian Personal Data Protection Law (Law No. 27 of 2022), complemented by a gap assessment against ISO/IEC 27001 and 27002 controls. The results show that the attack’s success was driven by incomplete Multi-Factor Authentication (MFA) deployment, the continued use of legacy/basic authentication, weak adaptive rate-limiting and lockout mechanisms, and a monitoring system limited to alert-only functions. The DPIA identified exposure of thousands of personal data records with medium-to-high privacy risks, particularly concerning confidentiality breaches and identity impersonation, necessitating possible notification to authorities and affected data subjects. The study recommends enforcing MFA across all access channels, disabling legacy authentication, implementing risk-based or step-up authentication, activating automatic blocking for password spraying and impossible travel anomalies, extending DPIA coverage during control changes, and updating the Statement of Applicability to reflect modern security controls. Strengthening identity protection and adopting preventive monitoring are shown to significantly reduce privacy risks while easing compliance obligations.
Pembuatan Game Visual Novel “One Year Of Time” pada Platform Website
The game “One Year of Time” is a form of interactive entertainment that engages players in rule-based activities with specific goals. Besides recreation, games also help develop critical thinking, problem-solving, strategic planning, and hand-eye coordination skills. “One Year of Time” is a 2D romance visual novel developed using Unity and programmed in C# for the web platform. Players take on the role of a god named Aion, who is descended to the human world due to a violation of the laws imposed by Zeus and is punished with a one-year time limit to find a partner—or face extinction. Along the way, players meet three main female characters, each with a unique personality. Each choice they make affects their relationships and determines the outcome of the story. Through these decisions and challenges, players explore the meaning of love, human relationships, and the consequences of their actions. Testing of this game was conducted. The game was tested using three methods: black box testing, an alpha test conducted by a supervisor, and a beta test conducted by 33 participants. The test results indicated that "One Year of Time" was considered engaging, easy to understand, and potentially well-received by players. This demonstrates the game's entertainment value and replayability. Therefore, it can be concluded that the game successfully provided a positive gaming experience and has potential for further development.
Pengembangan Animasi Edukatif Berbasis Cerita Rakyat untuk Pelestarian Budaya Lokal
This study aims to analyze the development of educational animation based on Indonesian folklore as a medium for preserving local culture. The method used is a systematic literature review (SLR) of various national and international journal publications from 2021 to 2025 that discuss educational animation, local wisdom, and cultural preservation. Data were collected from ScienceDirect, ResearchGate, and SINTA databases using the keywords “educational animation,” “folklore,” and “cultural preservation.” The data were analyzed qualitatively using content analysis and thematic mapping techniques to identify key themes and recent research directions. The results show that educational animation based on folklore significantly contributes to enhancing cultural awareness, character education, and student engagement in learning. Most studies emphasize that digital media—especially animation—play an important role in transmitting cultural and moral values to younger generations. However, research gaps remain, as most existing studies still focus on the entertainment aspect rather than educational integration. In conclusion, the development of educational animation based on folklore has the potential to serve as a strategic medium for intangible cultural heritage preservation while promoting creative and contextual learning in the digital era.
Simulasi Jaringan untuk Sistem Terdistribusi Website “Simple Restaurant App” dengan GNS3
Distributed systems, integral to contemporary computing, involve the coordinated functioning of autonomous nodes across interconnected networks. Architectural models like client-server and peer-to-peer configurations define their structure, presenting both advantages and challenges. Challenges stem from the intricacies of managing geographically dispersed nodes, encompassing issues of data consistency, fault tolerance, scalability, and security. Overcoming these hurdles demands advanced algorithms and protocols, especially for achieving consistency and resilience in the face of failures. Scalability is a critical consideration, and security concerns add complexity to their design. Current trends in distributed systems, such as edge computing, serverless architectures, and blockchain technologies, aim to address these challenges and enhance system capabilities. Edge computing optimizes proximity to data sources, serverless architectures streamline resource utilization, and blockchain offers decentralized and tamper-resistant solutions. Understanding these architectures, addressing challenges, and embracing emerging trends are pivotal for constructing robust and efficient distributed systems that align with the demands of the interconnected digital landscape.
Sistem Deteksi Penggunaan Helm Pada Pengendara Sepeda Motor di Indonesia Menggunakan Perbandingan Model YOLOv8 dan RT-DETR
Road safety is an important aspect in reducing accident risks, especially for motorcycle riders. To improve compliance with helmet use, this study compares the performance of two deep learning–based object detection models, namely YOLOv8 and RT-DETR, using a Roboflow dataset consisting of 3,735 images with two classes: with helmet and without helmet. The research process includes data acquisition, preprocessing (512×512 pixels), model training conducted in Visual Studio Code using an Nvidia GTX 1070 Ti GPU with the Ultralytics framework (100 epochs, AdamW optimizer, 0.0005 learning rate, 25 patience), testing on images, videos, and real-time inputs using last.pt, as well as evaluation through precision, recall, mAP, and confusion matrix, followed by implementation of the best algorithm in a local Streamlit web application.The results show that RT-DETR achieved slightly better training performance in terms of mAP50–95, while YOLOv8 performed better during real-world testing with more stable accuracy, particularly for the with helmet class. YOLOv8 reached up to 100% accuracy in video and real-time testing, whereas RT-DETR performed better in the without helmet class, achieving 95% accuracy on image data and up to 100% in video testing. Overall, YOLOv8 was selected as the best model for implementation in the Streamlit-based helmet detection application because it is faster, more stable, and more accurate. This system has the potential to support intelligent ETLE enforcement to enhance traffic safety in Indonesia.
Klasterisasi K-Means dan Model Markowitz dalam Pembentukan Portofolio Optimal Saham Syariah
This research seeks to construct an optimal portfolio of Sharia-compliant stocks listed in the Jakarta Islamic Index (JII) by integrating the Markowitz model with K-Means clustering. The dataset comprises the daily closing prices of JII constituent stocks and the risk-free return of BI-rate for the period of June–October 2025. The analysis begins with calculating the mean of stock returns and its corresponding standard deviation to identify the characteristics of each stock. The Silhouette Index is employed to determine the most suitable number of clusters, which indicates that three clusters provide the best separation. A representative stock is then selected from each cluster based on the Sharpe Ratio, resulting in BRPT, ASII, and UNVR as the primary candidates for portfolio construction. The Markowitz model is subsequently applied to determine the optimal portfolio weights, yielding an allocation of 16.29% for BRPT, 70.80% for ASII, and 12.91% for UNVR. The performance evaluation shows that the optimal portfolio achieves a Sharpe Ratio of 0.232872, higher than the JII index value of 0.118273, indicating superior risk–return efficiency. These findings demonstrate that a hybrid approach combining K-Means clustering and Markowitz optimization can enhance investment decision-making for Sharia-compliant stocks.
Perancangan dan Implementasi Sistem Informasi Nasabah pada BSI KCP Gunung Tua Studi Kasus Data Nasabah
The development of information technology has influenced the way various sectors work, including banking. Previously, customer data management was done manually, which often led to data duplication and time delays. This study aims to create and implement a more effective customer information system at Bank Syariah Indonesia (BSI) Gunung Tua Branch Office (KCP), so that the data management process becomes faster and more accurate. This study used the R&D method with the Waterfall model. The system was created using the PHP or Laravel programming language and relied on a MySQL database. The results showed that the system was successfully created and implemented. This system supports two main types of users: Admin/Customer Service who can log in, manage customer data (input, change, delete, and search), and print reports; and Customers who can view personal data, monitor savings status, and create new accounts. In conclusion, this system has an integrated database structure (Admin, Customer, Account, Transaction tables) and successfully meets user needs, thereby increasing efficiency and accuracy in customer data management at BSI Gunung Tua KCP.
Klasifikasi Resiko Depresi Berdasarkan Durasi Layar dan Pola Hidup Digital Mengguanakan Metode K-Nearest Neighbor (KNN)
This study aims to classify depression risk levels based on screen time and digital lifestyle patterns using the K-Nearest Neighbor (KNN) method. The dataset used includes several important variables, such as daily screen time, frequency of social media use, and sleep duration and quality. These variables were chosen because they are considered to have a strong association with mental health, particularly depressive symptoms that often arise from excessive digital device use. A KNN model was then developed and tested to categorize individuals into three depression risk categories: low, medium, and high. The evaluation results showed that the model with a k value of 5 achieved a predictive accuracy of 85%, indicating that this method is quite effective as a data-driven classification tool. The findings of this study suggest that digital lifestyle patterns can be an early indicator in predicting depression risk, thus requiring more systematic monitoring. However, this model still needs to be combined with clinical assessment for a more comprehensive and accurate diagnosis.
Pengaplikasian Algoritma C4.5 untuk Menganalisis Hubungan Kebiasaan Harian Siswa terhadap Prestasi Akademik
This study aims to analyze the relationship between students’ daily habits and their academic performance using the C4.5 algorithm. The daily habit variables examined include study duration and intensity, sleep quality and patterns, frequency and type of gadget use, attendance consistency, and students’ engagement in learning activities both inside and outside the classroom. The data were collected through questionnaires and combined with students’ academic grades as indicators of performance. The C4.5 algorithm was employed to construct a decision tree model capable of identifying the daily habit attributes that have the most significant influence on academic achievement. The findings reveal that study intensity, sleep quality, and attendance consistency serve as dominant factors in predicting students’ performance levels. Furthermore, the resulting decision tree model demonstrates a relatively high accuracy level, making it a useful tool for evaluation, student development planning, and decision-making processes by educators. These results confirm that the application of the C4.5 algorithm is effective in uncovering the relationship patterns between daily habits and academic achievement and has the potential to serve as a reference for efforts to improve students’ learning quality.
Klasifikasi Indikator Kesehatan Diabetes Menggunakan Algoritma Random Forest
Diabetes continues to rise as a global health concern, highlighting the need for analytical methods that can assist in earlier and more accurate detection. This study aims to classify diabetes conditions using the Random Forest algorithm implemented through the Orange Data Mining platform. The dataset used contains various health-related attributes such as glucose levels, blood pressure, body mass index, age, and other clinical indicators associated with diabetes risk. Random Forest was selected due to its ability to produce stable models, handle large and complex datasets, and minimize overfitting by combining multiple decision trees. The research process includes data preprocessing, splitting the dataset into training and testing portions, building the Random Forest model, and evaluating its performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that Random Forest delivers strong and consistent performance in classifying diabetes conditions based on the given health indicators. These findings suggest that employing data mining techniques especially Random Forest within Orange—can serve as a practical and reliable approach to support medical analysis and assist healthcare practitioners in achieving earlier and more accurate diabetes detection.
Perancangan Sistem Informasi Pengadaan Barang Berbasis Web pada PT XYZ Kota Depok
The procurement process is a critical activity that supports the operational continuity of a company. However, manual procurement procedures often lead to issues such as delayed documentation, data duplication, inaccurate information, and difficulty in tracking records. This study aims to design a web-based procurement information system for PT XYZ in Depok City to ensure that procurement activities are managed in an integrated, efficient, and accurate manner. The development method used in this research is the Prototype model, which allows direct interaction between developers and users, enabling iterative validation of system requirements. Data were collected through observation, interviews, and literature review to obtain a comprehensive understanding of user needs. The system design includes process modeling using UML diagrams, database modeling with ERD and LRS, and the creation of a system interface prototype. Based on black-box testing, essential functions such as user authentication performed as expected. The resulting system design is considered capable of improving data recording effectiveness, accelerating ordering processes, and simplifying procurement monitoring. This research serves as a foundation for future system development and full implementation within the company.
Comprehensive Cybersecurity Framework for Digital Governance: Threat Assessment, Risk Mitigation, and Regulatory Compliance in Indonesia
Digital transformation of government administration brings significant benefits in improving public service efficiency and citizen access to information. However, digitalization also opens opportunities for increasingly complex and organized cyber threats. This journal explores a comprehensive cybersecurity framework for digital governance through an extensive literature review that includes threat assessment, risk mitigation strategies, and regulatory compliance analysis. This research analyzes international frameworks (NIST CSF 2.0, ISO/IEC 27001:2022, COBIT 2019), Indonesian national standards (Law No. 1 of 2024 on Information and Electronic Transactions, SPBE, BSSN), and best practices in incident response and Zero Trust Architecture. Results demonstrate that government cybersecurity requires a holistic approach integrating technical aspects, policy, human resources, and governance. This journal recommends implementing a comprehensive cybersecurity framework, enhancing human capital capacity, adopting cutting-edge technology, and fostering inter-institutional coordination to build sustainable cybersecurity resilience for government entities.
Penerapan Algoritma Association Rule Mining (Apriori) untuk Analisis Pola Pembelian (Market Basket Analysis) pada Data Transaksi Ritel
Market Basket Analysis (MBA) is a data mining technique used to identify product combination patterns that frequently occur together within a single transaction. This study applies the Apriori Algorithm as the primary method for discovering Association Rules from a large-scale e-commerce retail transaction dataset consisting of approximately 500,000 records, focusing on consumer purchasing behavior in the United Kingdom market. The research follows a systematic data mining process that includes data integration, data cleaning to remove anomalies such as negative prices and quantities, and data transformation using One-Hot Encoding to convert transaction records into a suitable binary matrix format. The Apriori Algorithm is then used to generate frequent itemsets, which are evaluated using Support, Confidence, and Lift to determine their strength and significance. The results show several strong Association Rules with Lift values greater than 1.0, indicating positive correlations between specific product pairs. These findings offer useful insights into consumer purchasing tendencies and can support various retail strategies, such as improving product recommendation systems, optimizing store layout, enhancing promotional bundles, and strengthening targeted marketing efforts. Overall, this study demonstrates that Apriori-based MBA is capable of extracting actionable knowledge from large-scale retail datasets and contributes to more effective, data-driven decision-making in the retail sector.
Sistem Pendukung Keputusan Penerimaan Anak Asuh Baru di Panti Asuhan Al-Washliyah Pulo Brayan Menggunakan Metode Multi-Attribute Utility Theory (Maut)
This study discusses the development of a Decision Support System (DSS) to assist the process of accepting new foster children at the Al-Washliyah Pulo Brayan Orphanage. The previous foster child selection process was conducted manually and subjectively, making it vulnerable to unfairness and assessment errors. This study applies the Multi-Attribute Utility Theory (MAUT) method to objectively assess foster children based on eight criteria: age, family circumstances, prospective economic conditions, health, education, legal status, independence, and behavioral and social well-being. This study uses a web-based software engineering approach with the PHP programming language and a MySQL database. The results show that the application of the MAUT method can accelerate the decision-making process, improve fairness in selection, and minimize subjective bias from decision-makers. The developed system is expected to serve as a reference for other orphanages in developing more efficient and transparent digital-based admission systems.
Sistem Infromasi Geografis Pencarian Rute Terpendek Klinik Bersalin Di Kota Tebing Tinggi Menggunakan Algoritma Floyd Warshall Berbasis Android
Maternity clinics are often spread across different locations, making it difficult for expectant mothers to find facilities that match their preferences and needs. As a result, many pregnant women who require routine check-ups, pregnancy programs, or delivery services tend to depend on recommendations from relatives without having adequate knowledge about the clinic’s quality, which can lead to dissatisfaction. To overcome this problem, this study develops a geographic information system that incorporates the Floyd–Warshall algorithm to determine the shortest route to each maternity clinic and integrates Mapbox to visualize clinic locations interactively. The research applies quantitative data collection methods and adopts the waterfall system development model to ensure a structured and systematic process. The system aims to offer practical, accurate, and easily accessible information regarding clinic locations, facilities, and routes, thereby helping pregnant women make more informed decisions when selecting maternity services that best suit their needs.
Sistem Deteksi Penyakit pada Tanaman Cabai Menggunakan RT-DETR dan YOLLOv8
This study investigates the performance of two state-of-the-art object detection models, YOLOv8 and RT-DETR, in identifying diseases in chili plants, which represent a major challenge affecting horticultural productivity. Diseases such as anthracnose and Cercospora leaf spot often cause significant yield losses, and traditional manual identification tends to be inefficient, subjective, and error-prone due to the visual similarities found among disease symptoms. The objective of this research is to evaluate and compare the capabilities of both models using the Chili dataset from Roboflow Universe consisting of four classes: Anthracnose, Cercospora Leaf Spot, Healthy Fruit, and Healthy Leaf. The methodology includes data preprocessing, training using identical hyperparameters, and performance evaluation through accuracy and model behavior analysis during real-world testing. The findings indicate that RT-DETR achieves higher accuracy in controlled testing, reaching 90% for Anthracnose, 95% for Healthy Leaf, 100% for Healthy Fruit, and 85% for Cercospora Leaf Spot, supported by its transformer-based architecture that enhances spatial understanding. However, YOLOv8 demonstrates superior stability and consistency in real-world scenarios involving varying lighting, leaf orientations, and natural texture variations. The model also produces fewer misclassification errors, making it more reliable for practical field deployment. The implications of these results show that YOLOv8 is the most suitable model for integration into a Streamlit-based application due to its fast, responsive, and accurate inference, supporting early disease detection for chili farmers.
Pengaruh Perkembangan Teknologi AI terhadap Kenaikan Harga GPU STMIK PESAT NABIRE
The rapid evolution of artificial intelligence (AI) has triggered a transformative shift in global computing demand, particularly for graphics processing units (GPUs), which have become essential for handling large-scale parallel computations. This study explores how the accelerating development and adoption of AI contribute directly to rising GPU prices, emphasizing the interplay between technological growth, supply chain constraints, and semiconductor manufacturing limitations. By utilizing qualitative and literature-based approaches, the research examines academic publications, industry reports, and online datasets to evaluate pricing trends, production bottlenecks, and the broader economic implications of AI’s expansion. Findings indicate that AI-driven applications, such as generative models, predictive analytics, and automated decision-making systems, have significantly increased the demand for high-performance GPUs, placing additional pressure on semiconductor supply chains already limited by production capacity, geographical concentration, and fluctuating global market conditions. These constraints create persistent price inflation, highlighting a strong causal link between AI deployment and GPU market volatility. The study also identifies structural weaknesses in supply chain systems, including overdependence on a small number of manufacturers and delays in scaling fabrication technologies. The results offer a clearer understanding of how AI’s rapid growth reshapes hardware economics and complicates accessibility for education sectors, small businesses, and independent developers. Furthermore, the study underscores the need for sustainable strategies, such as diversifying semiconductor production, exploring alternative computing architectures, and improving forecasting models to support balanced technological advancement.
Klasifikasi Wajah untuk Rekomendasi Gaya Rambut Menggunakan SVM dan Random Forest
The goal this project is to create a face-shape classification and hairstyle recommendation system by combining Support Vector Machine (SVM) and Random Forest (RF) algorithms with Histogram of Oriented Gradients (HOG) feature extraction. This study is motivated by the growing demand for individualized appearance support, as many users find it difficult to find haircuts that complement their face features. The method first preprocesses facial photos, uses HOG to extract key geometric and texture-based features, and then uses SVM and RF models to categorize the images. For training, validation, and testing, a dataset of five different face shapes is utilized. According to experimental results, the Random Forest model has an accuracy of about 89%, while the SVM model achieves an accuracy of about 95%. These findings suggest that SVM is better suited for managing high-dimensional feature spaces generated by HOG extraction. A recommendation system that offers hairstyle recommendations based on the anticipated face shape is then integrated with the trained model. The system is useful for real-time use since it can process pictures taken with the camera or uploaded from the gallery. Overall, this study shows that integrating HOG with SVM offers a dependable basis for creating customized hairdo recommendations as well as an efficient method for face-shape classification.
Peran Pemanfaatan Data Konsumen dalam Meningkatkan Omzet UMKM di Era Digital: Studi Kasus pada UMKM Kalimantan Barat
This study aims to analyze the role of consumer data management in increasing the revenue of micro, small, and medium enterprises (MSMEs) in West Kalimantan Province. A total of 50 MSME entrepreneurs from various sectors—including culinary, fashion, handicrafts, and beauty products—participated as respondents through Likert-scale questionnaires and semi-structured interviews facilitated by the Cooperative Office on Jl. Dr. Soetomo, Pontianak. The findings indicate that consumer data utilization has a positive and significant effect on revenue growth, with the most influential aspects being data-driven promotion and customer record management. Conversely, digital device usage and consumer behavior analysis remain relatively underdeveloped. These results highlight that digital literacy and systematic consumer data management are essential factors in supporting digital transformation and enhancing MSME competitiveness. The study recommends strengthening collaboration between the government, higher education institutions, and MSME actors to improve data management capabilities as a foundation for strategic business decision-making.
Sistem Informasi Penerbitan Surat Izin Berlayar Menggunakan Metode First In First Out (FIFO) di Kantor Kesyahbandaran dan Otoritas Pelabuhan Utama Belawan
The issuance of sailing permits at the Belawan Harbormaster and Main Port Authority Office (KSOP) still faces various obstacles, particularly related to the accumulation of application files, irregular queues, and long document processing times. These problems impact service efficiency and user satisfaction. This study aims to design and implement a web-based information system that applies the First In First Out (FIFO) method to improve the effectiveness of the sailing permit issuance process. The study uses a Waterfall approach, which includes the stages of needs analysis, system design, implementation, and functional testing using Blackbox Testing. The results show that the application of the FIFO method to the web-based system is able to organize the service flow more fairly based on the chronological order of submissions, thereby reducing the risk of service discrimination and minimizing input errors that previously often occurred in manual processes. The system is also able to accelerate the process of verification, data validation, and printing of permits, resulting in more efficient service times. In addition, transparency of the service process is increased through real-time monitoring of application status. Overall, the information system developed has been proven to improve service quality and public trust in the performance of KSOP Belawan
Sistem Pendukung Keputusan Berbasis Web untuk Penerimaan Bantuan Langsung Tunai Menggunakan Metode Multi Attribute Utility Theory (MAUT) di Desa Selemak
This study aims to develop a web-based decision support system to assist the selection process for Direct Cash Assistance (BLT) recipients in Selemak Village. The current manual selection process often leads to unfairness and subjectivity. This system was developed using the Multi-Attribute Utility Theory (MAUT) method, which is capable of providing selection results based on predetermined criteria and weighting. The research stages include collecting data on potential recipients, determining criteria, weighting, and implementing the system using PHP and MySQL. The system was evaluated using the K-Nearest Neighbor (KNN) algorithm to measure the accuracy of the calculation results. The results showed that the system was able to provide recipient recommendations with an accuracy of 96%, with 68 eligible families and 107 ineligible families. With this system, the process of determining BLT recipients is faster, more objective, and more transparent.
Memprediksi Jenis Tanaman Berdasarkan Parameter Lingkungan Menggunakan Algoritma Decision Tree dan Random Forest Berbasis Web
The advancement of information technology and artificial intelligence has created new opportunities to enhance efficiency and accuracy in the agricultural sector. One major challenge faced by farmers is determining the most suitable crop type based on environmental factors such as temperature, humidity, soil pH, rainfall, and light intensity. This study aims to develop a web-based crop prediction system using Decision Tree and Random Forest algorithms to support farmers in making data-driven decisions. The research methodology includes environmental data collection, data preprocessing, model training using both algorithms, performance evaluation based on accuracy metrics, and deployment of the best-performing model into a web application. The system allows users to input environmental parameters and obtain real-time crop predictions instantly. The novelty of this study lies in integrating both Decision Tree and Random Forest algorithms into a single interactive web platform, providing not only accurate predictions but also easy accessibility for users. Experimental results indicate that the Random Forest algorithm achieves higher accuracy than the DeciSsion Tree in crop classification. Therefore, this system can serve as an effective tool for farmers and researchers to identify suitable crops under specific environmental conditions..
The Future of Work: Digital Leadership and Employee Wellbeing in Hybrid Organizations
This study examines the role of digital leadership in enhancing employee well-being within organizations that adopt hybrid work models, which have become increasingly prevalent due to the acceleration of digital transformation. Technology-based work environments require leaders to effectively manage virtual communication, digital workloads, and working conditions that influence employees’ psychological well-being. This study aims to analyze the effect of digital leadership on employee well-being while also evaluating the mediating role of digital working conditions. A quantitative method was employed, with data collected through an online survey of employees from various industrial sectors implementing hybrid work arrangements. The data were analyzed using a structural equation modeling approach. The results indicate that digital leadership has a direct positive effect on employee well-being and an indirect effect through the reduction of digital stressors such as information overload and task fragmentation. These findings highlight the importance of human-centered, adaptive leadership behaviors that are capable of balancing technological demands with employees’ psychological needs. The practical implications of this study underscore the need to develop digital leadership capacities to create a healthy and sustainable hybrid work ecosystem.
The Role of Blockchain in Securing Internet of Things (IoT) Ecosystems: A Cross Industry Analysis
The rapid growth of the Internet of Things (IoT) has increased system connectivity across industries while simultaneously intensifying cybersecurity risks related to data integrity, access control, and system resilience. This study aims to analyze the role of blockchain technology in securing IoT ecosystems through a cross-industry perspective. A quantitative comparative approach was employed using a structured survey distributed to organizations in the manufacturing, healthcare, energy, logistics, and smart infrastructure sectors. Data were analyzed using descriptive and inferential statistical techniques to examine the effect of blockchain-based security mechanisms on IoT security performance. The findings indicate that blockchain adoption has a positive and significant impact on improving data integrity, strengthening access control, and enhancing system resilience across all observed industries. Variations in the strength of these effects reflect differences in regulatory pressure and operational risk among sectors. The results confirm that blockchain functions not only as a technical security solution but also as a strategic infrastructure for decentralized trust and risk governance in IoT environments. This study provides both theoretical contributions to IoT security frameworks and practical guidance for cross-sector blockchain implementation.
Media Edukasi Ilmuwan Muslimah Berbasis Game FPS 3D
The advancement of digital technology has encouraged the development of interactive learning media that enhance student engagement and learning experiences. However, the representation of Muslim women scientists in digital educational media remains limited. This study developed a 3D First-Person Shooter (FPS) educational game presenting the profiles and contributions of prominent Muslim women scientists in the history of Islamic science. The research employed a Research and Development (R&D) method using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation. The game was tested on 20 high school and university students in East Java selected through purposive sampling. Evaluation included alpha testing for technical performance, beta testing for user experience, and validation by media and content experts. The results show that the game achieved a very positive evaluation with an average user score of 4.69, with most responses rated 5 across indicators. Expert validation also placed all assessment items in the “Very Appropriate (SS)” category, confirming the feasibility of the game in terms of design, interactivity, and content accuracy. Overall, the game enhances learning motivation, strengthens students’ understanding of Muslim women scientists, and meets the criteria for an interactive and representative digital learning medium suitable for the digital-native generation.
Digitalisasi UMKM dalam Pemasaran Peternakan Sapi di Tanjung Aur OKI
This study aims to implement digitalization in the marketing process of Micro, Small, and Medium Enterprises (MSMEs) engaged in cattle farming in Tanjung Aur Village, Ogan Komering Ilir (OKI) Regency, through the development of a web-based cattle sales system. Digitalization is applied to overcome the limitations of traditional marketing, which requires buyers to visit the farm directly, resulting in limited market reach and inefficient transaction processes. The developed system provides features such as product information, online ordering, payment proof uploads, and data management for both administrators and farmers. The research adopts the Waterfall development model, consisting of requirement analysis, system design, implementation, and testing using the black box method. The results indicate that the implemented website successfully improves marketing effectiveness, expands customer reach, and simplifies the transaction process for both buyers and sellers. Furthermore, the system enhances time and cost efficiency compared to conventional manual methods previously used. With this digital platform, cattle-farming MSMEs in Tanjung Aur are expected to become more competitive and better prepared to adapt to the demands of the digital economy era.
Pengembangan Sistem Informasi Manajemen Pelanggan Android untuk Meningkatkan Layanan Air Bersih PDAM Sungai Lilin
Customer service at PDAM Tirta Randik in Sungai Lilin District still faces several issues, such as delays in delivering billing information, manual data recording processes, and frequent input errors in customer data. Previous studies on PDAM information systems generally focused only on data recording or water flow monitoring, but have not provided an integrated solution for mobile-based customer management. This study presents a novelty in the form of an Android-based Customer Management Information System integrated with a server database, designed to provide real-time information access, automated billing generation, and monitoring of water usage history. The research employs a Research and Development (R&D) approach using the Software Development Life Cycle (SDLC) Waterfall model. The system was evaluated using: (1) black-box testing to validate system functionality; and (2) usability assessment using the System Usability Scale (SUS) to determine user acceptability. The results of black-box testing indicated that all features operated according to specifications. The SUS evaluation involving 20 respondents produced an average score of 82.5, categorized as “Excellent” with an acceptance level of Grade A. This information system can improve the accuracy of customer data recording, accelerate access to billing information, and enhance user satisfaction. It can serve as a reference model for the digitalization of PDAM services and has the potential for further development through the integration of digital payment features.
Analisis Sentimen Ulasan untuk Menentukan Kualitas Produk Terbaik E-Commerce (Tokopedia) Menggunakan Algoritma Naive Bayes
The development of e-commerce in Indonesia, particularly Tokopedia, has led to an increase in the number of reviews given by consumers after making a purchase. These reviews consist of positive and negative opinions that can be used to assess product quality. However, the high volume of reviews makes manual analysis ineffective. This study aims to classify product review sentiment on Tokopedia using the Naïve Bayes algorithm to determine the best product quality from several stores selling similar items. The Naïve Bayes algorithm was chosen because of its good classification capabilities and efficiency in text data processing. The results show that Naïve Bayes is capable of classifying reviews as positive or negative with an average accuracy of 95% on the test data. Testing per store also yielded consistent results with an accuracy of around 90%. Furthermore, the sentiment results were converted into quality metrics with the categories Good, Fair, and Poor. Products at Store A scored 85% and Store B scored 80%, so both were categorized as Good, with Store A being more recommended. This study shows that Naïve Bayes-based sentiment analysis is effective for assessing product quality and provides insights for sellers to improve their services.
Pengembangan E-Commerce Berbasis Website (Studi Kasus: pada Warung Seblak Mbak Sari Desa Karangasem)
The advancement of digital technology has driven significant transformations in the business sector, including the culinary industry, through the adoption of E-commerce. Warung Seblak Mbak Sari previously faced several challenges, such as manual transaction recording, limited market reach, and unintegrated digital promotion. This study aims to develop an E-commerce website as a solution to improve service efficiency and expand marketing reach. The method applied is Rapid Application Development (RAD), which consists of the Requirement Planning, User Design, Construction, and Cutover phases. The system was designed using Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD), and implemented using PHP and MySQL. The development results show that the website can display menus, process online orders, perform automatic checkout, manage products, and generate sales reports. Blackbox Testing proves that all features function as required, enabling the system to reduce recording errors, accelerate transaction processes, and increase marketing reach. Therefore, the implementation of this web-based E-commerce can help culinary MSMEs manage their businesses more effectively and competitively in the digital era.
Sistem Pendukung Keputusan Pemilihan Kampus Menggunakan Metode Multi Factor Evaluation Process
Choosing a campus is a crucial decision for prospective students because it influences their learning and self-development process. With 13 campuses available in Bengkalis and Pekanbaru, prospective students often face difficulties in determining the right choice for their preferences and needs. Therefore, a recommendation system is needed that can help make decisions quickly and accurately without complicated searches. This study aims to design a campus selection recommendation system using the Multi Factor Evaluation Process (MFEP) method, which considers six main factors: tuition fees, majors, scholarships, type of college, admission pathways, and accreditation. This method involves weighting and evaluation to produce more accurate recommendations. The system development uses the Rapid Application Development (RAD) method, enabling a fast and iterative process by involving users at every stage. The result of this system is a recommendation system that can help prospective students determine the best campus according to the selected criteria, thus facilitating the decision-making process efficiently. The result of this system is a recommendation system that can help prospective students determine the best campus according to the selected criteria, thus facilitating the decision-making process efficiently.
Rancang Bangun E-Helpdesk Kampus Menggunakan Metode Agile
Politeknik Negeri Bengkalis, which serves thousands of students, frequently receives complaints regarding facilities and infrastructure, often managed manually through the Student Affairs Department due to the lack of a dedicated campus complaint feature. This traditional method results in unrecorded complaints and slow processing. To address this, an effective and transparent system is necessary to improve service quality and student satisfaction. The purpose of this study is to design and build an E-Helpdesk Campus application for mobile platforms using the Agile development method. The system was developed through two main iterations, focusing on features like complaint submission with categorization, status monitoring, and user management for campus staff/admin. The application was built using the Flutter framework. Based on the results of Black Box Testing, the E-Helpdesk Campus system was successfully implemented and operates effectively, replacing the manual process. The system successfully records complaints in a structured manner, allows students to monitor progress, and is proven to be user-friendly, thereby achieving the research objectives of creating an effective and transparent student complaint system.
Sistem Pendukung Keputusan Pemilihan Santri Terbaik Menggunakan Metode Vikor Berbasis Web Pada Pesantren Darul Ihsan
Because Islamic boarding schools use a manual assessment system that tends to be subjective, it is often difficult to select the best students. The VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method was used in this study to design and implement a web-based Decision Support System (DSS). The purpose of this study was to find the best students at Darul Ihsan Islamic Boarding School quickly, fairly, and effectively. Examination scores, attendance, memorization of the Qur'an, participation in extracurricular activities, and non-academic achievements are all criteria used to assess a person. Needs analysis, system design, implementation, and system testing using Blackbox are all steps in Waterfall software development. PHP and MySQL databases were used to create this system. The results of the study show that the DSS built has the ability to produce fair and unbiased student rankings. The VIKOR method has been proven effective in producing fair compromise solutions that balance academic and non-academic standards. It is hoped that this system will help Islamic boarding schools make better and more honest decisions.
Analisis Kesadaran Mahasiswa dalam Menjaga Keamanan Data Pribadi pada Penggunaan Media
This study examines the level of personal data security awareness among college students, an age group that is vulnerable to digital risks due to the intensity of social media use. The main objective of the research is to measure the extent to which students understand, behave, and behave in protecting their personal data in an ecosystem of digital platforms such as Instagram, Twitter, and TikTok. The research method used a descriptive quantitative approach through the distribution of questionnaires to 33 students. The results of the study show that students have a sufficient level of knowledge about the types of personal data and potential threats that may arise. However, it was found that there was a significant gap between knowledge and actual behavior, where most respondents were still negligent in activating security features and managing the privacy of social media accounts. These findings confirm that increasing awareness is not enough just through providing information, but must be followed by internalizing consistent data security attitudes and practices. Thus, this research makes an important contribution in understanding the pattern of student awareness of personal data security, as well as being the basis for the development of more effective digital education strategies to encourage safe behavior in cyberspace.
Analisis Keamanan Arsitektur Sistem Smart Farming Berbasis NIST Cybersecurity Framework di Living Lab Universitas Widyatama
The development of Internet of Things (IoT) technology has driven digital transformation in the agricultural sector through the concept of Smart Farming, including in the academic environment. However, increased system connectivity and automation are also accompanied by increased cybersecurity risks, especially in IoT systems that are developed independently and are closed (proprietary). This study aims to analyze the cybersecurity posture of the IoT-based Smart Farming system architecture in the Living Lab of Widyatama University using the National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) version 2.0. The study uses a qualitative method with a case study approach, where the analysis focuses on the six core functions of the NIST CSF, namely Govern, Identify, Protect, Detect, Respond, and Recover. The object of the study is an off-grid Smart Farming system that utilizes Solar Power Plants (PLTS) for real-time monitoring of catfish cultivation water quality. The results of the study indicate that the level of system security maturity is still at Tier 1 (Partial), with major weaknesses in the aspects of security governance, early detection mechanisms, and incident response and recovery procedures. While the system offers advantages in energy independence and operational continuity, the absence of formal security policies and adequate technical controls potentially increases the risk of operational disruptions due to cyber threats. This study recommends implementing NIST CSF v2.0-based security to improve data integrity, operational reliability, and resilience of IoT-based Smart Farming systems in academic environments.
Aplikasi Pembelajaran Bahasa Inggris pada TK Terpadu Padre Annibale School Maumere Berbasis Mobile
This study aims to develop a mobile-based English learning application that can support the teaching process for both teachers and students in Kindergarten (TK). The subjects of this study are the students of Padre Annibale School for the 2025-2026 academic year. The background of this research is the limited use of learning media that cannot actively involve students. Additionally, many children in the millennial era are more attracted to electronic media, especially mobile phones, which presents a challenge in the learning process. The tendency for children to spend more time on mobile phones rather than reading books or listening to the teacher’s instruction can be seen as an opportunity to introduce technology-based learning media. With the presence of learning applications on mobile phones, students can access educational information anytime, both at school and at home. This application is designed to provide ease in obtaining English learning materials that meet the needs of students, as well as assist teachers in enriching their teaching methods. Through this approach, it is hoped that the teaching and learning process can be more interactive and engaging for students, while also supporting the integration of technology in education.
Perancangan Model Support Vector Machines Berbasis Geospasial Sebagai System Peringatan Dini Penyebaran DBD Di Kabupaten Sikka
Dengue hemorrhagic fever (DHF) is a disease that is commonly found in most tropical and subtropical regions, especially Southeast Asia, including Indonesia, one of which is Sikka Regency, NTT Province. In 2024, there were 821 cases of DHF recorded and at the beginning of the year until February 2025 there were 50 cases of DHF. To overcome the spread of this disease, an early warning system is needed that is able to identify its distribution patterns quickly and accurately. The purpose of this study is to build a mobile application as an early warning system with a Support Vector Machines (SVM) algorithm model based on geospatial data to inform the public and authorities about the potential for a DHF outbreak in Sikka Regency, so that preventive measures can be taken immediately. The SVM model was chosen because of its ability to classify data and is known as one of the classification methods that has high results in predicting potential classifications in data. The geospatial data used includes rainfall, temperature, and humidity data, population density, and the history of DHF cases in Sikka Regency. The results show that the SVM-based approach can improve prediction accuracy compared to conventional methods and provide an innovative solution for dengue fever mitigation through the use of geospatial-based technology.