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.