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 :
Analisis Penerapan Itil V3 pada Layanan Helpdesk dalam Manajemen Layanan Ti: Studi Literatur
The rapid development of information technology has encouraged organizations to provide efficient and high-quality IT services to support operational activities and user needs. One of the main services in IT service management is the helpdesk, which functions to handle incidents, service requests, and provide technical support to users in an efficient manner. However, in practice, helpdesk services often face various challenges such as slow response times to incidents, lack of systematic documentation, and the absence of clear standard operating procedures in managing IT services. These issues can lead to a decline in service quality and user satisfaction. Therefore, a framework is needed to help improve the quality of IT services, one of which is the Information Technology Infrastructure Library (ITIL) V3. This study aims to analyze the implementation of ITIL V3 in helpdesk services within IT service management. The method used in this research is a literature review by examining various relevant journals and previous studies. The results show that the implementation of ITIL V3, particularly in the Service Operation domain. such as incident management and helpdesk, can improve service efficiency, accelerate incident handling time, and enhance overall IT service quality. In addition, ITIL implementation also contributes to process standardization and increased user satisfaction. Thus, ITIL V3 can be an appropriate solution to improve helpdesk service performance and support more structured and sustainable IT service management.
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Evaluasi Manajemen Layanan TI Organisasi Publik dan Swasta Menggunakan Kerangka Kerja ITIL
Information Technology (IT) service management is a fundamental pillar for supporting organizational operations in the digital era, across both public and private sectors. However, high dependence on IT is often not balanced with measurable governance, risking decreased service quality for users. This study aims to evaluate the maturity level of IT service management using the Information Technology Infrastructure Library (ITIL) framework as the primary audit instrument. The research method employed is a descriptive literature review and comparative analysis of 15 organizational case studies in Indonesia, encompassing government agencies, the hospitality sector, healthcare services, and commercial digital platforms. The results indicate significant variations in maturity levels; while commercial application-based services like GoFood have reached level 4 (Managed), the majority of educational organizations and public services remain at level 3 (Defined) and level 2 (Repeatable). Key findings identify critical obstacles such as unsystematic incident management, limited network infrastructure, and a lack of HR competence in implementing formal Standard Operating Procedures (SOP). This study's contribution provides a map of current IT service maturity conditions and strategic recommendations for IT managers to standardize operational procedures and enhance staff capacity to ensure sustainable, high-quality, and measurable service in the future.
Analisis Perbandingan Algoritma TF-IDF dan Word2Vec dalam Rekomendasi Film Berbasis Konten Letterboxd
The exponential growth of film streaming platforms has created significant challenges for users in discovering content that aligns with their preferences. Recommendation systems have proven to be a strategic solution. This study aims to compare the performance of Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec algorithms in the context of content-based filtering recommendation systems, utilizing data from the Letterboxd platform. The research was conducted through a Systematic Literature Review (SLR) of 15 prior studies, with a selection process comprising the definition of inclusion and exclusion criteria, literature searches across academic databases, and comparative synthesis of findings. The review findings indicate that TF-IDF excels in weighting explicit terms within structured metadata such as genre, director, and cast, and can be implemented without a complex training process. Word2Vec, on the other hand, demonstrates superiority in capturing semantic relationships between words through dense vector representations, yet requires a large corpus and careful hyperparameter tuning. Based on the literature synthesis, TF-IDF combined with Cosine Similarity is deemed more optimal for a content-based film recommendation system using Letterboxd metadata, given the structured and explicit nature of the data. Recall@5 of 73% and Recall@10 of 80% reported in prior studies further support this conclusion
Efektivitas Penanganan Gangguan Sistem melalui Incident Management Practice ITIL 4
Information Technology (IT) service management is a fundamental pillar for supporting organizational operations in the digital era, across both public and private sectors. However, high dependence on IT is often not balanced with measurable governance, risking decreased service quality for users. This study aims to evaluate the maturity level of IT service management using the Information Technology Infrastructure Library (ITIL) framework as the primary audit instrument. The research method employed is a descriptive literature review and comparative analysis of 15 organizational case studies in Indonesia, encompassing government agencies, the hospitality sector, healthcare services, and commercial platforms. The results indicate significant variations in maturity levels; while a few educational organizations have reached level 4 (Managed), the majority of public and private services remain at level 3 (Defined) and level 2 (Repeatable). Key findings identify critical obstacles such as unsystematic incident management, manual helpdesk systems, high dependence on centralized servers in public services, and a lack of HR competence in implementing formal Standard Operating Procedures (SOP). This study's contribution provides a map of current IT service maturity conditions and strategic recommendations for IT managers to standardize operational procedures and enhance staff capacity to ensure sustainable, high-quality, and measurable service in the future.
Rancang Bangun Game Interaktif Berbasis Android Sebagai Sarana Media Pembelajaran pada Mata Pelajaran Matematika Materi Bangun Ruang di Madrasah Tsanawiyah Laboratorium Jambi
This space building learning application is designed using Construct 2, a game engine that allows the development of 2-dimensional games with an easy-to-use interface. This research was conducted at Madrasah Tsanawiyah Laboratorium Jambi because currently learning is done with explanations from teachers that are still monotonous and cause boredom in students. The type of research used is Research and Development (RnD) with the Multimedia Development Live Cycle (MDLC) method with Uniefied Modeling Language (UML) modelling consisting of Usecase diagrams, Activity diagrams and Sequence diagrams The material used in this study is data obtained from the results of interviews so that it can be known what is needed to design learning applications The results of Blackbox Testing software system testing with the results declared successful, and feasibility testing using a Likert scale resulted in a score of 90% (very feasible). This research is expected to increase learning motivation, and develop the spatial thinking skills of Jambi Laboratory Madrasah Tsanawiyah students in a fun and effective way.
Evaluasi Manajemen Insiden Layanan TI pada Aplikasi E-Wallet Menggunakan Cobit 5 DSS02
The rapid development of financial technology has driven the increased use of digital wallets, such as OVO, in daily transactions. This growing dependence on digital services requires stable and well-structured information technology service management, particularly in incident handling. Disruptions such as transaction failures, notification delays, incorrect balance deductions, and system errors can significantly affect user satisfaction and trust. This study aims to evaluate the information technology service incident management of the OVO application using the COBIT 5 framework, specifically the DSS02 domain (Manage Service Requests and Incidents). This research employed a qualitative descriptive approach through literature review and direct observation of the application. The evaluation results indicate that most DSS02 practices have been formally implemented and documented, including incident recording, classification, investigation, and resolution. Based on the COBIT 5 capability level mapping, the IT service incident management of the OVO application is at Level 3 (Defined Process). This level shows that the processes have been standardized and consistently executed; however, they have not been fully measured and quantitatively controlled. Therefore, improvements are needed in performance monitoring, Service Level Agreement (SLA) transparency, and continuous evaluation to achieve a higher maturity level and enhance service quality.
Sistem Pendukung Keputusan Penentuan Tenaga Kesehatan Teladan Menggunakan Metode Moora Berbasis Web
This study aims to design and develop a web-based Decision Support System (DSS) using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to determine the exemplary health worker at Puskesmas Kosambi. The current problem is that the assessment process for 35 employees is still done manually, making it prone to subjectivity bias and taking a long time in data recapitulation. This study uses five main criteria, namely Work Discipline, Responsibility and Performance, Service Ethics and Behavior, Employee Performance Target (SKP) Score, and Length of Service. Candidate data is processed through the MOORA method stages, starting from the formation of a decision matrix, matrix normalization, to optimizing the value of benefit and cost attributes to obtain the highest preference value. The implementation results show that the candidate named dr. Anis Julianti obtained the highest optimization value of 0.14363. Software testing using Black Box and White Box proves the system runs well without errors, and user response testing obtained a feasibility rate of 82.74% which is included in the Very Strong category. This system is expected to help the management of Puskesmas Kosambi in producing recommendation decisions that are more accurate, fair, objective, and transparent.
Literature Review: Metode Machine Learning dan Deep Learning dalam Prediksi Indeks Harga Saham Gabungan (IHSG)
The Jakarta Composite Index (JCI) is a key indicator reflecting the condition of the Indonesian capital market and plays a crucial role in investment decision-making. The JCI's fluctuating, non-linear movement, influenced by various economic factors, makes prediction a challenging process. This study aims to analyze and predict JCI movements using machine learning and deep learning approaches. The methods studied include conventional time-series models and neural network-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), as well as hybrid approaches that have been widely used in previous research. This study was conducted using a systematic literature review approach in accredited national journals and reputable international journals that discuss JCI prediction. The results indicate that deep learning methods generally provide better prediction performance than traditional statistical methods, particularly in capturing long-term patterns and market volatility. This research is expected to serve as a reference basis in developing more accurate and reliable JCI prediction models.
Analisis Perbandingan Arsitektur Deep Learning Pada Klasifikasi Penyakit Tanaman Berbasis Citra Daun: Systematic Literature Review
Plant disease classification based on leaf images has become an important approach to supporting precision agriculture and early plant disease detection systems. This study aims to examine the development of Deep Learning methods for plant disease classification through a Systematic Literature Review (SLR) approach. The review analyzed 15 scientific articles covering various crops, including rice, maize, tomato, tea, cassava, citrus, tobacco, and coffee. The literature selection process followed the PRISMA framework. The analysis focused on comparing Deep Learning architectures, dataset characteristics, and model performance based on accuracy metrics. The findings indicate that conventional Convolutional Neural Networks (CNNs), modern architectures such as ResNet, Inception, EfficientNet, and DenseNet, as well as lightweight models such as MobileNetV2, are the most widely adopted methods. Model performance is influenced by dataset size and source, with large-scale public datasets generally achieving higher and more consistent accuracy. This study highlights that selecting an appropriate Deep Learning architecture requires balancing model complexity, data availability, computational efficiency, and practical deployment requirements to develop effective and reliable plant disease classification systems.
Pengaruh AI-Powered Chatbots terhadap Writing Proficiency: Studi pada Pembelajaran English as a Foreign Language (EFL)
This study aims to examine the effect of AI-powered chatbots on writing proficiency and to analyze the mediating role of writing motivation in English as a Foreign Language (EFL) learning. This research employed an explanatory quantitative approach using a survey design involving 100 EFL students selected through purposive sampling. Data were collected using a Likert-scale questionnaire and analyzed through Structural Equation Modeling–Partial Least Squares (SEM-PLS). The findings show that AI-powered chatbots have a positive and significant effect on writing motivation and writing proficiency. Writing motivation also positively affects writing proficiency and partially mediates the relationship between AI-powered chatbots and writing proficiency. These results indicate that AI chatbots support EFL writing by providing immediate feedback, helping students develop ideas, improve grammar, enrich vocabulary, organize paragraphs, and increase confidence in revising their writing. The implication of this study is that EFL teachers and institutions should integrate AI-powered chatbots ethically and pedagogically to enhance students’ writing motivation and writing quality.
Implementasi Metode AHP dan SAW pada Sistem Informasi Data Penduduk Desa Sebente untuk Penentuan Prioritas Bantuan Sosial
Social assistence distribution is one of the government’s efforts to improve the welfare of communities inneed. The process of determining social assistance recipients must be carried out accurately so that the aid can be distributed to individuals who are truly eligible. Haowever, in Sebente Village, the process of selecting soscial assistance recipients is still conducted manually, which requires considerable time and has the potential to create subjetive in deciision-making. The study aims to develop a Decision Support System (DSS) that can assist the village government in determining the priority of social assistance recipients objectively, transparently, and accurately. The methods used in this study are the Analytical Hierarchy Proses (AHP) and dan Simple Additive Weighting (SAW. The AHP method is employed to determine the weights of criteria based on their level of importance, while the SAW method is used to rank prospetctive social assistance recipients. The criteria applied in this study include income, number of dependents, housing, conditions, occupations, and assets. The system was developed as a web-based applications using PHP as the programming language and MySQL as teh database management system. The result of the study indicate that the AHP method producet the higets criterion weight for income, with a value of 0,49, and generated a Consistency Rasio (CR) of 0,064, demonstrating that the pairwise comparison matrix was consisten. Furthermore, the SAW method successfully ranked prospective soscial assistance recipients based on the highest preverence values. The developed system can assist Sebente Village officials in accelerating the selection process of social assistance recipients, improving the objectivity of assessments, and producing more effective and accurate decisions.
Analisis Pola Perjalanan Penumpang LRT di Palembang Menggunakan Metode K-Means Clustering Berdasarkan Data Gate dan Waktu Perjalanan
This study analyzes travel patterns of Single Trip ticket users of the Light Rail Transit (LRT) in Palembang City using the K-Means Clustering method within the Knowledge Discovery in Databases (KDD) framework. The dataset comprises 9,377 Single Trip ticket transaction records from the BPKARSS ticketing system on August 6, 2025. Modeled attributes include origin station code (gate-in), destination station code (gate-out), departure time in decimal format, and estimated travel duration derived through feature engineering. The Elbow Method identified K=3 as the selected cluster count based on the inflection point of the WCSS curve, further supported by a Silhouette Score of 0.3044 (moderate category), indicating discernible but partially overlapping clusters. K-Means with K-Means++ initialization produced three passenger segments: Cluster 0: Short-Distance Travel Pattern (39.8%; n=3,735), Cluster 1: Medium-Distance Travel Pattern (29.0%; n=2,724), and Cluster 2: Long-Distance Travel Pattern (31.1%; n=2,918). The results indicate that all cluster centroids fall within the midday period (12:45–13:45 WIB), which differs from conventional peak-hour assumptions. These findings offer an empirical basis for consideration in operational policy formulation.
Pengembangan Sistem Pendukung Keputusan Kontinu (Continuous Decision Support System) Berbasis Otomatisasi Laporan Konsolidasi menggunakan Google Apps Script dan Telegram API
The manual process of consolidated reporting often results in delays in information delivery, data inconsistencies, and low effectiveness in distributing reports to decision-makers. This study aims to develop a Continuous Decision Support System based on automated consolidated reporting using Google Apps Script and the Telegram API, and to analyze it from an Enterprise Architecture perspective. The research employs a Design Science Research approach through the stages of problem identification, architectural design, system development, testing, and evaluation. The system is designed by utilizing Google Sheets as the data source, Google Apps Script as the automation engine, and the Telegram API as the information distribution channel. The results show that the proposed system is capable of integrating data reading, validation, report consolidation, summary generation, and automated notification delivery processes. From an Enterprise Architecture perspective, the system forms a lightweight integration architecture that connects the business, data, application, and technology layers. This study concludes that the integration of Google Apps Script and the Telegram API can support faster, more structured, and continuous information delivery for decision-makers.
Program Aplikasi Website Pemilihan Sepeda Motor Listrik Berdasarkan Metode TOPSIS di Wilayah Jakarta Barat
The rapid growth of Indonesia’s electric motorcycle market, particularly in West Jakarta, has made consumer decision-making more complex due to the many available alternatives and evaluation criteria. This study aims to develop a web-based decision support system (DSS) for selecting electric motorcycles using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Data were obtained through questionnaires distributed to 68 respondents with experience using electric motorcycles in West Jakarta, selected through purposive sampling based on the Cochran formula at a 90% confidence level. Six criteria were applied, namely purchase price, travel range, battery capacity, maximum speed, charging time, and after-sales service, all treated as benefit criteria according to the Likert-scale perception instrument. The system was developed using Next js, JavaScript, and a MySQL/MariaDB database. TOPSIS analysis ranked Polytron Fox 350 first with a preference value of 0.7938, followed by ALVA N3 and Honda CUV e:. Validation through unit, black-box, and end-to-end testing confirmed that the system outputs matched manual calculations.
Pengembangan Sistem Informasi Manajemen Magang Berbasis Web di Dinas Komunikasi, Informatika, Statistik dan Persandian Kabupaten Tuban
Diskominfo SP Tuban requires an effective digital system to manage internship administration, participant monitoring, and digital learning processes. The previous web-based internship management system, developed using the Laravel framework with Role-Based Access Control (RBAC) and Hybrid Authentication, had several limitations: a uniform dashboard for all user roles, poor mobile responsiveness, a virtual user mechanism that prevented permanent account records, redundant database structures, the absence of an integrated e-learning feature, and profile photo synchronization issues. This study aims to develop and improve the system by: (1) redesigning the authentication flow from virtual users to permanent accounts, (2) developing role-specific dashboards for eight user types, (3) restructuring the database with seven new tables, (4) building an e-learning module supporting text, file, and YouTube video materials with a prerequisite mechanism, (5) improving responsive design for mobile devices, (6) resolving profile photo issues with gallery/camera/Google source options, and (7) adding an internship application module with supervisor search feature. Black-box testing on 15 test scenarios confirmed that all main system functions operate correctly without functional errors.
Analisis Perbandingan Python dan C++ dalam Menyelesaikan Sistem Persamaan Linear menggunakan Metode Gauss Seidel
A linear equation system is one of the fundamental problems in computational mathematics that is widely applied in various fields of science and engineering. Numerical solution of linear equation systems using iterative methods, particularly the Gauss-Seidel method, provides high efficiency for large-dimensional systems. This study aims to analyze and compare the performance of Python and C++ programming languages in implementing the Gauss-Seidel method to solve linear equation systems of order 3×3, 4×4, and 5×5. The research is of a quantitative comparative nature with comparison variables including computation time, number of iterations, memory usage, and accuracy level. The results show that C++ has a significant advantage in computation time, with an average speed 32.9 times faster than Python. For a 3×3 system, Python requires 2.341 ms while C++ only needs 0.089 ms. For a 5×5 system, Python requires 9.156 ms while C++ requires 0.251 ms. For the number of iterations and numerical accuracy, both languages produce equivalent values because they use the same algorithm with an error tolerance of 10⁻⁶. Python’s memory usage is on average 9.5 times greater than C++. The study concludes that C++ is superior in computational efficiency, while Python offers greater ease of development and programming productivity. The choice of programming language should be tailored to the application’s requirements, whether prioritizing execution speed or ease of implementation.
Perbandingan Efektivitas Metode Interpolasi Linier dan Kuadratik dalam Pengolahan Data Performa CPU
This study aims to compare the effectiveness of two numerical interpolation methods, namely linear interpolation and quadratic interpolation, in processing and estimating CPU (Central Processing Unit) performance data. The data used includes four main parameters: CPU usage (%), CPU temperature (°C), execution time (ms), and data processing speed (GFLOPS). Data collection was conducted through a gradual workload simulation over a 90-second interval with 10 measured data points in the range 0–90 seconds. The analysis method includes interpolation calculations using the Lagrange formula, accuracy evaluation with MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) metrics, as well as visual comparison through graphs. The results show that quadratic interpolation produces an MAE value of 0.1222 and RMSE of 0.1374, while linear interpolation produces an MAE of 0.3389 and RMSE of 0.3648. The quadratic method proved significantly more accurate with an MAE difference of 0.2167 (63.9% better). These findings indicate that the quadratic interpolation method is more suitable for CPU performance data that is nonlinear and involves non-constant velocity changes. The implications of this research provide practical recommendations in selecting interpolation methods for real-time monitoring and optimization of computing systems.
Analisis Kelayakan Penerima BLT (Bantuan Langsung Tunai) Menggunakan Metode Logistic Regression
The distribution of Direct Cash Assistance (BLT) in Pedekok Village, Pegasing District, Central Aceh Regency, is still carried out manually through village deliberations, so that it often causes subjectivity and inaccuracy of targets. This study aims to objectively analyze the feasibility of BLT recipients using the Python-based Logistic Regression method. The type of research used is applied quantitative research with a total of 166 Family Cards data covering economic and social variables such as income, number of dependents, elderly status, people with disabilities, and chronic diseases. The research process includes the pre-processing stages of data, variable coding, model formation, and performance evaluation using accuracy, precision, recall, and confusion matrix metrics. The results of the analysis showed that the Logistic Regression method was able to provide an accuracy level of 97% in the classification of BLT recipients. Income factors, the existence of members with disabilities, and chronic diseases are the variables that have the most influence on the probability of eligibility for receiving assistance. The implementation of this method is expected to increase transparency, objectivity, and accountability in the process of determining BLT recipients at the village level.
Analisis Perbandingan Hasil Integrasi Numerik antara Metode Manual dan Penggunaan Model AI dalam Kaidah Trapesium
The rapid development of artificial intelligence has encouraged the use of large language models as supporting tools for mathematical problem solving, including numerical integration. However, the accuracy and consistency of AI-generated numerical answers still need to be evaluated carefully because each model may produce different outputs under the same prompt. This study aims to compare the accuracy of ChatGPT, Claude, and Gemini in solving numerical integration problems using the composite trapezoidal rule. The research applies a quantitative comparative design involving three types of functions, namely polynomial, exponential, and trigonometric functions. Each model was tested five times for each function using the same prompt, interval, and number of subintervals, with n = 12. Manual calculation using the trapezoidal rule was used as the reference value, and the difference between AI output and manual calculation was measured using percentage error. The manual calculation procedure was written step by step, starting from determining the step size, generating the partition points, evaluating the function values, summing the interior ordinates, and substituting the values into the trapezoidal formula. The results show that ChatGPT achieved the lowest overall average error, followed by Claude and Gemini. The findings indicate that AI models can support numerical computation, but their outputs should be verified through manual procedures or computational tools before being used as final answers.
Analisis Kepuasan Pengguna SIAKAD Menggunakan Metode End User Computing Satisfaction (EUCS) dan Importance Performance Analysis (IPA)
The Academic Information System (SIAKAD) is a crucial digital infrastructure in managing higher education academic services, but its effectiveness is often hampered by low user satisfaction due to a system design that is less responsive to real needs. This study aims to analyze the level of SIAKAD user satisfaction at the Faculty of Technology, Widuri Institute of Technology (ISTEK Widuri) and identify system improvement priorities based on the perspective of students as the primary users. The study uses a quantitative approach with the End User Computing Satisfaction (EUCS) method to measure satisfaction in the dimensions of content, accuracy, format, ease of use, and timeliness, and Importance Performance Analysis (IPA) to map the gap between the level of importance and actual system performance. Data were collected through a questionnaire involving 35 student respondents and analyzed using SPSS. The results show that the accuracy and timeliness dimensions received the highest ratings, while the content and ease of use dimensions still require significant improvement. The IPA analysis identified several attributes in Quadrant II as top priorities for improvement because they have a high level of importance but low performance. This study concludes that the integration of EUCS and IPA is effective as an evaluation framework for academic information systems and can be a strategic basis for institutions in developing SIAKAD that is more responsive, accurate, and user-oriented.
Pengaruh Tingkat Ketergantungan Penggunaan ChatGPT terhadap Pemahaman Pembelajaran Siswa SDN 2 Sindangpanon Purwakarta
Advances in Artificial Intelligence (AI), particularly ChatGPT, have been utilized as a learning aid because it can provide information quickly and help students complete their assignments. However, excessive use can lead to dependency, which has the potential to affect students’ learning comprehension. This study aims to determine the effect of the level of dependence on ChatGPT use on students’ learning comprehension at SDN 2 Sindangpanon Purwakarta. The study employed a quantitative approach using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) method with the assistance of SmartPLS. The study sample consisted of 61 students selected using a saturation sampling technique. Data were collected via a Likert-scale questionnaire comprising 12 statements. The results indicate that the level of dependence on ChatGPT has a negative and significant effect on students’ learning comprehension, with a path coefficient of -0.375, T -statistic of 2.571, a P-value of 0.010, and an R-squared of 0.140. Thus, the level of dependence on ChatGPT use explains 14% of the variation in students’ learning comprehension, while 86% is influenced by other factors outside the scope of this study that were not analyzed in depth.
Metode Systematic Literature Review Arsitektur Convolutional Neural Network untuk Klasifikasi Sampah Berbasis Citra
Waste management is becoming increasingly complex as the volume and variety of waste types increase, making manual sorting processes inefficient and error-prone. In recent years, image-based waste classification using Deep Learning (DL), particularly Convolutional Neural Networks (CNN), has been widely developed as an automated waste sorting solution. This study aims to conduct a Systematic Literature Review (SLR) to analyze the performance, trends, and utilization of CNN architectures in image-based waste classification. The SLR method is carried out systematically through the stages of planning, literature search, study selection, data extraction, and result synthesis. A total of 15 scientific articles published between 2020 and 2025 were analyzed with a focus on CNN architecture, preprocessing techniques, dataset characteristics, number of classes, and evaluation metrics such as accuracy, precision, recall, and F1-score. The results of the study show that CNNs are capable of providing high classification performance in various scenarios. Standard CNNs are effective on small datasets with a limited number of classes, while transfer learning approaches such as MobileNetV2, ResNet50, VGG16, and EfficientNet show more stable performance in multi-class classification. Overall, CNN proved to be an effective and adaptive approach to support computer vision-based automated waste sorting systems.
Rancang Bangun Business Intelligence Dashboard untuk Hasil Panen Tanaman Pangan Menggunakan Tableau pada Dinas Pertanian TPH Provinsi Sumatera Selatan
This study aims to design and build a Business Intelligence Dashboard for food crop yield data using Tableau at the Food Crops and Horticulture Department of South Sumatra Province. The management of harvest data that still uses spreadsheets makes the analysis, reporting, and decision-making processes less effective. The study used a qualitative descriptive method with data collection techniques in the form of observation, interviews, and documentation studies. Data processing implemented Data Preparation stages including Data Ingestion, Data Cleaning, Data Transformation, Data Integration, and Data Extraction, while system development used the Prototype method. The results showed that the dashboard was successfully built with six main visualizations, namely KPI, Rice Production Trends, Corn Production Trends, Total Commodity Comparison, Regional Production Map, and Regional Production Performance. The dashboard is equipped with interactive filters based on period, location, and commodity to facilitate data analysis. The analysis results showed that rice production was the highest in South Sumatra Province in 2024 at 2,909,411.67 tons with Banyuasin Regency as the largest producer, while corn production reached 2,501,856.62 tons. The dashboard has been published through Tableau Public and is able to increase the efficiency of monitoring, information presentation, and support faster, more accurate, and data-driven decision-making.
Pembentukan Rule Otomatis Berbasis Maximum Membership pada Fuzzy Tsukamoto untuk Prediksi Persediaan Pakaian
Effective inventory management is essential for clothing retailers to avoid overstock and stockout, both of which can increase operational costs and reduce customer satisfaction. The Fuzzy Tsukamoto method has been widely used for inventory prediction; however, its rule base is generally constructed manually using expert knowledge, making it less adaptable to different datasets. This study proposes an automatic rule generation approach for the Fuzzy Tsukamoto method based on historical inventory data. The proposed method applies the Maximum Membership principle to assign linguistic labels with the highest membership degree for each input variable, thereby generating fuzzy rules automatically without expert intervention. The model uses three input variables, namely initial stock, incoming goods, and outgoing goods, to predict the final stock. The proposed approach was evaluated using 12 historical inventory records, and prediction performance was measured using the Mean Absolute Percentage Error (MAPE). The experimental results show that the proposed method automatically generated 10 unique fuzzy rules and achieved a MAPE of 14.97%, which is lower than the 17.99% reported in a previous study. These findings demonstrate that automatic rule generation can simplify rule construction while providing satisfactory prediction accuracy for clothing inventory management.
Implementasi Website E-Commerce untuk Meningkatkan Penjualan Toko Kelontong di Era Digital
The rapid advancement of information technology in the digital era has encouraged Micro, Small, and Medium Enterprises (MSMEs) to adopt internet-based sales systems to improve operational efficiency and business competitiveness. Toko Kelontong Ibu Soimah continues to face several operational challenges, including manual sales processes, unstructured transaction records, and marketing activities limited to the surrounding neighborhood. This study aims to implement an e-commerce website as an online sales platform and evaluate its impact on improving the store's sales performance. The system was developed using the Prototype method, consisting of five stages: Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, and Deployment, Delivery & Feedback. The application was developed using PHP with the Laravel framework and MySQL as the database management system. System functionality was evaluated through Black Box Testing, while user acceptance was measured using User Acceptance Testing (UAT) with a Likert scale. The testing results indicated that all major system features functioned properly without significant errors. The UAT produced an average score of 4.4, categorized as "Very Good," indicating that the system is user-friendly and meets operational requirements. The implementation of the e-commerce website generated significant positive outcomes, increasing weekly transactions by 75% (from 20 to 35 transactions) and sales revenue by 75% (from IDR 2,000,000 to IDR 3,500,000). These findings demonstrate that the e-commerce website effectively expands market reach, improves operational efficiency, and enhances sales performance at Toko Kelontong Ibu Soimah.
Optimasi Keuntungan Penjualan Produk pada Warung Bakso Menggunakan Metode Linear Programming
Increasing competition in the culinary business sector requires entrepreneurs to manage resources efficiently in order to maximize profits. One approach that can be used to determine the optimal production combination is the Linear Programming method. This study aims to optimize product sales profits at Warung Bakso Mas Manda Medan by considering the limitations of available raw materials. The method used in this research is the Simplex Method in Linear Programming. The research data consist of simulated data developed based on the operational conditions of the meatball business, including raw material requirements, production capacity, and profit per product. The results show that the optimal production combination is achieved by producing 80 portions of Bakso Urat and 40 portions of Mie Bakso per day. This combination generates a maximum profit of IDR 1,520,000 per day. The findings demonstrate that the Linear Programming method can assist culinary MSME owners in making more effective and efficient production decisions, thereby increasing business profitability.
Sistem Informasi E-SPB Pada PT. Ciliandra Perkasa Berbasis Web Menggunakan Codeigniter
Palm oil (Elaeis guineensis Jacq.) is a strategic commodity in Indonesia’s plantation industry, contributing significantly to the national economy through the production of vegetable oil. However, the recording process in managing Fresh Fruit Bunches (FFB), as practiced at PT Ciliandra Perkasa, is still largely manual, relying on Fruit Delivery Notes (Surat Pengantar Buah or SPB). This method is prone to recording errors, data loss, and complicates logistics tracking. To address these issues, a web-based information system is needed to digitize the recording and management process of SPB. The development of the E-SPB Information System based on the CodeIgniter framework is specifically designed to support FFB management at PT Ciliandra Perkasa. This system includes features for managing SPB data, vehicle information, transportation locations, and harvest trip transactions. Through digitalization, the company can improve efficiency, transparency, and data accuracy, which directly impacts the quality of decision-making. The implementation of this system is expected to promote the modernization of palm oil logistics management and meet the needs of an increasingly competitive industry.
Pengaruh Komunikasi Electronic Word of Mouth terhadap Brand Awareness Warung Kopi Pagi-Sore (WKPS) Berau
This study examines the effect of Electronic Word of Mouth (e-WOM) communication on the brand awareness of Warung Kopi Pagi Sore (WKPS), a local coffee-shop micro, small, and medium enterprise (MSME) in Berau Regency, East Kalimantan. The rapid growth of digital platforms such as Instagram, Google Maps, and WhatsApp has positioned consumers not only as message receivers but also as communicators who share reviews, experiences, and recommendations. This research used a quantitative approach with a survey method. Data were collected from 100 respondents selected through non-probability purposive sampling, based on the criterion of being at least 17 years old and familiar with WKPS through digital media. Data were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS 4. The measurement model showed that all indicators were valid (outer loading > 0.70; AVE > 0.50) and reliable (Cronbach's Alpha and Composite Reliability > 0.70). The structural model produced a path coefficient of 0.878, a T-statistic of 27.562 (> 1.96), and a P-value of 0.001 (< 0.05), indicating that e-WOM has a positive and significant effect on brand awareness, with an R-Square of 0.770. These findings confirm that consumer-generated digital communication functions as a horizontal, credible communication channel that strengthens brand recognition, recall, and consideration. The study contributes theoretically to Brand Equity theory and practically offers guidance for local coffee-shop MSMEs in optimizing digital word-of-mouth strategies to build brand awareness at low cost.
Analisis Tingkat Kesadaran Keamanan Siber Mahasiswa STMIK Pesat Nabire Provinsi Papua Tengah terhadap Ancaman Phishing dan Malware
The expansion of digital services in higher education has increased students' dependence on the internet for accessing academic information, communicating, storing data, and using application-based services. These benefits are accompanied by higher exposure to phishing and malware threats. This study analyzes the cybersecurity awareness level of STMIK Pesat Nabire students in Central Papua Province toward phishing and malware threats using pre-test and post-test measurements. A quantitative approach with a pre-experimental one-group pre-test and post-test design was applied. The instrument consisted of 15 Likert-scale items grouped into three dimensions: phishing threat awareness, malware threat awareness, and user self-protection behavior. Based on participant-name and student-number matching between the pre-test and post-test sheets, 120 respondents had complete paired data. The findings show that the mean cybersecurity awareness score increased from 2.83 in the pre-test to 3.89 in the post-test. The increase was statistically significant at the 5 percent level (t = 22.241; p < 0.001), with a strong effect size (Cohen's dz = 2.03). The largest improvement occurred in the malware threat dimension, followed by phishing threat awareness and user self-protection behavior. These findings indicate that practical cybersecurity education can strengthen students' awareness, particularly in recognizing unsafe software, suspicious links, and secure account practices. However, self-protection practices such as MFA/2FA, VPN use, and password manager adoption require continuous reinforcement.
Klasifikasi Tingkat Kematangan Pisang Emas Menggunakan Support Vector Machine Berbasis Fitur Warna RGB
The ripeness level of Pisang Emas (Cavendish banana) significantly affects its quality and market value, yet manual identification by humans is often subjective and inconsistent. This study proposes an automated banana ripeness classification system using Support Vector Machine (SVM) with color features extracted from the RGB color space. A total of 600 images were collected, consisting of 150 images for each of four classes: unripe, half-ripe, ripe, and overripe. Each image was processed to extract six color features, namely the mean and standard deviation of the Red, Green, and Blue channels. The dataset was split into 80% training data and 20% testing data using a stratified approach. Hyperparameter optimization was performed using Grid Search with 5-fold Cross-Validation to determine the optimal values for the C, gamma, and kernel parameters. The best model, using an RBF kernel, achieved an accuracy of 96.67% on the testing data, correctly classifying the majority of samples across all four ripeness classes. The classification results show that simple RGB-based color features combined with an optimized SVM model are highly effective for distinguishing banana ripeness stages. These findings indicate strong potential for implementing this low-complexity approach in practical agricultural quality control and sorting systems.
Evaluasi User Experience Sistem Informasi TboticsEducation menggunakan User Experience Questionnaire (UEQ) pada Tbotics Jakarta
Innovations in the field of information technology emphasize how crucial it is to evaluate user experience in order to provide top-notch digital services. The purpose of this study is to apply the User Experience Questionnaire (UEQ) approach to assess the Linkin.bio/TboticsEducation website's user experience. One hundred users who had engaged with the site were given questionnaires as part of a quantitative approach. Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty were the six UEQ qualities covered by the 26 questions that made up the research instrument. The findings of the instrument evaluation showed that every questionnaire item had very high reliability (a Cronbach's Alpha value of 0.940) and was valid (calculated r-values exceeded the critical r-table value of 0.195). Attractiveness (1.55), Perspicuity (1.56), Efficiency (1.64), Dependability (1.51), Stimulation (1.33), and Novelty (1.12) were all shown to have positive mean ratings. Based on UEQ benchmarks, the Efficiency and Dependability dimensions fell into the "Good" category, while Attractiveness, Perspicuity, Stimulation, and Novelty fell into the "Above Average" category. These results indicate that the Linkin.bio/TboticsEducation website provides a satisfying user experience and effectively meets user needs in supporting the learning of robotics systems. However, the Stimulation and Novelty aspects require improvement through the development of more innovative and engaging features to enhance the overall quality of the user experience.
Perbandingan Algoritma K-Means dan K-Medoids Clustering Penjualan Menu Restoran Bebek Cak Selem Gresik
Restoran Bebek Cak Selem Gresik in Gresik, East Java, recorded 11,339 transactions across 39 menu items from January to May 2025, yet this data had never been used as a basis for sales strategy. This study applies and compares K-Means and K-Medoids to group menu items by units sold and price, using the CRISP-DM framework. The optimal number of clusters was determined via the Elbow Method and evaluated using Silhouette Score, Davies-Bouldin Index, and Adjusted Rand Index (ARI). K=3 was found optimal, with both algorithms producing an identical Silhouette Score of 0.5368, Davies-Bouldin Index of 0.5861, and ARI of 1.0, proving both generate exactly the same cluster partition. Three menu segments were formed: 8 high-selling, 19 medium-selling, and 12 low-selling menus, each with a distinct sales-strategy recommendation, translated into an interactive HTML dashboard in owner-friendly language. K-Means is the more practical choice for restaurant-scale data, delivering equal clustering quality at substantially lower computation cost.
Implementasi Sistem Penunjang Keputusan Penentuan Peringkat Terbaik Sekolah Menengah Atas Menggunakan Metode TOPSIS di Jakarta Selatan
This study applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the best ranking of Senior High Schools in South Jakarta. The aim of this research is to develop a web-based decision support system that assists the school assessment process in an objective, measurable, and transparent manner. The TOPSIS method was chosen because it can compare each alternative based on its distance to the positive and negative ideal solutions, resulting in more accurate rankings. The criteria used include the UTBK score, school accreditation, teacher-to-student ratio, and transportation access. The data were processed through the stages of decision matrix construction, normalization, weighting, positive and negative ideal solution determination, distance calculation, and preference value determination to produce the best ranking results. The system was built using PHP and MySQL and was tested using Black Box and White Box testing. The results show that the application of the TOPSIS method provides consistent calculations aligned with the assessment criteria, with alternative A2 obtaining the highest preference value of 0.820. The developed system can clearly and easily display ranking results while minimizing subjectivity in decision-making, so it can serve as an effective tool in evaluating the quality of education in South Jakarta.
Rancang Bangun Pemantauan Kematangan Buah Pisang Cavendish Secara Real-Time Berbasis Internet of Things
Cavendish bananas are one of the most widely cultivated fruits and have a relatively high economic value. Determining the ripeness level of Cavendish bananas is essential to obtain good quality and harvest yields. However, this process is still commonly carried out manually based on visual observation of the fruit. This study aims to develop a real-time monitoring system for Cavendish banana ripeness using a TCS3200 color sensor and an ESP32-CAM camera based on the Internet of Things (IoT). The research employed the Waterfall method, which consists of requirements analysis, system design, implementation, and system testing stages. The TCS3200 sensor was used to read RGB color values from Cavendish bananas, while the ESP32-CAM was utilized to capture images of the bananas and leaves. The acquired data were then processed using fuzzy logic to classify fruit ripeness into three categories: unripe, semi-ripe, and ripe. The testing results showed that the system was able to detect color changes in Cavendish bananas and leaves and transmit the data to Firebase for display on a monitoring website. The average time required to send data from the device to the website ranged from 1 to 2 seconds. Based on these results, the developed system can assist in monitoring banana ripeness, enabling faster and easier determination of harvest time. In addition, the system can serve as a technology-based monitoring alternative to help farmers supervise crop conditions more effectively and efficiently.
Analisis Pengelompokan Wilayah Penerima Bantuan Sosial Menggunakan Algoritma K-Means dengan Visualisasi Peta Interaktif Berbasis Web
Parung District, Bogor Regency, is one of the regions receiving social assistance sourced from the Integrated Social Welfare Data (DTKS). Distribution of social assistance in this area has been supported by available recipient data; however, the data has not been grouped and analyzed systematically based on regional or geographical distribution. Recipient data is still processed in tabular form using spreadsheet files, so related parties find it difficult to quickly identify villages with high, medium, or low concentrations of social assistance recipients. Without spatial visualization, the distribution pattern across regions is difficult to monitor, hindering decision-making related to distribution priorities and oversight. To address this issue, this study applies the K-Means Clustering algorithm to group regions of social assistance recipients based on location coordinates (latitude and longitude). The data used consists of 6,946 rows of Village Fund Direct Cash Assistance (BLT-DD) recipient data spread across 9 villages in Parung District. Data was grouped into three categories (High, Medium, Low), with the number of clusters validated using the Elbow Method and Silhouette Score. The system was implemented using Python with the Flask framework, a MySQL database, and Leaflet.js to display clustering results on an interactive web-based map. Testing shows that K=3 produced a Silhouette Score of 0.5367, verified through manual calculation with 100% agreement with the program output.
Analisis Performa Hadoop Multi-Node Cluster pada Skenario Pemrosesan Small Files
The rapid growth of Big Data demands computing systems that are capable of processing data efficiently and in a distributed manner. Apache Hadoop, as a distributed computing framework, is widely used to address the limitations of centralized systems; however, it still faces challenges in handling small files, which can lead to increased file management overhead and imbalanced resource utilization. This study aims to analyze the performance of a Hadoop Multi-Node Cluster in small file processing scenarios, particularly in terms of workload distribution and CPU and memory resource utilization across nodes. The research method involves implementing a Hadoop Multi-Node Cluster in a local environment with a configuration of one master node and five worker nodes, followed by concurrent small file processing tests using the MapReduce mechanism. Performance evaluation is conducted by monitoring resource utilization through Hadoop ResourceManager, as well as Prometheus and Grafana monitoring systems to obtain real-time performance visualizations. The results of this study are expected to provide insights into the effectiveness of resource distribution in Hadoop Multi-Node Clusters for handling small files and to serve as a reference for the development and evaluation of distributed computing systems in academic and research environments.
Perancangan Jaringan untuk Pengembangan Infrastruktur Kantor menggunakan Metode NDLC
The development of a company's operational needs requires a more structured, efficient, and adaptive computer network infrastructure that can support organizational growth. The expansion of the office from one shophouse unit into two shophouse units increases the need for wireline and wireless networks capable of supporting user connectivity, work devices, internet access, and supporting devices such as CCTV. This research aims to design a wireline and wireless network that supports the development of office infrastructure using the Network Development Life Cycle (NDLC) method. The proposed design includes network topology planning, structured IP addressing, network segmentation using Virtual Local Area Network (VLAN), guest network access restriction, and multi-ISP connectivity through failover and failback mechanisms. The network simulation was carried out using GNS3 with MikroTik CHR as the main device to perform routing, VLAN, DHCP, NAT, firewall, and failover functions. The test results show that each client can obtain an IP address according to its respective VLAN, connect to the gateway, and access the internet. VLAN segmentation successfully separates the network based on its functions, namely Admin, Operational, CCTV, and Guest WiFi. Firewall testing shows that the Guest network can still access the internet but cannot access the internal network. In addition, the failover and failback mechanisms successfully operate when the primary ISP link is disabled and re-enabled. Thus, the proposed network design is able to represent the needs of office infrastructure development in a more structured manner and in accordance with the research objectives.