Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
962 Documents
<![CDATA[Data-Driven Student Group Formation for Group Investigation: A K-Medoids Clustering Approach in Cooperative Learning ]]>
<![CDATA[Alyasyifa, Salma]]>;
<![CDATA[Pratiwi, Oktariani Nurul]]>;
<![CDATA[Darmawan, Irfan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4765
<![CDATA[Group Investigation (GI) is a widely used cooperative learning strategy in higher education, but challenges such as large class sizes and diverse student profiles complicate manual group formation. Previous studies have applied clustering algorithms like K-Means, yet K-Medoids, which is robust to noise, remain underexplored for group formation, especially GI. This study proposed a data-driven approach using the K-Medoids clustering algorithm to create student groups that are both interest-aligned and heterogeneous in profile, which enhancing the effectiveness of GI activities. Employing the Knowledge Discovery in Databases (KDD) framework, the process included data selection, preprocessing, transformation, three grouping processes, and evaluation were performed. In grouping process students were initially grouped by interest, clustered using K-Medoids with various distance measures tested, and finally, groups were adjusted to balance homogeneity and diversity. In grouping stage 2, clustering with Euclidean distance and PCA achieved the highest Silhouette Score, indicating superior grouping quality. The result of heterogeneity group of students evaluated with Gower dissimilarity shows that the method produces internally diverse yet cohesive interest groups, supporting GI goals.]]>
<![CDATA[Predicting Hypnotherapy Effectiveness Using Ensemble Learning: A Case Study at The Mind Solution Hypnotherapy Clinic ]]>
<![CDATA[Fitrianto, Lindu Budi]]>;
<![CDATA[Zuliarso, Eli]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4859
<![CDATA[Mental health is recognized as a universal human right, yet effective interventions for psychological disorders like anxiety and phobias remain challenging. Hypnotherapy shows promise but suffers from variable effectiveness across individuals, compounded by limited data-driven tools for outcome prediction in clinical settings, particularly in Indonesia where social stigma impedes accessibility. This study aims to (1) identify demographic/clinical factors influencing hypnotherapy success, (2) develop an ensemble learning-based predictive model, and (3) evaluate its performance against conventional methods. Using retrospective data from 276 patients at Mind Solution Hypnotherapy Clinic, we implemented preprocessing (missing values imputation, label encoding) and trained Decision Tree and Random Forest models via Orange Data Mining, validated through *5-fold cross-validation*. Results demonstrate Random Forest superiority (accuracy: 92.7%; precision: 94.2%; AUC: 0.918) over Decision Tree, with key predictors being gender (32.54% gain ratio), occupation (31.75%), and birth order (15.58%). Notably, 71.5% of patients achieved improvement in just one session. These findings confirm ensemble learning’s efficacy in personalizing hypnotherapy protocols, offering clinicians a decision-support tool to optimize resource allocation. The study bridges AI and mental health practice, providing empirical evidence to reduce societal stigma while advancing predictive analytics in psychotherapy.]]>
<![CDATA[Evaluating the Impact of Model Complexity on the Accuracy of ID3 and Modified ID3: A Case Study of the Max_Depth Parameter ]]>
<![CDATA[Asrianda, Asrianda]]>;
<![CDATA[Mawengkang, Herman]]>;
<![CDATA[Sihombing, Poltak]]>;
<![CDATA[K. M. Nasution , Mahyuddin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4864
<![CDATA[The complexity of decision tree structures has a direct impact on the generalization capability of classification algorithms. This study investigates and evaluates the performance of the classical ID3 algorithm and its modified version in the context of tree depth. The primary objective is to identify the optimal accuracy point and assess the algorithms' robustness against overfitting. Experiments were conducted across tree depths ranging from 1 to 20, with accuracy used as the main evaluation metric. The results indicate that both algorithms achieved peak performance at depth 3, followed by a notable decline. While the classical ID3 algorithm exhibited a gradual decrease in accuracy, the modified ID3 showed a sharp drop and performance stagnation between depths 11 and 20. These findings suggest that the modified ID3 algorithm enhances sensitivity in selecting informative attributes but also increases the risk of overfitting in the absence of structural regularization mechanisms. Therefore, the study recommends the implementation of regularization strategies such as pruning and cross-validation to mitigate performance degradation caused by model complexity. This research not only contributes to the theoretical understanding of how tree depth influences classification performance but also offers practical insights for developing adaptive, stable, and accurate decision tree-based classification systems.]]>
<![CDATA[A Bluetooth-Based Attendance System for Educational Administration at SMA Muhammadiyah: Cross-Platform Development and Usability Validation ]]>
<![CDATA[Hayat, Muhyddin A.M.]]>;
<![CDATA[Rasyidi, Muhammad Fachri]]>;
<![CDATA[Faisal, Muhammad]]>;
<![CDATA[Bakti, Rizki Yusliana]]>;
<![CDATA[Syamsuri, Andi Makbul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4873
<![CDATA[The transformation of educational administration through technology has accelerated significantly, particularly in attendance systems, which have traditionally relied on manual roll calls. These conventional methods are time-consuming, error-prone, and susceptible to manipulation. This study presents a novel Bluetooth-based attendance system that contributes to the field by demonstrating passive MAC address detection for automated attendance recording, eliminating the need for additional software installations on student devices. The system was developed using React Native for cross-platform compatibility, with PostgreSQL for data management and NestJS for backend processing. The software engineering process followed Rapid Application Development (RAD) methodology, combined with comprehensive system validation through experimental testing. Usability evaluation with 133 participants using the System Usability Scale (SUS) yielded a score of 79.85, categorizing the system within the "Good to Excellent" usability range. The findings demonstrate significant improvements in efficiency and a reduction in attendance fraud compared to conventional methods. However, hardware quality and device proximity remain key limitations. Future research should explore the integration of Bluetooth Low Energy (BLE) technology, the implementation of machine learning algorithms for anomaly detection, or the development of hybrid validation models that combine multiple authentication factors. This system demonstrates the potential to modernize educational administration through seamless, device-level integration while maintaining high user acceptance.]]>
<![CDATA[Design of a Digital Platform for PAUD Child Development Monitoring Using the Dynamic Systems Development Method and Machine Learning ]]>
<![CDATA[Destriana, Rachmat]]>;
<![CDATA[Aksani, Muhamad Luthfi]]>;
<![CDATA[Yudi Priyanggodo, Dyas]]>;
<![CDATA[Farzani, Revalina]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4958
<![CDATA[This study aims to design a digital platform for monitoring early childhood development in PAUD (Pendidikan Anak Usia Dini) institutions by integrating Machine Learning (ML) into the Dynamic Systems Development Method (DSDM) framework. The research addresses persistent challenges in traditional monitoring systems, which are typically manual, fragmented, and lack real-time responsiveness. Utilizing a Research and Development (R&D) approach, the platform was developed iteratively with active involvement from teachers, parents, and administrators of PAUD institutions. System modeling employed Unified Modeling Language (UML), while ML techniques such as Decision Trees were trained on datasets sourced from PAUD Flamboyan in Tangerang. Key platform features include child data input, growth visualization, predictive analytics, and interactive dashboards. The system underwent black-box testing and usability assessments, achieving an average usability score of 4.5 out of 5. The ML model demonstrated statistically valid and reliable performance with 89% accuracy, 85% precision, and 87% recall in predicting developmental delays. The findings highlight the effectiveness of the DSDM approach in facilitating adaptive system development, and underscore the value added by ML integration in enhancing decision-making within early childhood education. The platform not only streamlines developmental monitoring but also supports early interventions. Future work is recommended to broaden data sources, enrich personalization, and scale deployment across varied PAUD contexts. This study contributes to the advancement of intelligent decision support systems in early childhood education, enabling more accurate developmental monitoring and timely interventions.]]>
<![CDATA[A General-Purpose Web-based TOPSIS tool for Accessible Multi-Criteria Decision Making ]]>
<![CDATA[Bata, Julius Victor Manuel]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4988
<![CDATA[Multi-Criteria Decision Making (MCDM) is a crucial framework for evaluating alternatives based on diverse and often conflicting criteria. However, most existing MCDM tools still require technical skills or software installation, which limits their accessibility for non-technical users. This study introduces Topsisku, a general-purpose web-based decision support system that implements the TOPSIS method. Topsisku enables users to upload datasets, assign weights and criterion types, and obtain ranking results directly through an intuitive web interface. The system was evaluated using three case studies from prior literature, each representing a different domain. Results indicate that the system’s computations are identical to manual calculations, with Spearman rank correlation coefficients approaching perfection (ρ ≈ 1.0; p < 0.001). A usability test involving 10 respondents yielded an average System Usability Scale (SUS) score of 76.5, placing Topsisku in the Good usability category. These findings confirm that Topsisku is both mathematically accurate and user-friendly. The primary contribution of this study lies in democratizing the application of MCDM for non-technical users while maintaining the reliability of the TOPSIS method. Future research directions include the development of advanced sensitivity analysis modules, integration of collaborative multi-user features, and the incorporation of artificial intelligence techniques to enhance system adaptability and decision-making support.]]>
<![CDATA[Design and Implementation of Kernel-Based Quantum Classification Algorithms for Data Analysis in Software Engineering using Quantum Support Vector Machine (QSVM) ]]>
<![CDATA[Abdillah, M. Zakki]]>;
<![CDATA[Nawangnugraeni, Devi Astri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5030
<![CDATA[With the increasing complexity of projects and the volume of data in Software Engineering (SE), the need for efficient and accurate data analysis techniques has become crucial. Classification algorithms play a vital role in various SE tasks, such as bug detection, software quality prediction, and requirements classification. Quantum computing offers a new paradigm with the potential to overcome classical computational limitations for certain types of problems. This research proposes the design and implementation of a kernel-based quantum classification algorithm (also known as Quantum Support Vector Machine - QSVM) tailored for data analysis in the SE domain. We will discuss the fundamental principles behind quantum feature mapping and quantum kernel matrices, and demonstrate its implementation using quantum computing libraries. As a case study, the designed algorithm will be tested on a software bug detection dataset, comparing its performance with classical kernel-based classification algorithms like Support Vector Machine (SVM). The result of the comparison show that QSVM is superior in terms of accuracy, precision, recall, and F1-score compared to SVM.]]>
<![CDATA[Mapping Gestures Based on Text Emotion Classification for a Virtual Chatbot for Early Marriage Consultation in Lombok Using RoBERTa Model ]]>
<![CDATA[Ramadhan, Adam Zahran]]>;
<![CDATA[Wijaya, Rifki]]>;
<![CDATA[Shaufiah, Shaufiah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5038
<![CDATA[To address the persistent issue of early marriage among Indonesian adolescents, this study proposes a virtual counseling chatbot that classifies emotional cues in text using a fine-tuned IndoRoBERTa model. The emotion classification framework is designed to support counseling-based prevention efforts by moving beyond basic sentiment analysis and adopting five functional emotional categories such as ‘Enthusiastic’, ‘Gentle’, ‘Analytical’, ‘Inspirational’, and ‘Cautionary’ to align with psychological counseling styles. Built on fine-tuned IndoRoBERTa architecture, the model was trained in two phases: first with 2,500 manually validated samples yielding 92.8% accuracy, and then with 12,500 auto-labeled entries, resulting in 91.3% accuracy. Performance was assessed using accuracy, precision, recall, and F1-score. A gesture mapping layer was also integrated to enhance empathetic response generation. Each emotion label was paired with a predefined body gesture, grounded in counseling theory, to support future development of multimodal virtual agents capable of expressing emotions both textually and physically. The novelty lies in combining context-aware emotion classification with gesture mapping, enabling future development of expressive, culturally relevant, and empathetic virtual chatbot agents.]]>
<![CDATA[Mapping Facial Expressions Based on Text for Virtual Counseling Chatbot Using IndoBERT Model ]]>
<![CDATA[Padilah, Rifki]]>;
<![CDATA[Wijaya, Rifki]]>;
<![CDATA[Shaufiah, Shaufiah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5039
<![CDATA[Early marriage in Lombok remains a serious issue, with a prevalence rate of 16.59% in 2021, the second highest in Indonesia. Limited access to counseling services, especially in rural areas, poses a significant prevention challenge. This study developed a virtual counseling chatbot system capable of mapping text-based emotions to facial expressions to improve the effectiveness of counseling for early marriage prevention. The methodology involved training an IndoBERT model on a synthetic dataset to analyze conversation texts. The model was designed to classify user input into five functional emotion categories: Enthusiasm, Gentleness, Analytical, Inspirational, and Cautionary. Performance evaluation revealed that the IndoBERT model achieved an outstanding accuracy of 94% in its final phase. This result significantly surpassed other models evaluated, such as CNN (71.6%) and KNN (79%), confirming the superiority of the chosen approach The study concludes that the high-accuracy IndoBERT model is a robust foundation for empathetic virtual agents. This research provides a significant contribution to the fields of Affective Computing and Human-Computer Interaction by demonstrating an effective framework for mapping nuanced, functional emotions from Indonesian text to facial expressions. The proposed system not only offers a scalable technological solution for mental health challenges like early marriage prevention but also highlights the impact of advanced, context-aware NLP models in creating more human-like and empathetic user interactions.]]>
<![CDATA[Comparative Analysis of CNN, SVM, Decision Tree, Random Forest, and KNN for Maize Leaf Disease Detection Using Color and Texture Feature Extraction ]]>
<![CDATA[Arifin, Nurhikma]]>;
<![CDATA[Insani, Chairi Nur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5128
<![CDATA[Corn (Zea mays L.) is an important agricultural commodity in Indonesia, serving as the second staple food after rice and playing a crucial role in supporting national food security. However, corn production is frequently threatened by sudden outbreaks of pests and diseases, making accurate early detection essential to maintaining yield stability. This study aims to detect maize leaf diseases using five classification algorithms: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Convolutional Neural Network (CNN). These algorithms were tested using a combination of texture and color features, including Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Hue-Saturation-Value (HSV), and L*a*b*. The dataset used consists of 2,048 maize leaf images classified into four categories: Blight, Common Rust, Gray Leaf Spot, and Healthy, with 512 images per class. Each class was divided into training and testing sets to train and evaluate the classification models. The results show that CNN achieved the highest accuracy of 93.93% when using a complete combination of color and texture features. Meanwhile, SVM also demonstrated high performance, achieving the same accuracy (93.93%) using only the combination of color features (HSV and Lab*). Random Forest and Decision Tree performed best when using color features alone, with accuracies of 89.81% and 87.14%, respectively. These findings indicate that color features have a dominant influence on classification accuracy, and that combining color and texture features can significantly enhance model performance, particularly in CNN architectures. This study contributes to the development of early disease detection systems in precision agriculture.]]>
