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[Enhancing Chronic Kidney Disease Classification Using Decision Tree And Bootstrap Aggregating: Uci Dataset Study With Improved Accuracy And Auc-Roc ]]>
<![CDATA[Zuriati, Zuriati]]>;
<![CDATA[Meilantika, Dian]]>;
<![CDATA[Arpan, Atika]]>;
<![CDATA[Permata, Rizka]]>;
<![CDATA[Sriyanto, Sriyanto]]>;
<![CDATA[Mas'ud, Mohd. Zaki]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5271
<![CDATA[Chronic Kidney Disease (CKD) is a progressive medical disorder that requires timely and precise identification to avoid permanent impairment of kidney function. However, Decision Tree models, although widely used in clinical applications due to their transparency, ease of implementation, and ability to handle both categorical and numerical data, are prone to overfitting and instability when applied to small or imbalanced datasets. The purpose of this study is to optimize CKD classification by integrating Bootstrap Aggregating (Bagging) with Decision Tree to enhance accuracy and robustness. The methodology involves testing two model variants a standalone Decision Tree and a Bagging-supported Decision Tree using 10-fold cross-validation and evaluating performance with accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Findings reveal that Bagging enhances model accuracy from 0.980 to 0.987, raises precision from 0.976 to 1.000, and improves recall from 0.954 to 0.954, and increases F1-score from 0.965 to 0.976. These results demonstrate that Bagging significantly improves the reliability and generalizability of Decision Tree classifiers, making them more effective for CKD prediction.]]>
<![CDATA[Web-Based Diabetes Risk Prediction System Using K-NN on Kaggle Early Stage Diabetes Dataset ]]>
<![CDATA[Ruziq, Fahmi]]>;
<![CDATA[Wayahdi, M. Rhifky]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5277
<![CDATA[Diabetes mellitus affects approximately 537 million adults globally, and its rising prevalence poses serious health and economic burdens. Early detection is crucial to reduce risks of complications and improve patient outcomes. This study aims to design and implement a web-based diabetes risk prediction system using the K-Nearest Neighbors (K-NN) algorithm to support early detection based on symptoms. The system utilizes the Kaggle Early Stage Diabetes Risk Prediction Dataset containing 520 records with 17 symptom attributes and one class label. Data preprocessing includes converting categorical data into numerical values, discretizing age into predefined ranges, and applying min-max scaling to normalize feature values. K-NN classification was conducted with K values of 1, 3, and 5, using the PHP Machine Learning (PHP-ML) library and MySQL database integration. The system achieved its highest accuracy of 93.46% at K = 1. Manual testing confirmed that the system processes symptom inputs correctly and provides predictions consistent with training data. This web-based tool offers an accessible platform for early diabetes risk screening, supporting self-assessment and triage. It demonstrates that PHP-ML can effectively implement machine learning in a web environment and can be further enhanced through parameter optimization and integration with larger, more diverse datasets to strengthen generalization.]]>
<![CDATA[An Integrated Pipeline with Hierarchical Segmentation and CNN for Automated KTP-el Data Extraction on the e-Magang Platform ]]>
<![CDATA[Syafrie Rahardian, Nuansa]]>;
<![CDATA[Maryanto, Eddy]]>;
<![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 ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5279
<![CDATA[In alignment with Indonesia's digital transformation agenda, this research addresses the inefficiencies and error-prone nature of manual data entry on the Foreign Policy Strategy Agency's (BSKLN) e-magang platform. This study introduces a comprehensive, end-to-end Optical Character Recognition (OCR) pipeline, specifically designed for structured identity documents and real-world government platform integration. The proposed methodology features a robust workflow, including image preprocessing with histogram matching, hierarchical segmentation using vertical projection, and intelligent postprocessing to structure the output. To overcome the limitations of a small dataset, three specialized Convolutional Neural Network (CNN) models were rigorously trained and validated using a stratified 5-fold cross-validation technique. The final system was successfully integrated, connecting a Flask-based model engine with the existing Laravel and React platform. End-to-end testing demonstrated strong performance, achieving an average character-reading accuracy of 93.31% with a mean processing time of 14.48 seconds per image. The primary contribution of this research to the field of informatics is the development of a complete and deployable system architecture that ensures data interoperability and reliability, providing a practical blueprint for integrating intelligent automation into digital public services.]]>
<![CDATA[Implementation of Clustering on Packaged Coffee Sales Data for Simulating Goods Entry in Sole Proprietorship Businesses ]]>
<![CDATA[Paramestuti, Ayu Anjar]]>;
<![CDATA[Wijayanto, Bangun]]>;
<![CDATA[Triansyah, Mochammad Agri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5283
<![CDATA[In retail businesses operating under the sole proprietorship structure, decision-making regarding partnerships with beverage distributors—especially those offering packaged coffee—remains a challenge. Store owners often face uncertainty about the profitability of accepting product offerings, which can lead to suboptimal inventory decisions. This study addresses that issue by simulating goods entry scenarios and applying clustering techniques to historical packaged coffee sales data, enabling data-driven insights into product performance and distributor value. Studies focusing on clustering within retail include segmenting customer behavior and stock management strategies, yet many lacked specific application to single owner businesses and product-centric simulations. This research is novel in its contextual focus on packaged coffee distribution within sole proprietorship environments, integrating real sales metrics and clustering algorithms to empower store owners with actionable evaluation tools. Results demonstrate that clustering reveals patterns of profitable product categories and distributor consistency, offering scalable insights for micro-retail optimization. The findings provide a framework that differs from prior studies by emphasizing the intersection between small business dynamics and algorithmic decision support.]]>
<![CDATA[RNN-Based Intrusion Detection System for Internet of Vehicles with IG, PCA, and RF Feature Selection ]]>
<![CDATA[Purnama, Benni]]>;
<![CDATA[Winanto, Eko Arip]]>;
<![CDATA[Sharipuddin, Sharipuddin]]>;
<![CDATA[Sandra, Dodi]]>;
<![CDATA[Nurhadi, Nurhadi]]>;
<![CDATA[Afuan, Lasmedi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5293
<![CDATA[Cyberattacks in the Internet of Vehicles (IoV) threaten road safety and data integrity, requiring intrusion detection systems (IDS) that capture temporal patterns in vehicular traffic. This study develops a Recurrent Neural Network (RNN)-based IDS and evaluates three feature-selection strategies—Information Gain (IG), Principal Component Analysis (PCA), and Random Forest (RF)—on the CICIoV2024 dataset. Features are normalized using Min–Max scaling before being fed into the RNN classifier. The models achieve perfect classification on held-out tests (accuracy/precision/recall/F1 = 1.00). However, probabilistic evaluation reveals low ROC–AUC scores (IG: 0.572, PCA: 0.429, RF: 0.415), indicating limited discriminative margins and potential overfitting or calibration issues despite flawless confusion matrices. PCA and RF further reduce computational overhead during inference compared to IG. These findings highlight that relying solely on accuracy can be misleading for IDS evaluation; temporal RNNs should be complemented with probability-aware training, calibration, or hybrid architectures. This work contributes a temporal-aware IDS framework for IoV and motivates future research on real-time deployment, hybrid RNN-CNN/LSTM models, and adversarial robustness to improve generalization and safety of connected vehicles]]>
<![CDATA[PROTEGO: Improving Breast Cancer Diagnosis with Prototype-Contrastive Autoencoder and Conformal Prediction on the WDBC Dataset ]]>
<![CDATA[Hiswati, Marselina Endah]]>;
<![CDATA[Diqi, Mohammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5294
<![CDATA[Breast cancer remains one of the leading causes of mortality among women, making accurate and trustworthy early detection a critical challenge in healthcare. To address this, we propose PROTEGO, a Prototype-Contrastive Autoencoder with integrated Conformal Prediction, designed to achieve both high diagnostic accuracy and reliable uncertainty quantification. The framework combines dual-head autoencoding, supervised contrastive learning, prototype-based regularization, and conformal calibration to generate discriminative yet interpretable representations. Using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, PROTEGO was trained and evaluated through stratified data splits, with performance measured by AUROC, AUPRC, F1-score, Balanced Accuracy, Brier score, calibration error, and conformal coverage metrics. The results show that PROTEGO achieves highly competitive performance with an AUROC of 0.992 and an AUPRC of 0.995, while uniquely providing conformal coverage guarantees with an average set size close to one and more than 92% decisive predictions. Ablation studies confirm the complementary role of each component in enhancing both accuracy and calibration. These findings demonstrate that integrating prototype-guided representation learning with conformal prediction establishes a clinically meaningful diagnostic framework. PROTEGO highlights the importance of unifying precision and reliability in medical AI, offering a step toward more interpretable, safe, and clinically trustworthy systems for breast cancer detection.]]>
<![CDATA[Comparison of Port Scanning, Vulnerability Scanning, and Penetration Testing Combinations for Network Vulnerability Detection in GNS3 Testbed ]]>
<![CDATA[Rusdianto, Rusdianto]]>;
<![CDATA[Yusuf, Raka]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4917
<![CDATA[Network security faces significant challenges due to the increasing number and complexity of system vulnerabilities. This study aims to develop and evaluate a full combination method (ABC) integrating port scanning (Nmap), vulnerability scanning (OpenVAS), and penetration testing (Metasploit), and compare it with partial combinations (AB, BC, AC) for more effective vulnerability detection. Using a quantitative experimental approach within a controlled GNS3 TestBed, three key indicators were analyzed: number of vulnerabilities detected, detection time, and exploit validity. Experimental results show that the ABC method detected 62 potential vulnerabilities, including 11 high and medium severity CVEs, matching the AB method but significantly outperforming AC, which detected none. In terms of detection time, the ABC method achieved a balanced performance at 91 minutes, which is 31.5% faster than AB (133 minutes), while maintaining full exploit validation. Notably, the ABC method successfully validated 100% of critical vulnerabilities using Metasploit, confirming the practical applicability and reliability of the integrated approach compared to dual combinations. Overall, the findings demonstrate that the full combination method (ABC) offers superior accuracy and comprehensiveness in detecting and validating network vulnerabilities. This research contributes to cybersecurity practices by proposing an integrated detection workflow that effectively balances speed and depth of analysis, setting a practical benchmark for vulnerability detection systems applicable to both simulated and real-world network environments.]]>
<![CDATA[Carrot Quality Classification Based on Color and Texture Features Using Artificial Neural Network Method ]]>
<![CDATA[Idris, Muh Gimnastiar]]>;
<![CDATA[Fauzi, A. Arfan]]>;
<![CDATA[Syasikirani. N, Adelia]]>;
<![CDATA[Kaswar, Andi Baso]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.1401
<![CDATA[Carrots are popular vegetable plants that are usually consumed by the public. Determination of quality using the visual of human eye is considered to have many shortcomings. In previous studies, the carrot classification process had been carried out using a certain method. However, the level of accuracy resulting from several previous studies is still lacking because the processes and methods used are considered to be inaccurate, so innovation is needed by using processes and methods that are more precise to obtain classification results with a better level of accuracy. Therefore, this research proposes a classification of carrot quality based on color and texture features using an artificial neural network method. The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification using artificial neural networks. In this study, quality is divided into three classes, namely feasible, less feasible, and not feasible using 300 carrot image datasets. The results obtained in the testing process obtained an accuracy of 100%, a misclassification error of 0%, and a computation time of up to 55 seconds. Based on the test results it can be seen that the proposed method can classify the quality of carrots accurately.]]>
<![CDATA[STUDENT FOCUS DETECTION USING YOU ONLY LOOK ONCE V5 (YOLOV5) ALGORITHM ]]>
<![CDATA[Rosalina, Rosalina]]>;
<![CDATA[Bimantoro, Fitri]]>;
<![CDATA[Suta Wijaya, I Gede Pasek]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.1977
<![CDATA[Education has a very important role in life, student involvement in the learning process in the classroom is an important factor in the success of learning. However, some students pay less attention to the lesson, indicating a lack of productivity in learning. The use of machine learning and computer vision techniques has undergone significant development in the last decade and is applied in a variety of applications, including monitoring student attention in the classroom. One of the commonly used techniques in machine learning and computer vision to detect objects is by applying image processing. One of the algorithms implemented for object detection that can provide good results is You Only Look Once. This research proposes the application of YOLOV5 in real time student focus detection and analyzes the performance and computational load of the five YOLOV5 architectures (YOLOV5n, YOLOV5s, YOLOV5m, YOLOV5l, and YOLOV5x) in student surveillance during classroom learning. The dataset used is video data that has been converted into image form, and 297 images are produced. Where, this dataset is divided into 2 classes, namely the "Focus" and "Not Focus" classes. The results show that YOLOV5x has the highest computational load with large parameter values and GFLOPs. However, in term model performance YOLOV5m provides more optimal results than other architectures, with precision of 83.3%, recall of 85.1%, and mAP@50 of 89.9%. The results of this study show that the proposed YOLOV5 model can be a good performing method in detecting student focus in real time.]]>
<![CDATA[CYBERBULLYING SENTIMENT ANALYSIS OF INSTAGRAM COMMENTS USING NAÏVE BAYES CLASSIFIER AND K-NEAREST NEIGHBOR ALGORITHM METHODS ]]>
<![CDATA[Anisa Nirmala, Fitri]]>;
<![CDATA[Jazman, Muhammad]]>;
<![CDATA[Rozanda, Nesdi Evrilyan]]>;
<![CDATA[Salisah, Febi Nur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.1997
<![CDATA[The high number of social media users presents major threats and risks, such as cyberbullying Cyberbullying or cyberbullying is one of the negative impacts of the rapid development of technology and social media. Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiment. One of Indonesian social media that often gets user sentiment through social media is Instagram. By using the Text Mining technique, the classification method will determine whether a sentiment is positive, neutral or negative. This research uses the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods with tf-idf weighting accompanied by the addition of an emotional icon (emoticon) conversion feature to determine the existing sentiment classes from tweets about Instagram users. The results of calculations using the first three methods using the Partitionong model, the results using the Naive Bayes method, get an accuracy value of 91.25%, a recall value of 92% and a precision value of 90% and calculations using the KNN method have an accuracy value of 67%, a recall value of 49% and a precision value of 34 %. So it can be concluded that the Naïve Bayes Classifier algorithm has the best performance.]]>
