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
1,111 Documents
<![CDATA[Comparative Analysis of Explainable AI Methods LIME, SHAP, and ELI5 on Random Forest Based Indonesian E-Commerce Sentiment Classification ]]>
<![CDATA[Winanta, Haditya Pandu]]>;
<![CDATA[Hana, Muhammad Yusril]]>;
<![CDATA[Hasan, Firman Noor]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5642
<![CDATA[The rapid growth of e-commerce platforms in Indonesia has generated a massive volume of product reviews, making sentiment classification essential for understanding customer perceptions and supporting data-driven decision making. This study aims to develop a sentiment classification model for Indonesia e-commerce product reviews while enhancing model transparency through Explainable Artificial Intelligence (XAI). The proposed approach employs a Random Forest classifier eith Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The dataset consists of 23,194 product reviews from the fashion and electronics categories, classified into positive, negative, and neutral sentiment. Model performance is evaluated using accuracy, precision, recall, and F1-Score metrics. Experimental results show taht the Random Forest model achieves an accuracy of 93.74%, with the best performance observed in the postive sentiment class. To improve interpretability, three XAI methods-LIME, SHAP, and ELI5-are applied. The analysis indicates that LIME is effective for local explanations, SHAP provides consistent global and local feature importence, and ELI5 offers concise and computationally efficient global explanations. This study contributes to the field of computer science by demostrating how comparative XAI analysis can bridge the gap between high-performing black-box models and interpretable sentiment classification in high-dimensional extual data, thereby supporting transparent and accountavle AI system in e-commerce applications.]]>
<![CDATA[Benchmarking Brain-Training Apps Using DEGREE and Fuzzy Logic: Lumosity vs Elevate ]]>
<![CDATA[Sismoro, Heri]]>;
<![CDATA[Kurniawan, Mei Parwanto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5646
<![CDATA[This study provides an actionable benchmark of two popular brain-training apps—Lumosity and Elevate—by applying the 14-factor DEGREE framework as a structured UX evaluation tool and using fuzzy scoring to improve interpretability. We recruited 190 Computer Science undergraduates; each participant evaluated both apps, yielding 380 app evaluations using a counterbalanced two-sheet questionnaire. Fourteen factors covering usability, engagement, and perceived learning were rated on a five-point Likert scale. Reliability was strong for both apps (Cronbach’s α = 0.822 for Lumosity; 0.847 for Elevate). Descriptive results showed mid-to-high perceptions overall, with mean scores of 3.51 (Lumosity) and 3.44 (Elevate). Fuzzy aggregation transformed subjective ratings into a 0–1 index, producing overall scores of 0.503 (Lumosity) and 0.490 (Elevate), indicating a small global advantage for Lumosity. At the factor level, Lumosity was slightly higher on most DEGREE dimensions, whereas Elevate showed relative advantages on Learnability and Confidence, suggesting potential benefits for early onboarding and self-efficacy. Overall, the proposed DEGREE–Fuzzy pipeline yields a transparent, reproducible benchmark that translates multi-factor perceptions into decision-ready recommendations for selecting apps aligned with instructional goals.]]>
<![CDATA[Optimization Of Hybrid K-Means–Naïve Bayes Using Optuna for Classification of Global Plastic Waste Management Levels ]]>
<![CDATA[Madani, Aulya Fani]]>;
<![CDATA[Poningsih, Poningsih]]>;
<![CDATA[Almaida, Zulia]]>;
<![CDATA[Saputra, Widodo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5651
<![CDATA[The rapid growth of plastic waste has become a serious global environmental challenge, while existing waste management analysis methods often struggle to handle large and heterogeneous environmental datasets. This study aims to improve the classification of global plastic waste management performance by integrating K-Means clustering and Naïve Bayes with Optuna-based hyperparameter optimization. Using a dataset of global plastic waste indicators from multiple countries during 2020–2024, K-Means is first applied to generate waste management level clusters, which are then classified using Naïve Bayes. The hybrid model is further optimized by tuning the var_smoothing parameter using Optuna. Experimental results show that the hybrid approach improves classification performance compared to the baseline Naïve Bayes model, while the optimized model increases accuracy from 89% to 95% along with improvements in precision, recall, F1-score, and ROC-AUC. These results indicate that combining clustering-based labeling with automated hyperparameter optimization can enhance the reliability of machine learning models for large-scale environmental data analysis. Therefore, the proposed approach can support more accurate evaluation of global plastic waste management and assist data-driven environmental policy development.]]>
<![CDATA[Security Assessment of JWKS-Based Authentication: Mitigating JWT Attack Vectors Through Penetration Testing ]]>
<![CDATA[Pratama, Ferry Andhika]]>;
<![CDATA[Hermanto, Agus]]>;
<![CDATA[Kusnanto, Geri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5662
<![CDATA[JSON Web Tokens (JWT) have become the de facto standard for stateless authentication in modern web applications and microservices architectures. However, improper implementation exposes systems to critical vulnerabilities including algorithm confusion attacks, signature bypass, and key injection exploits. This paper presents a comprehensive resilience analysis of JSON Web Key Set (JWKS)-based authentication mechanisms against known JWT attack vectors through a systematic penetration testing approach. We implemented and evaluated a production-grade courier management system (City Courier) featuring dynamic JWKS key rotation, RFC 7517-compliant public key distribution, and encrypted private key storage. Our penetration testing methodology systematically evaluated the system against 10 critical JWT attack vectors including algorithm confusion (CVE-2022-29217), kid parameter injection, weak secret exploitation, and signature verification bypass. Results demonstrate that proper JWKS implementation with dynamic key rotation, strict algorithm validation, and comprehensive audit logging provides robust defense against all tested attack vectors. The system successfully mitigated algorithm confusion attacks through explicit algorithm whitelisting, prevented kid injection via UUID-based key identifiers, and maintained security during key rotation events. Performance analysis shows minimal overhead (less than 50ms) for JWKS endpoint queries with aggressive caching. This research contributes practical implementation patterns for secure JWT authentication, providing both empirical evidence for JWKS-based security controls and a validated blueprint to neutralize critical vulnerabilities in modern microservices architectures.]]>
<![CDATA[Comparative Analysis of IndoBERT and mBERT for Online Gambling Comment Detection in Indonesian Social Media ]]>
<![CDATA[Nugraha, Satria Adi]]>;
<![CDATA[Lestari, Citra]]>;
<![CDATA[Sanjaya, Karyna Budi]]>;
<![CDATA[Naya, Rafi Abhista]]>;
<![CDATA[Jolie, Jocelyn]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5677
<![CDATA[The rapid growth of illegal online gambling promotions in Indonesian social media comments requires automated detection systems capable of handling informal and noisy text. This study aims to evaluate the effectiveness of Transformer-based language models for detecting online gambling-related comments in Indonesian Twitter and YouTube data. Two pre-trained models, IndoBERT and mBERT, were fine-tuned and compared using a labeled dataset consisting of gambling and non-gambling comments. Model performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that IndoBERT achieved 98% accuracy and F1-score, outperforming mBERT, which achieved 96% on the same dataset. Additionally, performance was compared against a recurrent neural network baseline to validate the effectiveness of Transformer-based architectures. The findings demonstrate that language-specific pre-training provides measurable advantages for detecting domain-specific content in Indonesian social media. This study contributes empirical evidence supporting the application of Transformer models for automated moderation of harmful online content in Indonesian digital platforms.]]>
<![CDATA[Machine Learning Decision Support System for Heart Disease Prediction with Optuna and Threshold Optimization ]]>
<![CDATA[Ramdhan, William]]>;
<![CDATA[Hutahaean, Jeperson]]>;
<![CDATA[Jollyta, Deny]]>;
<![CDATA[Karim, Abdul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5684
<![CDATA[Cardiovascular disease remains a major global health challenge, necessitating accurate and reliable decision support systems for early detection. This study proposes a machine learning–based decision support system that integrates ensemble learning, automated hyperparameter optimization using Optuna, and decision threshold tuning. The system was evaluated using several baseline machine learning models, including Logistic Regression, SVM, KNN, Decision Tree, and Random Forest, with the Random Forest model selected for optimization. Hyperparameter tuning with Optuna and decision threshold optimization led to a significant improvement in accuracy (95.0%) and ROC–AUC (0.977), with the optimized model outperforming all baseline models. This approach demonstrates improved sensitivity, reduced false negatives, and enhanced predictive performance, offering a clinically reliable tool for early heart disease detection. The results emphasize the importance of model optimization and decision threshold calibration in clinical decision support systems.]]>
<![CDATA[Optimizing YOLO11 for Dense Crowd Counting under Severe Occlusion via Head-Detection Fine-Tuning ]]>
<![CDATA[Sutrisno, Joko]]>;
<![CDATA[Winarno , Sri]]>;
<![CDATA[Affandy, Affandy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5699
<![CDATA[Accurate and real-time people counting is essential for crowd management and public safety, yet achieving precision in high-density environments remains a challenge due to severe visual occlusion. While the recently released YOLO11 architecture introduces advanced features such as C3k2 and C2PSA modules, its performance as a pre-trained model for people counting tasks has not been fully explored. This study evaluates the efficacy of a head-detection-based fine-tuning strategy using the YOLO11 model, compared against the default pre-trained baseline. The fine-tuning performance is analyzed across three distinct scenarios: S1 (full fine-tuning at 960 pixels), S2 (partial backbone freezing at 960 pixels), and S3 (partial freezing at 640 pixels). The fine-tuning process was conducted using the CC_Mach_1 dataset from Roboflow Universe, which consists of high-density images annotated for head detection. The results demonstrate that the baseline pre-trained YOLO11, which relies on full-body features, exhibits extremely limited performance with an mAP@0.5 of 0.017 and a Mean Absolute Error (MAE) of 100.3. In contrast, the fine-tuned scenarios achieved substantial improvements, led by S1 which reached the highest accuracy with an mAP@0.5 of 0.682 and reduced the MAE by 62% to 37.8. While S2 remained highly competitive with an MAE of 39.6, the performance in S3 declined to 46.9, confirming that lower input resolutions limit the model's ability to identify small-scale head features. These findings provide empirical evidence that domain-specific fine-tuning for head detection substantially improves the robustness of YOLO11 against occlusion. Beyond technical accuracy, this detection-based approach offers a more computationally efficient alternative to traditional density-map-based methods, making it highly suitable for deployment in real-time surveillance systems for large-scale public monitoring.]]>
<![CDATA[Job Recommendation for Fresh Graduates to Reduce Competency Gaps Using Content-Based Filtering and Retrieval-Augmented Generation ]]>
<![CDATA[Rahmawati, Iftitah Yanuar]]>;
<![CDATA[Mufarihati, Felda]]>;
<![CDATA[Aditya, Christian Sri Kusuma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5723
<![CDATA[Job recommendation systems are frequently used to help job seekers find suitable positions. Nevertheless, many existing systems focus primarily on accuracy and provide limited justification. This lack of openness can erode user confidence, particularly among recent grads who need a clear explanation of how their individual experiences fit the recommendations. Furthermore, these systems frequently lack sophisticated methods to explain the reasoning behind the recommendations, such as Retrieval-Augmented Generation (RAG), which makes them seem impersonal and difficult to trust. The purpose of this research is to develop an explainable job recommendation system that generates employment suggestions based on language comprehension by integrating RAG and Content-Based Filtering (CBF). User profiles and open positions are displayed using TF-IDF and Sentence-BERT, and then the experience level-based cosine similarity is calculated. To measure competency coverage, matching and absent skills are identified in an explicit skill-gap analysis. The Large Language Model and FAISS-based RAG modules leverage the explanations that are produced by finding matched and missing abilities as context. The CBF approach was used to evaluate recommendation relevance, while BLEU and ROUGE on ten test documents were used by HR specialists for validation. The system's mean ROUGE-1 F1 score was 0.4659, and its mean ROUGE-L score was 0.2918, based on 10 evaluation cases. Results show that the proposed recommendation system provides accurate and adequate guidelines based on HR references. This paper enriches Informatics by consolidating semantic similarity modeling, explicit competency-gap reasoning, and grounded text generation together to form a cohesive explainable recommendation framework targeted to cold-start job seekers.]]>
<![CDATA[Decision Support Systems in Electronic Procurement for Public Sector Procurement: A Systematic Literature Review on Machine Learning Integration ]]>
<![CDATA[Cahyono, Teguh]]>;
<![CDATA[Muhammad, Alva Hendi]]>;
<![CDATA[Wahyuni, Sri Ngudi]]>;
<![CDATA[Al Fatta, Hanif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5747
<![CDATA[This study analyzes the evolution of Decision Support Systems (DSS) and Multi-Criteria Decision Making (MCDM) in public sector procurement between 2020 and 2025. Using bibliometric analysis of Scopus and Web of Science articles, the research focuses on themes such as e-procurement, supplier selection, public procurement, and the integration of intelligent technology. Network visualization, overlays, and density mapping were applied to explore keyword relationships, temporal trends, and research intensity. Findings reveal that in 2020, studies concentrated on transparency and digitalization in public e-procurement, with classical MCDA methods, fuzzy TOPSIS, and semantic DSS dominating the approaches. By 2022–2023, the emphasis shifted toward intelligent technologies, including artificial intelligence, neuro-fuzzy systems, and data mining algorithms. These innovations expanded DSS functions from evaluation to predictive analytics and optimization. Core themes such as supplier selection, optimization, and public procurement remained central, while emerging topics like sustainability and clinical decision support systems pointed to new research directions. A significant gap was identified in the university context. Although public sector e-procurement has been widely studied, no research has specifically addressed DSS–MCDM applications in higher education procurement systems. Consequently, future agendas should prioritize adaptive DSS tailored to universities, blockchain integration for transparency, and AI applications in clinical and humanitarian systems.]]>
<![CDATA[Banking Stock Price Prediction Dashboard Using Long Short-Term Memory ]]>
<![CDATA[Navila, Ilma]]>;
<![CDATA[Nada, Noora Qotrun]]>;
<![CDATA[Renaldy, Ramadhan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5802
<![CDATA[The high volatility of banking sector stocks (BBCA, BBRI, BMRI) and the limitations of conventional forecasting methods in handling non-linear data necessitate robust and adaptive predictive models. This study aims to develop an integrated stock price prediction system utilizing a Stacked Long Short-Term Memory (LSTM) architecture embedded within a Flask-based interactive web dashboard. Adopting the CRISP-DM framework, the model was trained using daily and hourly historical data from Yahoo Finance to accommodate both short-term and medium-term forecasting. Backtesting evaluation demonstrated that the LSTM model achieved Mean Absolute Percentage Error (MAPE) values below 2% for daily single-step predictions and below 0.5% for hourly intraday predictions. Furthermore, in a 7-period recursive projection, the proposed LSTM proved highly robust in mitigating error accumulation compared to Linear Regression and Support Vector Regression (SVR), successfully maintaining MAPE values below 5% for all issuers. The implementation of this dashboard system provides a significant impact on financial informatics by bridging advanced deep learning predictive algorithms into a practical, real-time decision support system for investment analysis.]]>
