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
1,174 Documents
<![CDATA[Prediction of Indonesian Banking Stock Prices Using a Hybrid LSTM and XGBoost Model with Optuna Based Hyperparameter Optimization ]]>
<![CDATA[Admaja Admaja]]>;
<![CDATA[Kurniabudi Kurniabudi]]>;
<![CDATA[Nurhadi Nurhadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5715
<![CDATA[Stock price prediction is a critical task in investment decision-making, particularly in highly volatile financial markets such as the Indonesian banking sector. While Long Short-Term Memory (LSTM) networks are effective in modeling temporal dependencies, they often fail to capture nonlinear residual patterns in financial time-series data, and their performance is highly sensitive to hyperparameter selection. To address these limitations, this study proposes a residual learning–based hybrid LSTM–XGBoost framework optimized using Optuna for predicting stock prices of major Indonesian banking stocks, namely BBCA, BBNI, BBRI, and BMRI. LSTM is employed as the base learner to model log-return sequences, while XGBoost is used to learn nonlinear residual structures from LSTM predictions. Latent embeddings extracted from the LSTM are further refined using Principal Component Analysis (PCA) to reduce redundancy and improve generalization. Hyperparameters of the LSTM, PCA, XGBoost, and calibration components are jointly optimized using Optuna with a Tree-structured Parzen Estimator (TPE) strategy. Experimental results demonstrate that the Optuna-optimized hybrid model consistently outperforms the baseline hybrid model across all datasets, achieving lower Mean Absolute Percentage Error (MAPE) values of 1.196% for BBCA, 1.67% for BBNI, 1.53% for BBRI, and 1.70% for BMRI. Additional stability analyses confirm that the proposed framework delivers stable and reliable predictions on unseen data. These findings provide a scalable hybrid forecasting framework that contributes to the development of intelligent financial decision-support systems and demonstrates the effectiveness of adaptive hybrid deep learning optimization techniques in real-world time-series prediction problems within the field of informatics.]]>
<![CDATA[Optimizing Breast Cancer Classification: SVM and Random Forest with Hybrid Hyperparameter Tuning and Feature Selection ]]>
<![CDATA[Adil Setiawan]]>;
<![CDATA[Soeheri Soeheri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5720
<![CDATA[Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the urgent need for early, accurate, and reliable diagnostic support systems. This study proposes an optimized breast cancer classification framework using Support Vector Machine (SVM) and Random Forest (RF) models enhanced through hybrid hyperparameter tuning and feature selection. The Breast Cancer Wisconsin (Diagnostic) dataset, comprising 569 samples with 30 numerical features derived from Fine Needle Aspirate (FNA) examinations, was utilized in this research. Feature selection was conducted using Random Forest feature importance to identify the most relevant diagnostic attributes and reduce dimensionality. Hybrid hyperparameter tuning was implemented using GridSearchCV combined with 5-fold cross-validation to obtain optimal model configurations. Model performance was evaluated using accuracy, malignant-class recall, confusion matrix analysis, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC). Experimental results show that the optimized SVM model achieved significant improvements in accuracy, recall, and ROC–AUC compared to baseline models, indicating enhanced sensitivity and discrimination capability, while the Random Forest model maintained stable performance with marginal gains after optimization. These findings highlight the critical importance of systematic optimization strategies in improving diagnostic safety and reducing false negatives, thereby contributing to the development of more reliable and clinically applicable machine learning-based medical decision support systems.]]>
<![CDATA[Regional Segmentation of School Dropouts Based on Economic and Accessibility Factors Using K-Means Clustering ]]>
<![CDATA[Juna Eska]]>;
<![CDATA[Dinda Djesmedi]]>;
<![CDATA[Yuhandri Yuhandri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5727
<![CDATA[The high dropout rate in Asahan Regency has become a serious problem affecting the quality of human resources and equitable access to education across various regions. This study aims to identify patterns and characteristics of dropout-prone areas using the K-Means clustering technique. The research method involves collecting dropout data from the Asahan Regency Education Office for the period 2022–2025, followed by data pre-processing for cleaning and normalization, and then clustering analysis to generate three regional clusters based on dropout vulnerability levels. The results indicate that clusters with high dropout rates are largely influenced by economic factors, followed by limited access to education and social conditions in the community. The resulting regional segmentation provides a spatial overview of dropout vulnerability levels in Asahan Regency. These findings offer data-driven insights that can support the formulation of more targeted education policies and programs to encourage inclusive education development in the region. Scientifically, this study contributes to strengthening the validity and effectiveness of the K-Means algorithm as a quantitative approach in mapping and identifying complex patterns in socio-educational data, thereby expanding its application in data-driven analytical studies in the field of education.]]>
<![CDATA[Evaluation of Image Transmission Strategies on Edge Server-Based Centralized Object Detection Systems ]]>
<![CDATA[Firmansyah Achmad Adam]]>;
<![CDATA[Bambang Harjito]]>;
<![CDATA[Fajar Muslim]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5731
<![CDATA[Urban waste management in smart city development requires efficient and stable visual monitoring systems. Utilization of edge devices such as Raspberry Pi is often constrained by limited computational power for complex computer vision models, making edge server architecture a relevant solution. This study evaluates the performance of image transmission from a Raspberry Pi to a centralized server for YOLOv8 object detection by comparing MJPEG streaming and HTTP POST-based periodic snapshot methods. Evaluation metrics included median latency (p50), jitter, and tail latency (p95 and p99). The results indicate that MJPEG streaming provides more stable latency compared to snapshots, particularly at tight transmission intervals. The transmission interval proved to have a significant effect on inference pipeline stability, while image resolution showed no observable impact on latency distribution under the evaluated conditions. This research recommends selecting appropriate transmission strategies to maintain the reliability of visual monitoring systems. These findings provide practical guidance for designing reliable centralized visual monitoring systems in resource-constrained edge environments.]]>
<![CDATA[Information Gain-Based Feature Selection and Machine Learning Classification for DDoS Attack Variant Detection in Cloud Computing Environment ]]>
<![CDATA[Eko Arip Winanto]]>;
<![CDATA[Kurniabudi Kurniabudi]]>;
<![CDATA[Sharipuddin Sharipuddin]]>;
<![CDATA[Denia Igesti Nur Mellyati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5752
<![CDATA[Cloud computing environments face significant security vulnerabilities from Distributed Denial of Service (DDoS) attacks, which can cause system failures and service disruptions. Despite various existing detection methods, challenges remain regarding high computational overhead and suboptimal accuracy due to redundant features in complex datasets. This study aims to identify the optimal feature subset and evaluate its impact on detection performance across multiple machine learning algorithms for multi-class DDoS variants. The research methodology employs a two-stage approach: feature selection using Information Gain (IG) to reduce 47 original features into subsets of 8, 10, 15, and 20, followed by classification using Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB) on the CICIoT2023 dataset. Experimental results demonstrate that the Decision Tree model with an optimized subset of only 8 features, primarily Inter-Arrival Time (IAT), Header_Length, and Tot_size, achieves a superior accuracy of 99.97%. While Naïve Bayes performs well in binary classification, its accuracy drops significantly to approximately 30% in multiclass settings. This study concludes that IG-based feature selection reduces computational complexity by 30-40% while maintaining high performance across 12 DDoS variants. These findings provide a practical framework for scalable and efficient intrusion detection systems suitable for real-time deployment in resource-constrained IoT-cloud environments.]]>
<![CDATA[Implementation of Moving Average and Weighted Moving Average for Forecasting Palm Oil Harvest and Income in a Web-Based GIS System ]]>
<![CDATA[Elvia Andriyani]]>;
<![CDATA[Bambang Agus Herlambang]]>;
<![CDATA[Khoiriya Lathifa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5754
<![CDATA[Independent palm oil farmers face significant challenges in financial management due to inefficient manual recording, fluctuating harvest yields, and volatile Fresh Fruit Bunch (FFB) prices. This study aims to develop a web-based harvest and income recording system integrated with a Geographic Information System (GIS) and forecasting methods to support decision-making. The system is developed using a Research and Development (R&D) approach by comparing Moving Average and a dynamically weighted Moving Average that adapts to price fluctuations for predicting future net income. Model performance is evaluated using Mean Absolute Percentage Error (MAPE) and validated with the Diebold–Mariano test, while system usability is assessed through User Acceptance Testing (UAT). The results show that the dynamically weighted Moving Average achieves a prediction accuracy of 93.08% (MAPE 6.92%), slightly outperforming the standard Moving Average (93.03%), although no statistically significant difference is found based on the Diebold–Mariano test. The system also obtains a “Very Good” usability rating with a UAT score of 95.11%. These findings demonstrate that the proposed approach provides a practical and adaptive forecasting mechanism integrated within a spatial financial management system, contributing to improved decision support and offering methodological value in time-series forecasting for agricultural informatics.]]>
<![CDATA[Comparative Evaluation Of Sparse, Dense, And Hybrid Retrieval Models On Indonesian Wikipedia ]]>
<![CDATA[Tino Saputra]]>;
<![CDATA[Eric Julianto]]>;
<![CDATA[Ari Widjonarko]]>;
<![CDATA[Budi Tjahjono]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5776
<![CDATA[This study presents a comparative evaluation of Information Retrieval (IR) models on the Indonesian Wikipedia corpus, focusing on sparse, dense, and hybrid retrieval approaches. The evaluated methods include TF-IDF and BM25 as sparse models, SBERT (MiniLM) as a dense retrieval model, and hybrid retrieval implemented through score fusion. The dataset consists of 713,044 Wikipedia articles, with experiments conducted using 1,000 test queries. Performance is measured using Precision@10 (P@10) and Mean Reciprocal Rank (MRR). The results show that BM25 achieves the highest performance, with a P@10 of 0.973 and an MRR of 0.9174, significantly outperforming TF-IDF and SBERT. Hybrid retrieval provides a slight performance improvement, where the BM25 + SBERT combination reaches a P@10 of 0.979 and an MRR of 0.9253 at higher α values. These findings indicate that lexical matching remains dominant in encyclopedic corpora, while semantic representations provide complementary improvements. However, the performance gain of hybrid retrieval is relatively marginal compared to the additional computational cost introduced by dense embedding and score fusion processes, indicating a trade-off between effectiveness and efficiency. These results highlight that, for low-resource languages such as Indonesian, lexical-based retrieval remains highly reliable, while hybrid approaches provide incremental improvements. Therefore, this study provides practical guidelines for developing efficient, scalable, and reliable Information Retrieval systems for Indonesian Wikipedia and other low-resource language corpora.]]>
<![CDATA[Reliable Intent Detection in Public Service Chatbots Using Hybrid IndoBERT and Bidirectional Long Short-Term Memory with Confidence-Based Decision Strategy ]]>
<![CDATA[Barka Satya]]>;
<![CDATA[Mei Parwanto Kurniawan]]>;
<![CDATA[Toto Indryatmoko]]>;
<![CDATA[As'adurrofiq As'adurrofiq]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5794
<![CDATA[The rapid digitalization of public services has increased the demand for intelligent information systems capable of providing accurate and responsive assistance to citizens on a 24/7 basis. However, many existing public service chatbots still rely on rule-based mechanisms or single-model natural language processing (NLP) approaches, which often fail to handle linguistic variations, informal expressions, and ambiguous user queries. This study proposes a Hybrid Natural Language Understanding (NLU) architecture that integrates a fine-tuned IndoBERT model with a Bidirectional Long Short-Term Memory (BiLSTM) network to improve intent detection performance in public service chatbots. To enhance system reliability, a confidence-based decision-making mechanism is introduced, enabling the system to dynamically select the most reliable prediction or activate a fallback pattern-matching module when confidence thresholds are not met. The proposed approach was evaluated on a custom dataset comprising 53 public service intents, spanning formal and informal Indonesian language use. Experimental results demonstrate that the hybrid architecture achieves an intent classification accuracy of 86.8%, outperforming single-model approaches while maintaining an acceptable response time for practical deployment, particularly in public service scenarios where accuracy and reliability are prioritized over response speed. Furthermore, integrating a continuous learning mechanism enables the system to adapt to low-confidence queries over time, thereby improving robustness in real-world applications. These findings indicate that hybrid NLP architectures with confidence-aware decision mechanisms offer a practical and scalable solution for intelligent public service chatbots.]]>
<![CDATA[Interpretable and Statistically Validated Comparative Evaluation of EfficientNetB0, MobileNetV2, and ResNet50 for Bold and Natural Makeup Classification on CelebA ]]>
<![CDATA[Aurelia Chiara Suryabangun]]>;
<![CDATA[Abdussalam Abdussalam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5806
<![CDATA[Facial makeup classificationplays a critical role in beauty technology, visual style analysis, and intelligent web-based image inference. Distinguishing bold makeup from natural makeup is challenging due to subtle visual overlap, borderline facial appearance, and inconsistent makeup intensity across images. While numerous prior studies have applied deep learning for facial analysis, most focus solely on conventional performance metrics without addressing statistical validation, probability calibration, or interpretability — a critical gap that limits reliable model selection in visually subtle classification tasks. This study presents an interpretable and statistically validated comparative evaluation of three transfer learning architectures — EfficientNetB0, MobileNetV2, and ResNet50 — for binary makeup classification using a curated CelebA-based dataset. The final dataset comprises 12,000 facial images equally divided into natural_makeup and bold_makeup classes, with separate training, validation, and clean test subsets. Models were evaluated using holdout testing, 10-fold cross-validation, McNemar statistical testing, calibration analysis, confidence intervals, ROC and PR curves, and Grad-CAM visualization. Experimental results show that EfficientNetB0 achieved the best overall performance, with 0.7900 Accuracy, 0.7898 Macro-F1, 0.8829 ROC-AUC, and 0.8461 PR-AUC on the clean holdout test set. Across ten-fold cross-validation, EfficientNetB0 further achieved 0.7801 ± 0.0093 Accuracy and 0.8780 ± 0.0090 ROC-AUC. It also demonstrated the strongest calibration performance, with the lowest Expected Calibration Error (ECE = 0.0558) and Brier Score (0.1449) among all compared models. The selected model was further implemented in a FastAPI-based backend system for web-based prediction. From a broader Informatics and Computer Science perspective, this study contributes a rigorous and reproducible evaluation framework that integrates statistical validation, calibration assessment, and interpretability, enabling more reliable model selection in visually subtle facial analysis tasks and supporting practical deployment in intelligent systems.]]>
<![CDATA[Optimizing Naive Bayes for Sentiment Analysis of M-Passport Reviews Using N-Gram and Synthetic Minority Over-sampling Technique ]]>
<![CDATA[Devia Kartika]]>;
<![CDATA[Sarjon Defit]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5853
<![CDATA[The diverse user perceptions and increasing number of negative reviews of the M-Passport application indicate the need for sentiment analysis-based evaluation to more accurately measure the quality of digital immigration services. This study aims to analyze user sentiment towards the M-Passport application using an optimized Naïve Bayes classification model. Review data was obtained through web scraping from various digital platforms and processed using text preprocessing, TF-IDF feature extraction, N-Gram representation, and the Synthetic Minority Over-sampling Technique (SMOTE) technique to address data representativeness. The proposed model classifies user reviews into positive, neutral, and negative sentiment categories. Test results show that optimization using N-Gram and SMOTE successfully improved model performance, with accuracy increasing from 61% to 77.51%, precision from 0.75 to 0.78, recall from 0.53 to 0.78, and F1-score from 0.50 to 0.77. These results demonstrate that the combination of feature engineering and data balancing can improve text context representation and sentiment classification stability across multiple classes. Furthermore, sentiment analysis successfully identified key factors contributing to user dissatisfaction, such as technical constraints, feature limitations, and application difficulty. These results demonstrate that the proposed approach is effective in supporting data-driven evaluation to improve the quality of digital immigration services.]]>
