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[Enhanced Lung Cancer Detection Using ANN with Random Oversampling, RFE-Based Feature Selection, and GridSearchCV Hyperparameter Tuning ]]>
<![CDATA[Nurwafiqah, Nurwafiqah]]>;
<![CDATA[Al Fiqran, M. Yudi]]>;
<![CDATA[Puteri, Annisa Nurul]]>;
<![CDATA[Arafah, Muhammad]]>;
<![CDATA[Maslihatin, Tatik]]>;
<![CDATA[Sumardin, A.]]>
<![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.2026.7.2.5391
<![CDATA[Amid the most predominant mortality factors on a global scale, Lung cancer constitutes one of the most significant oncological burdens, chiefly because most patients receive a diagnosis only at later stages. The limitations of conventional diagnostic approaches underscore the urgent need for artificial intelligence–based detection systems that can improve both diagnostic accuracy and efficiency. This study aims to develop a lung cancer prediction model using an Artificial Neural Network (ANN) optimized through an integrated strategy that includes data preprocessing, class balancing via Random Oversampling (ROS), feature selection using Recursive Feature Elimination (RFE), and hyperparameter tuning with Grid Search. The evaluation of model effectiveness employs accuracy, precision, recall, F1-score, along with a confusion matrix. Experimental results demonstrate an accuracy of 98%, with average precision, recall, and F1-score values of 0.95. Statistical validation using McNemar’s test confirms a significant performance improvement over the baseline model (χ² = 18.05, p < 0.001), accompanied by a large effect size (Cohen’s h = 0.82). Furthermore, the model exhibits balanced performance in identifying both lung cancer and non-cancer cases, reflecting the effectiveness of the data balancing and feature selection strategies. These findings suggest that the optimized ANN model has strong potential as a foundation for a medical decision support system for early lung cancer detection, contributing to more reliable diagnoses and more accurate clinical decision-making.]]>
<![CDATA[A Comparative Study of Generalized Linear Mixed Model and Mixed Effects Random Forest for Analyzing Data with Outliers ]]>
<![CDATA[Arianti, Reza]]>;
<![CDATA[Notodiputro, Khairil Anwar]]>;
<![CDATA[Angraini, Yenni]]>
<![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.2026.7.2.5407
<![CDATA[This study compares MERF and GLMM-NB in analyzing hierarchical data and focusing on the role of residual outliers and the application of winsorization. A two-stage analytical pipeline was implemented: (1) winsorization to reduce extreme residual values, and (2) model training using MERF and GLMM-NB. The dataset comes from the 2021 National Socio-Economic Survey (Susenas) in West Java Province, measuring tobacco consumption intensity. Two statistical approaches are compared, MERF and GLMM with a Negative Binomial distribution (GLMM-NB). Models were trained under two conditions: without winsorization (WIN0) and with two-sided 5% winsorization (WIN5). Winsorization was applied to the training data, and the test data were adjusted using thresholds from the training set. Model performance was assessed using Root Mean Squared Error (RMSE) and the train-test ratio. Under WIN0, GLMM recorded an RMSE of 49.65 for training and 42.27 for testing, while MERF achieved 35.96 and 39.94, respectively. After WIN5, GLMM showed a larger error reduction, with RMSE values of 34.90 (train) and 30.20 (test), while MERF dropped to 26.63 (train) and 28.64 (test). These results indicate that MERF provides higher predictive accuracy, whereas GLMM benefits more from winsorization. Household expenditure, employment status, age, and gender consistently emerged as key variables linked to tobacco consumption intensity. This study is the first to compare MERF and GLMM-NB with winsorization using Indonesia’s hierarchical data. The analytical framework helps inform public health policies aligned with SDG 3: Good Health and Well-being, particularly in reducing tobacco-related health risks.]]>
<![CDATA[Analysis of Public Sentiment Indonesia’s Personal Data Protection Law: A Comparison of SVM and IndoBERT on X Platform ]]>
<![CDATA[Kurniawati, Yulia]]>;
<![CDATA[Hamid, Ricky Bahari]]>;
<![CDATA[Sensuse, Dana Indra]]>;
<![CDATA[Lusa, Sofian]]>;
<![CDATA[Putro, Prasetyo Adi Wibowo]]>;
<![CDATA[Indriasari, Sofiyanti]]>
<![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.2026.7.2.5415
<![CDATA[The high number of data misuses, thefts, and leaks led to the enactment of the PDP Law, which regulates the rights and obligations of data owners and electronic system providers. The purpose of this study is to examine the public’s response to the implementation of the law through the X platform, using tweet harvest as a scraping tool, and to evaluate model performance through a comparative approach between SVM and BERT. The feature extraction used in this study is TF-IDF for SVM and BERT with IndoBERT. The accuracy results indicate that BERT is better with an accuracy of 86% compared to SVM with a training and test data ratio of 85:15. This advantage is because BERT can understand linguistic context that SVM cannot. On the other hand, SVM has advantages in computational efficiency and faster processing, making it a suitable choice in situations with limited computational resources. The sentiment analysis result revealed that data protection, digital footprint and the institution's role were the most frequently discussed topics. Furthermore, periodic or real-time evaluations can be conducted on the public's response to the PDP Law to ensure it remains aligned and relevant to technological developments and societal needs.]]>
<![CDATA[Optimizing E-commerce Personalization through Hybrid Decision Tree–Nearest Neighbor Recommendation Integration ]]>
<![CDATA[Syaifuddin, Akhmad]]>;
<![CDATA[Saptono, Ristu]]>;
<![CDATA[Rohmadi, Arif]]>;
<![CDATA[Widoyono, Bambang]]>;
<![CDATA[Hendrasuryawan, Brilyan]]>
<![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.2026.7.2.5418
<![CDATA[Single-method recommendation systems face critical limitations: content-based filtering suffers from overspecialization while collaborative filtering struggles with data sparsity and cold-start problems. This research introduces an innovative hybrid recommendation framework that synthesizes Content-Based Filtering (CBF) utilizing Decision Trees with Collaborative Filtering (CF) employing Nearest Neighbor algorithms. Our approach addresses the inherent limitations of singular recommendation methodologies by integrating product attribute analysis with collective user behavior patterns. We conducted comprehensive evaluations using a shopping behavior dataset comprising 3,900 consumer records with diverse demographic and product interaction data. Our findings reveal that an asymmetric hybrid configuration—weighted at 70% for CBF and 30% for CF—achieves optimal performance with a Root Mean Square Error (RMSE) of 0.7422. The system incorporates an interactive user interface that facilitates a natural shopping experience: browsing available items, receiving personalized recommendations, and providing explicit feedback on suggested products. Through feature importance analysis, we identified key product attributes that significantly influence recommendation quality, including size variations and specific color preferences. The hybrid approach demonstrates 42% greater category diversity and 37% more recommendation diversity compared to pure content-based filtering, while maintaining superior accuracy metrics. Our research contributes to understanding optimal hybrid architectures and provides practical insights for implementing effective personalization strategies in real-world e-commerce environments.]]>
<![CDATA[Clustering And Classification Of Toddler Stunting Risk Using K-Means And Naive Bayes: A Case Study At Kembaran 1 Community Health Center ]]>
<![CDATA[Fitriya Maharani, Lulu Amnah]]>;
<![CDATA[Purwadi, Purwadi]]>;
<![CDATA[Ummul Hidayah, Debby]]>
<![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.2026.7.2.5420
<![CDATA[Stunting continues to be a significant public health concern in Indonesia, with a frequency of 17.25% at Kembaran 1 Public Health Center, highlighting ongoing difficulties in early childhood nutrition and growth surveillance. This work seeks to assess and forecast stunting risk in toddlers by employing K-Means clustering and Naive Bayes classification to enhance early detection precision. The K-Means method was utilized on 1,168 toddler growth records to categorize stunting features, whereas the Davies–Bouldin Index (DBI) was employed to evaluate cluster quality. The ideal cluster was attained at k = 8, yielding a DBI value of 4.353, indicating compact and distinctly differentiated clusters. The Naive Bayes classifier subsequently predicted stunting potential with an accuracy of 93.56%, accurately categorizing 218 out of 233 test examples, yielding precision, recall, and F1-score values for the “short” class of 97.41%, 94.95%, and 96.18%, respectively. The findings indicate that the hybrid model successfully combines unsupervised and supervised learning, improving stunting prediction accuracy and cluster interpretability. The research provides a data-centric framework for localized stunting surveillance, aiding community health centers in formulating targeted early treatments and mitigating long-term developmental hazards.]]>
<![CDATA[Hybrid LSTM-CNN-GRU Deep Learning for Integrating IoT and Social Media Sentiment Analysis in Indonesian Higher Education Reputation Management ]]>
<![CDATA[Murti Prabowo, Kresno]]>;
<![CDATA[Nidauddin, Ikbal]]>;
<![CDATA[Andiono, Endro]]>
<![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.2026.7.2.5426
<![CDATA[Higher education institutions in Indonesia face critical challenges in managing digital reputation. Despite 85% of prospective students using social media for university research, only 23% of institutions have integrated monitoring systems, resulting in 67% experiencing undetected reputation crises with substantial financial losses. This research proposes a novel framework integrating IoT campus data with social media sentiment analysis using hybrid deep learning architecture. The system employs LSTM-CNN networks with multi-head attention mechanisms for sentiment classification and GRU networks for reputation trend prediction, enhanced with data fusion strategy. Data collected from 428 IoT sensors and 3.2 million social media posts across five Indonesian universities over six months underwent advanced preprocessing including Indonesian-specific slang normalization and Sastrawi stemming. The hybrid LSTM-CNN architecture with attention achieved 90.3% sentiment classification accuracy (Macro-F1: 0.903), significantly outperforming baseline methods including Naive Bayes (76.2%), traditional LSTM (84.5%), and IndoBERT (87.1%). IoT integration contributed 18.2% RMSE improvement in trend prediction (R²: 0.874). The early warning system predicted reputation crises with 85.7% precision and 82.4% recall, providing critical intervention windows averaging 14.3 days before incidents. The real-time dashboard achieved 98.5% availability with sub-3-second response time and excellent usability (SUS score: 82.4). This research contributes: (1) novel IoT-sentiment integration framework with demonstrated effectiveness, (2) context-aware deep learning architecture optimized for Indonesian language achieving state-of-the-art performance, (3) validated early warning system enabling proactive reputation management, and (4) practical implementation with significant improvements over existing methods, advancing educational data analytics and AI-based decision support systems.]]>
<![CDATA[Optimizing Heart Disease Classification Using C4.5, Random Forest, and XGBoost with ANOVA, Chi-Square, and AdaBoost ]]>
<![CDATA[Pratama, Andika]]>;
<![CDATA[Assegaff, Setiawan]]>;
<![CDATA[Jasmir, Jasmir]]>;
<![CDATA[Nurhadi, Nurhadi]]>
<![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.2026.7.2.5430
<![CDATA[Heart disease remains one of the leading causes of mortality worldwide, underscoring the need for accurate and scalable prediction models within clinical informatics. This study proposes a leakage-safe machine learning pipeline combining stratified splitting, SMOTE-based imbalance handling, and in-fold feature selection using ANOVA, Chi-Square, and AdaBoost-assisted ranking to enhance classification performance on a large heart-disease dataset consisting of 10,000 samples and 21 attributes. Three widely used algorithms, C4.5, Random Forest, and XGBoost, were evaluated to determine the optimal model-feature selection configuration for structured medical data. The results demonstrate that feature relevance contributes more significantly to predictive performance than increasing model complexity, with Random Forest achieving the highest accuracy, precision, recall, and F1-Score at 98.43% when combined with Chi-Square or ANOVA feature selection. C4.5 showed the greatest relative improvement, rising from 76.52% to 97.57% using AdaBoost-assisted selection, while XGBoost improved from 66.32% to 94.88% after statistical filtering. The dominant features identified such as CRP, BMI, blood pressure, fasting glucose, LDL, triglycerides, and homocysteine align with well-established cardiovascular biomarkers, supporting clinical validity. This research provides an important contribution to computer science by demonstrating an efficient and scalable hybrid FS-boosting framework capable of reducing unnecessary model complexity, improving generalization, and supporting low-latency deployment in clinical decision-support systems. The findings highlight the potential of structured-data machine learning to strengthen digital health diagnostics in resource-limited environments.]]>
<![CDATA[Enhancement Of The C4.5 Decision Tree Algorithm With Anova For Predicting Academic Achievement Of Students At Smpn.16 Kota Jambi ]]>
<![CDATA[Osviarni, Rice]]>;
<![CDATA[Assegaff, Setiawan]]>;
<![CDATA[Jasmir, Jasmir]]>;
<![CDATA[Nurhadi, Nurhadi]]>
<![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.2026.7.2.5431
<![CDATA[This study aims to improve the accuracy of predicting student academic achievement by integrating the Analysis of Variance (ANOVA) method with the C4.5 Decision Tree algorithm. In the context of information systems, this research holds significant importance for the development of more reliable Decision Support Systems (DSS) or early warning systems in school environments. The research was conducted at SMPN 16 Jambi City using secondary data from three academic years (2022/2023-2024/2025) covering academic variables, attendance, and parental income. The main issue addressed was the limitations of the C4.5 algorithm in handling irrelevant features and unbalanced data, which, at the system implementation level, can lead to inaccurate recommendations or alerts.This research method employed a data mining approach with stages including data cleaning, numeric conversion, missing value imputation, formation of derived variables, and categorization of the target variable "Achievement." The initial C4.5 model produced 72.81% accuracy on the training data and 69.71% accuracy on cross-validation. After feature selection using ANOVA, one insignificant variable was removed, resulting in a hybrid C4.5+ANOVA model with nine key features. Test results showed an increase in accuracy to 80.44% on the training data and 73.66% on the cross-validation data, representing an improvement of 7.63 and 3.95 percentage points, respectively.This improvement in model performance directly translates to an enhancement in the quality of the information system's output, yielding more reliable reports and predictions for teachers and school management.]]>
<![CDATA[A Locally Grounded Retrieval-Augmented LLM-Based Chatbot for Bilingual Stunting Prevention Consultation among Health Cadres in Indonesia ]]>
<![CDATA[Tanwir, Tanwir]]>;
<![CDATA[Hidjah, Khasnur]]>;
<![CDATA[Susilowati, Dyah]]>;
<![CDATA[Anggrawan, Anthony]]>;
<![CDATA[Sulistianingsih, Neny]]>
<![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.2026.7.2.5459
<![CDATA[Stunting remains a major public health challenge in Indonesia, affecting 21.6% of children under five nationally and 18.34% in Nusa Tenggara Barat (NTB), which strains the capacity of health cadres to deliver timely and accurate nutrition education. This study aims to develop a consultation chatbot by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to provide context-aware stunting prevention guidance. A total of 45 journal articles and 7 books were curated to construct 7,642 question–answer pairs using a RAG-based pipeline. Text preprocessing involved segmentation, embedding, and Byte Pair Encoding tokenization, followed by fine-tuning a LLaMA 3 model on an NVIDIA L4 GPU. Model performance was evaluated using ROUGE and BERTScore metrics, complemented by a small pilot usability assessment. The RAG-integrated model achieved a ROUGE-1 score of 81.03% and a BERTScore F1 of 93.48%, consistently outperforming baseline models. These findings demonstrate the potential of RAG-enhanced LLMs to support scalable and accessible health informatics solutions for empowering health cadres in resource-limited and rural settings.]]>
<![CDATA[Performance Evaluation of Gradient Boosting Techniques for Predicting Customer Purchase Decisions ]]>
<![CDATA[Arini, Florentina Yuni]]>;
<![CDATA[Djuanda, Lyon Ambrosio]]>;
<![CDATA[Kristianto, Ananda Hisma Putra]]>;
<![CDATA[Tiadah, Muthia Nis]]>;
<![CDATA[Wicaksono, Aufa Putra]]>;
<![CDATA[Putra, Fatih Akbar Alim]]>
<![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.2026.7.2.5461
<![CDATA[Customer purchase prediction remains a critical challenge in e-commerce and retail analytics, with significant implications for marketing strategies and business revenue. This research provides a detailed comparative evaluation of advanced gradient boosting techniques XGBoost, LightGBM, and CatBoost to predict customer purchasing behavior using review trends and demographic factors. The study employed a dataset of 100 customer records with attributes such as age, gender, review quality, and education level. Through systematic feature engineering, including age group categorization and categorical feature combinations, as well as addressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), all three models were trained and evaluated using default hyperparameters with optimal settings. The experimental results show that CatBoost achieved the best performance, with 78.26% accuracy, 0.8011 precision, 0.7826 recall, and a 0.7775 F1-score, outperforming LightGBM (73.91% accuracy) and XGBoost (60.87% accuracy). The evaluation includes confusion matrix analysis, precision–recall metrics, and visual comparisons across all performance dimensions. These findings provide valuable insights for practitioners selecting appropriate machine learning algorithms for customer purchase prediction tasks, particularly in scenarios involving limited datasets and categorical features. This research contributes to the growing body of literature on the use of gradient boosting techniques for predicting consumer behavior and offers important practical implications for e-commerce applications. These findings offer important contributions to machine learning applications in customer behavior prediction.]]>
