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[Brain Tumor Segmentation From MRI Images Using MLU-Net with Residual Connections ]]>
<![CDATA[Rompisa, Eric Timothy]]>;
<![CDATA[Kusuma, Gede Putra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4742
<![CDATA[Brain tumor segmentation plays an important role in medical imaging in assisting diagnosis and treatment planning. Although advances in deep learning such as Unet already perform image segmentation, many challenges exist in segmenting brain tumors with tumor spread boundaries. This paper proposes a model that combines CNN and MLP (MLU-Net) techniques enhanced by the addition of residual connections to improve segmentation accuracy called ResMLU-Net. This architecture combines 2D covolution layers, block MLP and residual connections to process MRI images with the dataset used is BraTS 2021. The residaul connection helps reduce gradient degradation which ensures smooth information flow and better feature learning. The performance of ResMLU-Net will be evaluated using Dice and IoU metrics and will also be compared with several models such as Unet, ResUnet and MLU-Net. The experimental scores obtained from ResMLU-Net for segmenting brain tumors are 83.43% for IoU and 89.94% for Dice. These results show that adding residual connections can improve the accuracy in segmenting brain tumors which can be seen that there is an increase in the Dice and Iou scores. The proposed ResMLU-Net model is a valuable contribution to medical imaging and health informatics. With its provision of a standard and computationally viable solution to brain tumor segmentation, it offers incorporation into Computer-Aided Diagnosis (CAD) systems and support to clinical decision-making protocols.]]>
<![CDATA[Comparing BERTBase, DistilBERT and RoBERTa in Sentiment Analysis for Disaster Response ]]>
<![CDATA[Firmansyah, Hafiz Budi]]>;
<![CDATA[Afriansyah, Aidil]]>;
<![CDATA[Lorini, Valerio]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4766
<![CDATA[Social media platforms are vital for real-time communication during disasters, providing insights into public emotions and urgent needs. This study evaluates the performance of three transformer-based models—BERTBase, DistilBERT, and RoBERTa—for sentiment analysis on disaster-related social media data. Using a multilingual dataset sourced from the Social Media for Disaster Risk Management (SMDRM) platform, the models were assessed on classification metrics including accuracy, precision, recall, and weighted F1-score. The results show that RoBERTa consistently outperforms the others in classification performance, while DistilBERT offers superior computational efficiency. The analysis highlights the trade-offs between model accuracy and runtime, emphasizing RoBERTa's suitability for scenarios prioritizing accuracy, and DistilBERT's potential in time-sensitive or resource-constrained applications. These findings support the integration of sentiment analysis into disaster response systems to enhance situational awareness and decision-making.]]>
<![CDATA[Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy ]]>
<![CDATA[Iskandar, Muhammad Yashlan]]>;
<![CDATA[Nugroho, Handoyo Widi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4877
<![CDATA[Early detection of lung cancer is essential for improving treatment outcomes and patient survival rates. This paper presents a comparative evaluation of two classification algorithms: Decision Tree and Random Forest, focusing on both predictive performance and computational efficiency. The models were tested using 10-fold cross-validation to ensure robustness. Both algorithms achieved the same accuracy of 93.3%. However, Random Forest slightly outperformed Decision Tree in recall (88.8% vs. 87.9%), F1-score (92.2% vs. 92.1%), and AUC (0.94 vs. 0.91), while Decision Tree obtained higher precision (97% vs. 95.9%). In terms of computational efficiency, Decision Tree demonstrated faster training and testing times, lower memory usage, and reduced energy consumption compared to Random Forest. The results reveal a clear trade-off between prediction quality and resource usage, highlighting the importance of selecting algorithms not only for their accuracy but also for their practicality in real-world healthcare scenarios. This comprehensive evaluation provides valuable insights for developing intelligent decision support systems that are both effective and resource-efficient, especially in environments with limited computing capacity. These findings contribute to the advancement of resource-aware intelligent systems in the field of medical informatics.]]>
<![CDATA[Hyperparameter Optimization Of IndoBERT Using Grid Search, Random Search, And Bayesian Optimization In Sentiment Analysis Of E-Government Application Reviews ]]>
<![CDATA[Iskoko, Angga]]>;
<![CDATA[Tahyudin, Imam]]>;
<![CDATA[Purwadi, Purwadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4897
<![CDATA[User reviews on Google Play Store reflect satisfaction and expectations regarding digital services, including E-Government applications. This study aims to optimize IndoBERT performance in sentiment classification through fine-tuning and hyperparameter exploration using three methods: Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on Sinaga Mobile app reviews, evaluated using accuracy, precision, recall, F1-score, learning curve, and confusion matrix. The results show that Grid Search with a learning rate of 5e-5 and a batch size of 16 provides the best results, with an accuracy of 90.55%, precision of 91.16%, recall of 90.55%, and F1-score of 89.75%. The learning curve indicates stable training without overfitting. This study provides practical contributions as a guide for improving IndoBERT in Indonesian sentiment analysis and as a foundation for developing NLP-based review monitoring systems to enhance public digital services.]]>
<![CDATA[Predicting Smartphone Addiction Levels with K-Nearest Neighbors Using User Behavior Patterns ]]>
<![CDATA[Wayahdi, M. Rhifky]]>;
<![CDATA[Ruziq, Fahmi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4905
<![CDATA[Smartphones have become an integral part of everyday life, but their ever-increasing popularity has raised growing global concerns about excessive use (nomophobia), which impacts quality of life, mental health, and academic performance. Existing research often relies on subjective questionnaires, limiting scalability and objectivity. This study addresses this gap by developing a machine learning model to predict smartphone addiction levels through an objective analysis of user behavior patterns. This research evaluates the effectiveness of the K-Nearest Neighbor (KNN) algorithm, identifies the most influential behavioral features, and assesses the model's classification performance. Using a dataset of 3,300 user behavior entries with 11 features, a waterfall-based framework was employed for data preprocessing, model design, and evaluation. The KNN model achieved 95% accuracy in classifying addiction levels. Permutation Feature Importance analysis confirmed ‘App Usage Time’ and ‘Battery Drain’ as the two most influential predictive features. This study demonstrates that KNN is a powerful and viable method for objectively classifying smartphone addiction. The findings provide a strong foundation for developing scalable, AI-driven early detection and intervention systems, offering significant contributions to the fields of computer science and digital well-being.]]>
<![CDATA[Comparison of IndoNanoT5 and IndoGPT for Advancing Indonesian Text Formalization in Low-Resource Settings ]]>
<![CDATA[Firdausillah, Fahri]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Nugraha, Adhitya]]>;
<![CDATA[Dewi, Ika Novita]]>;
<![CDATA[Hafiizhudin, Lutfi Azis]]>;
<![CDATA[Mumtaz, Najma Amira]]>;
<![CDATA[Syarifah, Ulima Muna]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4935
<![CDATA[The rapid growth of digital communication in Indonesia has led to a distinct informal linguistic style that poses significant challenges for Natural Language Processing (NLP) systems trained on formal text. This discrepancy often degrades the performance of downstream tasks like machine translation and sentiment analysis. This study aims to provide the first systematic comparison of IndoNanoT5 (encoder-decoder) and IndoGPT (decoder-only) architectures for Indonesian informal-to-formal text style transfer. We conduct comprehensive experiments using the STIF-INDONESIA dataset through rigorous hyperparameter optimization, multiple evaluation metrics, and statistical significance testing. The results demonstrate clear superiority of the encoder-decoder architecture, with IndoNanoT5-base achieving a peak BLEU score of 55.99, significantly outperforming IndoGPT's highest score of 51.13 by 4.86 points—a statistically significant improvement (p<0.001) with large effect size (Cohen's d = 0.847). This establishes new performance benchmarks with 28.49 BLEU points improvement over previous methods, representing a 103.6% relative gain. Architectural analysis reveals that bidirectional context processing, explicit input-output separation, and cross-attention mechanisms provide critical advantages for handling Indonesian morphological complexity. Computational efficiency analysis shows important trade-offs between inference speed and output quality. This research advances Indonesian text normalization capabilities and provides empirical evidence for architectural selection in sequence-to-sequence tasks for morphologically rich, low-resource languages.]]>
<![CDATA[Analysis of Technology Adoption Factors in Learning among Vocational Students using UTAUT2 Model ]]>
<![CDATA[Harimanto, Bambang]]>;
<![CDATA[Berlilana, Berlilana]]>;
<![CDATA[Barkah, Azhari Shouni]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4940
<![CDATA[Technology acceptance in vocational education is a key factor in supporting the effectiveness of teaching and learning processes in the digital era. This study aims to analyze the factors influencing technology acceptance among students of the Computer and Network Engineering (TKJ) Department at SMK Ma'arif 1 Kroya using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model includes the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, Behavioral Intention, and Actual Usage. The results reveal that five key variables—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, and Price Value—significantly influence Behavioral Intention, while Habit, Facilitating Conditions, and Behavioral Intention directly affect Actual Usage. All constructs in the model meet validity and reliability criteria, and no multicollinearity was detected (VIF < 3.3). The coefficient of determination (R²) values of 0.612 for Behavioral Intention and 0.673 for Actual Usage indicate strong predictive power of the model. These findings confirm the relevance of the UTAUT2 framework for understanding and enhancing technology acceptance in vocational education settings and provide valuable insights for improving technology integration in technical learning environments.]]>
<![CDATA[Stacked Random Forest-LightGBM for Web Attack Classification ]]>
<![CDATA[Pradana, Fadli Dony]]>;
<![CDATA[Farikhin, Farikhin]]>;
<![CDATA[Warsito , Budi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4950
<![CDATA[The rapid expansion of web services in the digital era has intensified exposure to increasingly complex and imbalanced cyber threats. This study proposes a stacking hybrid ensemble framework for web attack classification, integrating Random Forest as the base learner and LightGBM as the meta-learner, enhanced by the SMOTE technique for data balancing. The Web Attack subset of the CICIDS-2017 dataset serves as a case study, with a focus on detecting minority attacks such as SQL Injection, XSS, and Brute Force. The preprocessing pipeline includes data cleaning, removal of irrelevant features, normalization, extreme value imputation, and ANOVA F-test-based feature selection. Evaluation results indicate that the proposed model outperforms baseline models in both multiclass classification (98.7% accuracy, 0.634 macro F1-score) and binary classification (99.41% accuracy, 99.47% F1-score), while maintaining high sensitivity to minority classes. These results contribute to informatics and cybersecurity scholarship through a generalizable stacking baseline and well-specified evaluation procedures for web-attack detection, facilitating replicability, fair comparison, and dataset-agnostic insights.]]>
<![CDATA[Enhancing Customer Purchase Behavior Prediction Using PSO-Tuned Ensemble Machine Learning Models ]]>
<![CDATA[Kafilla, Princess Iqlima]]>;
<![CDATA[Utomo, Fandy Setyo]]>;
<![CDATA[Karyono, Giat]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4952
<![CDATA[Predicting customer purchase behavior remains a significant challenge in e-commerce and marketing analytics due to its complex and nonlinear patterns. This study introduces a machine learning framework that integrates ensemble learning models with Particle Swarm Optimization (PSO) for hyperparameter tuning to improve classification accuracy and class discrimination. Several ensemble algorithms, including CatBoost, XGBoost, LightGBM, AdaBoost, and Gradient Boosting, were compared against a baseline Logistic Regression model, both with default and PSO-optimized configurations. Experiments on a real-world e-commerce dataset containing behavioral and demographic variables showed that ensemble methods substantially outperformed traditional models across accuracy, F1-score, and ROC AUC metrics. Notably, the PSO-tuned Gradient Boosting model achieved the highest ROC AUC of 0.9547, improving the AUC by approximately 0.0076 compared to its default configuration, while CatBoost obtained the highest overall accuracy and F1-score. PSO optimization was especially effective in enhancing simpler models such as Logistic Regression but showed marginal gains and some convergence instability in more complex ensemble models. Feature importance analyses consistently identified variables such as time spent on the website, discounts availed, age, and income as key drivers of purchase intent. These findings demonstrate the benefit of combining ensemble learning with metaheuristic optimization, offering actionable insights for developing robust, data-driven marketing strategies.]]>
<![CDATA[Bayesian Optimized Pretrained CNNs for Mango Leaf Disease Classification: A Comparative Study ]]>
<![CDATA[Rahayu, Sri]]>;
<![CDATA[Romdoni, Sayyid Faruk]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.4967
<![CDATA[Mango leaf diseases pose a major threat to crop productivity, causing significant economic losses for farmers. Accurate and early detection is essential, yet manual diagnosis remains subjective and inefficient. This study aims to evaluate and compare the performance of five pretrained Convolutional Neural Network (CNN) architectures—DenseNet121, ResNet50V2, MobileNetV3 Small, MobileNetV3 Large, and InceptionV3—by systematically optimizing their hyperparameters to identify the most effective model for mango leaf disease classification. The public MangoLeafBD dataset, containing 4,000 images from eight balanced classes, was used. Bayesian Optimization was applied to fine-tune each model, and their performances were assessed before and after optimization. Results show that optimization substantially improved all models, with MobileNetV3 Large achieving the highest accuracy of 100% on the test set, followed by DenseNet121 (99.75%), ResNet50V2 (99.63%), MobileNetV3 Small (99.50%), and InceptionV3 (98.50%). The findings highlight that a well-tuned lightweight model can outperform more complex architectures, offering a practical and efficient solution for developing mobile-based diagnostic tools to support precision agriculture in resource-constrained settings.]]>
