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.
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962 Documents
<![CDATA[User Interface Evaluation of the Business Development Center Website at UIN Syarif Hidayatullah Jakarta: A Content, Visual, and Navigation Perspective ]]>
<![CDATA[Sobri, Muhammad]]>;
<![CDATA[Subchi, Imam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5208
<![CDATA[The business development center State Islamic University Syarif Hidayatullah Jakarta (UIN Jakarta) as the main manager of the campus's business lines that functions to manage, develop, generate funding sources, underpin the development of business ideas and implement business ideas that have been designed and agreed upon by the UIN Jakarta Leadership and other stakeholders. This study aims to evaluate the business development center website UIN Jakarta from a user interface perspective. This study uses a questionnaire containing to evaluate the user interface in terms of content, visuals and navigation. Based on the questionnaire data that has been filled out by respondents, the most significant findings from each aspect. The content aspect, valued at 76.3% for information regarding the vision, mission, work programs, and the foreword of the head of the center are presented completely and easily accessible, but 63.2% complained that some parts of the service are not detailed enough. The visual aspect, valued at 63.2% for the consistent campus color identity on the background and title, but 60.5% complained that the contrast of white text on the dark gray background area is less user-friendly. While the navigation aspect, valued at 71.1% for the about, services, and gallery features are clearly visible, but 63.2% complained that there is no visual highlight for the menu being accessed. This study contributes to the development of the business development center website UIN Jakarta based on responses from participants completed the questionnaire to be more optimal and provide all information regarding the business units at UIN Jakarta as a form of promotion to be known and serve the wider community. In addition, this study presents innovations for developing a user interface evaluation framework for institution websites, particularly those related to business development center in state islamic universities.]]>
<![CDATA[Monkeypox Classification Using Convolutional Neural Networks (CNN) Pruned Residual Network-50 (ResNet-50) Architecture on Flutter Framework ]]>
<![CDATA[Priatna, Irfan]]>;
<![CDATA[Permadi, Ipung]]>;
<![CDATA[Nofiyati, Nofiyati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5232
<![CDATA[The monkeypox outbreak, which was previously only found in Africa, has now spread to other continents, including Asia, causing public concern as it occurred shortly after the COVID-19 pandemic was declared over. This disease has symptoms similar to cowpox, chickenpox, and measles, making early detection based on visual observation difficult. To address this issue, various studies have developed Deep Learning (DL)-based classification models using datasets such as WSI, MSID, MCSI, and MSLD v2, which are also utilized in this research. This study proposes a pruned ResNet-50 model using the Global MP method for pruning and QAT for quantization. These modifications not only maintain the model's performance with an accuracy of 94.44%, precision of 94.12%, recall of 94.71%, and F1-score of 94.16%, but also significantly reduce the model size to just 20.993 MB. As a result, the model can be implemented on Android devices with limited resources, enabling rapid and practical early detection of monkeypox in the field without requiring large-scale servers. Blackbox testing results show that the Flutter-based application utilizing this model performs well, potentially providing tangible support for medical personnel and the public in monitoring the spread of monkeypox in a more efficient and accessible manner.]]>
<![CDATA[Improving Lateral-Movement Intrusion Detection in Virtualized Networks using SHAP Feature Selection, SMOTE, and a Voting Ensemble Classifier ]]>
<![CDATA[Maulana, Avin]]>;
<![CDATA[Anam, Syaiful]]>;
<![CDATA[Aziz Bukhori, Hilmi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5233
<![CDATA[Modern virtualized networks, such as those using VXLAN (Virtual eXtensible LAN), generate heavy east–west traffic, which can conceal the lateral movement of attackers. Detecting such infiltration attacks is challenging due to overlay encapsulation (e.g., VXLAN) and flat subnet architectures create blind spots for traditional IDS. This study aims to evaluate a robust methodology for addressing class imbalance in intrusion detection by integrating SHAP-driven feature selection with SMOTE in a voting ensemble. We conducted an ablation study on the CICIDS2017 Thursday-WorkingHours-Afternoon-Infiltration subset, which is highly imbalanced (36 infiltration flows vs. 288,566 benign flows), varying SHAP feature sets (Top-5 vs. Top-30), classification thresholds , and SMOTE (Synthetic Minority Over-sampling Technique) balancing. The ensemble combined XGBoost, Random Forest, and Logistic Regression, and was evaluated with ROC-AUC, precision, recall, and F1-score. Results indicate that using more SHAP‑important features improves ROC‑AUC and recall, while SMOTE substantially enhances minority‑class detection. The best configuration is Top‑30 SHAP features with SMOTE at , achieved ROC‑AUC = 0.976 and F1‑score = 0.78, whereas using fewer features or omitting SMOTE significantly reduced recall and F1‑score. This synergy of interpretable feature selection and synthetic oversampling establishes a practical methodology for intrusion detection in highly imbalanced, modern virtualized environments. The novelty lies in demonstrating that SHAP + SMOTE integration yields both transparency and resilience, directly addressing encapsulation challenges in detecting stealthy lateral movement.]]>
<![CDATA[Performance Comparison and Seo Optimization Between Laravel Blade and Laravel Inertia in The Development of The Muncak.Id Website in Indonesia ]]>
<![CDATA[Tempariyawan, Maulana Hafez Ahyatara]]>;
<![CDATA[Akbar, Mohammad Irham]]>;
<![CDATA[Nofiyati, Nofiyati]]>;
<![CDATA[Waluyo, Sugeng]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5242
<![CDATA[Climbing mountains in Indonesia requires careful preparation, especially in understanding safe hiking routes and related information. A hiking route information website serves as a practical solution for hikers to access data easily and quickly. This study compares the performance and SEO optimization between Laravel Blade and Laravel Inertia in the development of the muncak.id website, which provides information on mountain hiking routes. The performance indicator tested is execution time, measured using Laravel Dusk. One of the test results for the hiking route access feature shows that Laravel Inertia Vue.js has an average execution time of 1.56 seconds, which is faster compared to Laravel Blade, which reached 3.89 seconds. In terms of SEO, both websites show good quality with no issues found. However, there are still opportunities for improvement in some areas marked as warnings and opportunities. Overall, Laravel Inertia with Vue.js proves superior in performance and consistency, as well as more effective in SEO optimization, making it the better choice for developing informative websites.]]>
<![CDATA[Sentiment Analysis of Getcontact Application Reviews on Google Play Store Using Naive Bayes Algorithm ]]>
<![CDATA[Kurniawan, Rido Dwi]]>;
<![CDATA[Yohannis, Alfa]]>;
<![CDATA[Atmojo, Wahyu Tisno]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5248
<![CDATA[In the contemporary digital era, the increasing incidence of fraud and unwanted communications has become a serious concern, driving the adoption of security apps like GetContact. This study aims to analyze public perception of the GetContact app by conducting a systematic sentiment analysis of user reviews on the Google Play Store. Using a text mining framework, 990 user reviews were collected, processed to ensure data quality, and then classified using the Naive Bayes algorithm to determine sentiment polarity. Quantitative results show a significant dominance of negative sentiment, comprising 419 reviews (42.3%), followed by positive sentiment, comprising 291 reviews (29.4%), and neutral sentiment, comprising 280 reviews (28.3%). Qualitative analysis through data visualization reveals that the primary user complaints center on basic functionality issues such as login difficulties, while positive sentiment is driven by the perception that the app is very helpful. These findings provide critical actionable insights for developers to prioritize improvements in areas of greatest user concern. This study advances sentiment analysis by demonstrating the efficacy of Naive Bayes in classifying unstructured app reviews, offering a scalable approach to evaluating user feedback in mobile app development.]]>
<![CDATA[Development of a Web-Based Management Information System for Student Creativity Program (PKM) Using Extreme Programming and Laravel Framework ]]>
<![CDATA[Rahmah, Nihayatur]]>;
<![CDATA[Hidayat, Nurul]]>;
<![CDATA[Wibowo, Dwi Kurnia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.5267
<![CDATA[This research originates from the absence of an integrated system for managing the Student Creativity Program (PKM) at the Faculty of Engineering, Universitas Jenderal Soedirman, which has caused inefficiencies in archiving, monitoring, and reporting. To address this problem, a web-based management information system was developed using the Extreme Programming (XP) methodology, selected for its flexibility, iterative process, and strong user involvement. The novelty of this study lies in the development of a system specifically designed for PKM management at the faculty level, which has not been previously available. Unlike prior studies, the system not only supports proposal submission but also integrates review, scoring, revision, and progress monitoring. The development process followed the four main stages of XP: planning, design, coding, and testing, with active user participation in each cycle. Blackbox testing confirmed that all core features functioned properly. The implementation of this system has proven to enhance efficiency, transparency, and accountability, reduce administrative workload, and contribute to informatics by demonstrating the practical application of the XP methodology in developing academic information systems.]]>
<![CDATA[Single-Image Face Recognition For Student Identification Using Facenet512 And Yolov8 In Academic Environtment With Limited Dataset ]]>
<![CDATA[Imam Muttaqin, Almas Najiib]]>;
<![CDATA[Luthfiarta, Ardytha]]>;
<![CDATA[Nugraha, Adhitya]]>;
<![CDATA[Salsabila, Pramesya Mutia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.3908
<![CDATA[Face recognition has become one of the most significant research areas in image processing and computer vision, mainly due to its wide applications in security, identity verification, and human and machine interaction. In this study, FaceNet512 and YOLOv8 models are used to overcome the challenges in face recognition with a limited dataset, which is only one formal photo per individual. The application of image augmentation to the model achieved 90% accuracy and ROC curve of 0.82, while the model without augmentation achieved 89% accuracy and ROC curve of 0.79. FaceNet512 showed superiority in producing more accurate and detailed facial representations compared to other models, such as ArcFace and FaceNet, especially in handling minimal facial variations. Meanwhile, YOLOv8 provides efficient face detection across various lighting conditions and viewing angles. The main challenge in this research is the low quality of the original image, which can reduce the accuracy of face recognition. These results show the great potential of using deep learning-based face recognition systems in the real world, especially for automatic attendance applications in academic environments.]]>
<![CDATA[Implementation of Extra Trees Classifier and Chi-Square Feature Selection for Early Detection of Liver Disease ]]>
<![CDATA[Al Ghifari, Muhammad Akmal]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Saragih, Triando Hamonangan]]>;
<![CDATA[Mazdadi, Muhammad Itqan]]>;
<![CDATA[Herteno, Rudy]]>;
<![CDATA[Rozaq, Hasri Akbar Awal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4261
<![CDATA[The imbalanced distribution of medical data poses challenges in accurately detecting liver disease, which is crucial as symptoms often remain unnoticed until advanced stages. This study examines the application of the Extra Trees Classifier algorithm and chi-square feature selection for early detection of liver disease. Compared to traditional methods like Random Forest and SVM, the Extra Trees Classifier offers enhanced computational efficiency and better handling of imbalanced datasets, while chi-square feature selection helps identify the most relevant medical indicators. The data consists of five medical variables likely to be laboratory test results from patient samples, with labels indicating classes A and B. The data is randomly divided with a ratio of 80% for each class. To address data imbalance, SMOTE technique was applied before the data was randomly split into a ratio of 80% for training and 20% for testing to ensure effective learning and testing of the model's performance. The results showed that with the help of chi-square feature selection, the Extra Trees Classifier algorithm could provide fairly accurate predictions in liver disease classification, with an accuracy of 82.6%, sensitivity of 85.5%, precision of 78.3%, and F1-Score of 81.7%. These results demonstrate significant improvement over existing methods, and the proposed approach can aid healthcare practitioners in making timely diagnostic decisions, potentially reducing mortality rates through early intervention in liver disease cases.]]>
<![CDATA[Prediction of Life Expectancy of Lung Cancer Patients After Thoracic Surgery Using Decision Tree Algorithm and Adaptive Synthetic Sampling ]]>
<![CDATA[Erdi, Muhammad]]>;
<![CDATA[Mazdadi, Muhammad Itqan]]>;
<![CDATA[Nugroho, Radityo Adi]]>;
<![CDATA[Farmadi, Andi]]>;
<![CDATA[Saragih, Triando Hamonangan]]>;
<![CDATA[Rozaq, Hasri Akbar Awal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4724
<![CDATA[This research focuses on predicting the life expectancy of lung cancer patients after undergoing thoracic surgery, using a decision tree classification algorithm (C4.5) combined with adaptive synthetic sampling to handle data imbalance. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. By applying ADASYN, the data distribution becomes more even, thus improving the performance of the C4.5 model. The results showed that combining these methods increased the prediction accuracy from 67% to 87%. In addition, the precision, recall, and f1-score for minority classes have significantly improved, which were previously difficult to identify by the model. Thus, combining the C4.5 algorithm and the ADASYN technique proved effective in dealing with the challenge of data imbalance and resulted in better prediction in the case of lung cancer. This study is expected to contribute to the field of medical classification and serve as a reference for further research on similar cases.]]>
<![CDATA[Performance Comparison of AdaBoost, LightGBM, and CatBoost for Parkinson's Disease Classification Using ADASYN Balancing ]]>
<![CDATA[Anshari, Muhammad Ridha]]>;
<![CDATA[Saragih, Triando Hamonangan]]>;
<![CDATA[Muliadi, Muliadi]]>;
<![CDATA[Kartini, Dwi]]>;
<![CDATA[Indriani, Fatma]]>;
<![CDATA[Rozaq, Hasri Akbar Awal]]>;
<![CDATA[Yıldız, Oktay]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.4726
<![CDATA[Parkinson's disease is a neurodegenerative condition identified by the decline of neurons that produce dopamine, causing motor symptoms such as tremors and muscle stiffness. Early diagnosis is challenging as there is no definitive laboratory test. This study aims to improve the accuracy of Parkinson's diagnosis using voice recordings with machine learning algorithms, such as AdaBoost, LightGBM, and CatBoost. The dataset used is Parkinson's Disease Detection from Kaggle, consisting of 195 records with 22 attributes. The data was normalized with Min-Max normalization, and class imbalance was resolved with ADASYN. Results show that ADASYN-LightGBM and ADASYN-CatBoost have the best performance with 96.92% accuracy, 97.10% precision, 96.92% recall, and 96.92% F1 score. This improvement suggests that combining boosting methods and data balancing techniques can improve the accuracy of Parkinson's diagnosis. These results demonstrate the effectiveness of ADASYN in addressing data imbalance and improving the performance of boosting algorithms for medical classification problems. The findings contribute to the development of intelligent diagnostic systems in the field of medical informatics and computer science. These findings are essential for developing more accurate and efficient diagnostic tools, supporting early diagnosis and better management of Parkinson's disease.]]>
