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[Securing Medical Images Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for Image Steganography ]]>
<![CDATA[Pramudya, Elkaf Rahmawan]]>;
<![CDATA[Handoko, L. Budi]]>;
<![CDATA[Harjo, Budi]]>;
<![CDATA[Sani, Ramadhan Rakhmat]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Shidik, Guruh Fajar]]>;
<![CDATA[Andono, Pulung Nurtantio]]>;
<![CDATA[Sarker, Md. Kamruzzaman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4426
<![CDATA[Steganography is a technique for embedding secret information into digital media, such as medical images, without significantly affecting their visual quality. The primary challenge in medical image steganography is preserving the quality of the cover image while ensuring robustness against distortions such as compression or data manipulation attacks, which may impact diagnostic accuracy. This study proposes an enhanced steganographic method based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to improve the security and robustness of medical image embedding. DWT decomposes the medical image into four frequency sub-bands (LL, LH, HL, HH), while SVD is applied to embed the secret image while maintaining essential medical features. Experimental results show that the proposed method achieves a PSNR value of up to 78 dB and an SSIM value approaching 1, indicating that the stego image quality is nearly identical to the original cover image. Compared to previous DCT-SVD and IWT-SVD-based approaches, the DWT-SVD method offers superior robustness and imperceptibility, particularly in preserving image quality in complex-textured medical images. This method contributes to enhancing data security in telemedicine and AI-based medical imaging applications by ensuring that sensitive medical data remains protected while preserving image integrity for diagnostic use.]]>
<![CDATA[Modification Of Yolov11 Nano And Small Architecture For Improved Accuracy In Motorcycle Riders Face Recognition Based On Eye ]]>
<![CDATA[Ardiansyah, Randy]]>;
<![CDATA[Wirarama WW, I Gde Putu]]>;
<![CDATA[Husodo, Ario Yudo]]>
<![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.2025.6.5.4535
<![CDATA[Face recognition still faces challenges in identifying faces covered by masks and helmets with open visors, such as those commonly used by motorcyclists, especially when entering parking areas. To improve the accuracy of face recognition in these conditions, this study proposes nano and small versions of the YOLOv11 modification, which is an internal version. Modifications are made to the neck section and the DySample module is added in place of the UpSample module to improve the model's capabilities. Experiments were conducted using a self-generated dataset consisting of 50 classes. The results show that the modified nano version achieves 99.3% accuracy at the same mAP50 as YOLOv11n and YOLOv12n. At mAP50-95, it shows a 1.6% accuracy improvement compared to YOLOv11n and YOLOv12n with 75% accuracy. Meanwhile, the modified small version achieved an accuracy improvement of 1.3% and 1.2% compared to YOLOv11n and YOLOv12n, respectively, reaching 76.1% on mAP50-95, although the accuracy on mAP50 remained the same as YOLOv11n and 0.1% superior to YOLOv12n. However, recall and precision did not show significant improvement in both as well as the increase in model parameters. However, the model is still in the nano and small versions. Therefore, the model can be implemented on edge devices. This research is important for the field of computer vision, especially in the context of face recognition. The contribution of this research is the improvement of the accuracy of the mAP50-95 metric in eye-based face recognition, which is relevant for intelligent security systems with limited resources.]]>
<![CDATA[Design and Implementation of a Social CRM-Based Website Framework for Enhanced Information Services ]]>
<![CDATA[Ibrahim, Ali]]>;
<![CDATA[Oklilas, Ahmad Fali]]>;
<![CDATA[Novianti, Hardini]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4660
<![CDATA[The development of website technology has encouraged digital transformation in various sectors, especially in the provision of information services. The website not only acts as a medium for delivering information, but also as a means of interaction between service providers and users. However, there are still many institutions that have not optimized the potential of the website to the fullest to build sustainable relationships with users. The result of this research is the design of a Social CRM-Based Website Framework for Enhanced Information Services. The Social CRM model enables two-way interaction, real-time feedback monitoring, and digital community management that supports openness and active user participation. The application of this model is directed at optimizing information services through improving the quality of communication, responsiveness, and personalization of content based on user needs. The results show that the majority of users want a website that is easy to access, informative, and integrated with social media, especially Facebook. The number of respondents in this study was 150 respondents, and the application of the Quicksort Algorithm for data analysis. This study proposes a Social CRM-based website framework to enhance user engagement in academic information services. Using the User-Centered Design (UCD) method, the system was developed and evaluated through a System Usability Scale (SUS) with 106 users. The results showed a usability score of 72 (Good). Integration with Facebook showed positive engagement, although data privacy concerns were noted.]]>
<![CDATA[Integration of K-Means Clustering and Elbow Method for Mapping Baccaurea spp. Distribution to Support Agroindustrial Development in West Sulawesi ]]>
<![CDATA[Irawan, Heri]]>;
<![CDATA[Ihsan Fawzi, Muhammad]]>;
<![CDATA[Hidayat, Syarif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.4671
<![CDATA[Baccaurea spp. is a type of wild plant with potential value for sustainable agroindustrial development. This study aims to map and segment regions in West Sulawesi based on the habitat suitability for Baccaurea spp. using K-Means Clustering integrated with the Elbow Method. Field data were collected from 25 villages in Mamasa and Mamuju districts, involving five parameters: land area, production estimate, altitude, humidity, and average temperature. Based on the results of the exploration, 3 species of Baccaurea spp. have been found, namely Baccaurea Lanceolata, Baccaurea Costulata, and Baccaurea Racemosa. The analysis yielded three clusters, with Cluster 1 being identified as the top priority for agroindustrial development due to its high productivity and optimal land conditions. The findings provide a data-driven foundation for policymakers and industries to support the sustainable cultivation of Baccaurea spp. in Indonesia. This research contributes to informatics-based decision-making in agroindustry development and regional planning.]]>
<![CDATA[Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest ]]>
<![CDATA[Wardana, Muhammad Difha]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Indriani, Fatma]]>;
<![CDATA[Nugrahadi, Dodon Turianto]]>;
<![CDATA[Saputro, Setyo Wahyu]]>;
<![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.2025.6.5.4696
<![CDATA[Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.]]>
<![CDATA[Fine-Tuned Transfer Learning with InceptionV3 for Automated Detection of Grapevine Leaf Diseases ]]>
<![CDATA[Sholihin, Miftahus]]>;
<![CDATA[Zamroni, Moh. Rosidi]]>;
<![CDATA[Anifah, Lilik]]>;
<![CDATA[Fudzee, Mohd Farhan Md]]>;
<![CDATA[Ismail, Mohd Norasri]]>
<![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.2025.6.5.4717
<![CDATA[Grape leaf diseases pose a major threat to vineyard productivity, making early and accurate detection essential for modern grape plantation management. Despite advancements in computer vision, challenges remain in differentiating diseases with visually similar symptoms. This study addresses that gap by developing a grape leaf disease classification system using a fine-tuned deep learning model based on the InceptionV3 architecture. Three training scenarios were conducted with fixed parameters batch size of 32 and learning rate of 0.001while varying the number of epochs (25, 50, and 75). Results showed a consistent improvement in classification accuracy with increased training epochs, reaching 98.64%, 98.78%, and 99.09% respectively. Confusion matrix analysis revealed that most misclassifications occurred between visually similar diseases such as Black Rot and ESCA, but error rates declined as the number of epochs increased. Rather than merely applying transfer learning, this research highlights the impact of systematic tuning specifically epoch count optimization in enhancing model accuracy for difficult to distinguish disease classes. These findings underscore the urgency of developing high performance, automated disease detection tools to support precision agriculture and sustainable crop health monitoring.]]>
<![CDATA[Accurate Skin Tone Classification for Foundation Shade Matching using GLCM Features-K-Nearest Neighbor Algorithm ]]>
<![CDATA[Syahputra, Muhammad Reza]]>;
<![CDATA[Mazdadi, Muhammad Itqan]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Farmadi, Andi]]>;
<![CDATA[Saputro, Setyo Wahyu]]>;
<![CDATA[Rozaq, Hasri Akbar Awal]]>;
<![CDATA[Sutaji, Deni]]>
<![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.2025.6.5.4723
<![CDATA[Foundation shade matching remains a significant challenge in the beauty industry, particularly in Indonesia where consumers exhibit three distinct skin tone categories: ivory white, amber yellow, and tan. Manual foundation selection often results in mismatched shades, leading to customer dissatisfaction. This study presents a novel automated skin tone classification system combining Gray Level Co-Occurrence Matrix (GLCM) feature extraction with the K-Nearest Neighbor (KNN) algorithm. The GLCM method extracts four key texture features (contrast, homogeneity, energy, and entropy) from facial images, while KNN performs classification. A comprehensive dataset of 963 facial images was used, with 770 training and 193 test samples collected under controlled lighting conditions. After testing K values from 1 to 15, the optimal K=1 achieved 75.65% accuracy. Compared to baseline color histogram methods (60% accuracy), our GLCM-KNN approach demonstrates 15.65% improvement in classification performance. This research contributes to computer vision applications in beauty technology, enabling the development of mobile applications for virtual foundation try-on and personalized product recommendations. The findings have significant implications for the cosmetics industry, particularly for automated cosmetic shade matching systems and enhanced customer experience in online beauty retail. Further research is recommended to explore deep learning approaches and expand dataset diversity to improve accuracy.]]>
<![CDATA[The Role of Deep Learning in Cancer Detection: A Systematic Review of Architectures, Datasets, and Clinical Applicability ]]>
<![CDATA[Abdurrahman, Muhammad Farhan]]>;
<![CDATA[Rianto, Yan]]>;
<![CDATA[Hamzah, Nasir]]>;
<![CDATA[Firmansyah, Muhammad]]>;
<![CDATA[Prawira, Nurul Adi]]>;
<![CDATA[Nugraha, Thomas Fajar]]>
<![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.2025.6.5.4748
<![CDATA[Early cancer detection continues to be a significant challenge in clinical practice due to limitation of conventional diagnostic technique that often takes time and error prone. This systematic review evaluates the efficacy of deep learning (DL) architecture and datasets to improve cancer detection and diagnosis. We performed a structural analysis on 40 high-impact research paper published in Q1 journals between 2014 and 2025, considering DL model performance, datasets, and clinical relevance. Results indicate that fundamental architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) consistently report high diagnostic accuracy (>90%) on radiology- and histopathology-based imaging datasets. Conversely, DL performance on non-imaging clinical data, including electronic medical records (EMDs), is more varied. Evaluation metrics such as AUC and DICE shows the trade-off between classification precision and segmentation accuracy. Despite their potential, DL models have significant limitations in terms of generalization, interpretability, and integration within real-world clinical workflows. This review highlights the need for standardized evaluation, implementation of ethical models, and multi-modal data fusion to facilitate wider and more equitable clinical uptake of DL in cancer diagnostics.]]>
<![CDATA[REACH: A Reinforcement Learning-Based Protocol for Adaptive Cluster Head Selection in Wireless Sensor Networks ]]>
<![CDATA[Hadi, Novi Trisman]]>;
<![CDATA[Supriyanto, Supriyanto]]>;
<![CDATA[Pinastawa, I Wayan Rangga]]>;
<![CDATA[Setyadinsa, Radinal]]>
<![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.2025.6.5.4754
<![CDATA[Wireless Sensor Networks (WSNs) are widely used in critical applications such as environmental monitoring and the Internet of Things (IoT), where energy efficiency and minimal latency are critical for network robustness and effectiveness. Conventional clustering and routing methods often struggle to adapt to fluctuating network conditions, resulting in suboptimal energy usage and increased latency. This study introduces REACH, an adaptive clustering and routing algorithm that leverages reinforcement learning to optimize energy consumption and reduce latency in WSNs. The proposed protocol dynamically selects cluster heads based on real-time network characteristics, including node density and energy levels, enhancing adaptability and robustness. Simulation results using MATLAB show significant improvements, with energy consumption reduced by 35% and latency reduced by 40% compared to traditional protocols such as LEACH and HEED. These findings suggest that reinforcement learning can significantly improve the performance of WSNs by extending the network lifetime and minimizing data transmission delay. This research contributes to the development of intelligent network protocols, offering practical insights into the integration of reinforcement learning for sustainable and scalable WSN design.]]>
<![CDATA[Cross-Temporal Generalization of IndoBERT for Indonesian Hoax News Classification ]]>
<![CDATA[Riadi, Agus Teguh]]>;
<![CDATA[Indriani, Fatma]]>;
<![CDATA[Mazdadi, Muhammad Itqan]]>;
<![CDATA[Faisal, Mohammad Reza]]>;
<![CDATA[Herteno, Rudi]]>
<![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.2025.6.5.4757
<![CDATA[The spread of hoaxes in digital media poses a major challenge for automated detection systems as language and topics evolve over time. Although Transformer-based models such as IndoBERT have demonstrated high accuracy in previous studies, their performance across different time periods remains underexplored. This study examines the cross-temporal generalization ability of IndoBERT for hoax news classification. The model was trained on labeled articles from 2018–2023 and tested on data from 2025 to evaluate its robustness against temporal distribution shifts. The results indicate high accuracy on similar-period data (99.67–99.89%) but a decrease on 2025 data (95.45–95.87%), with most errors occurring as false negatives in the hoax class. These findings highlight the impact of temporal distribution shifts on model reliability and underscore the importance of adaptive strategies such as periodic retraining and domain-based data augmentation. Practically, this model has the potential to assist social media platforms and government institutions in developing dynamic and time-adaptive hoax detection systems. The cross-temporal approach employed in this study also offers methodological innovation compared to conventional random validation, as it better reflects real-world conditions where misinformation patterns continually evolve.]]>
