Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
Articles
1,174 Documents
<![CDATA[Comparison of SVR Parameter Optimization Using Particle Swarm Optimization (PSO) and Random Search for Rice Harvest Yield Prediction ]]>
<![CDATA[Narlin Yumeivia]]>;
<![CDATA[Farid Wajidi]]>;
<![CDATA[Wawan Firgiawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5509
<![CDATA[Rice yield is an important part in a precision agriculture system that can support farmers' decision-making in a more targeted manner. The author's research aims to help farmers and stakeholders in Bambang Village predict crop yields accurately to overcome production fluctuations. Through appropriate efforts and strategies, this technology is expected to improve food security and farmer welfare. The research method uses the Support Vector Regression (SVR) algorithm for the modeling process, with the help of Particle Swarm Optimization (PSO) and Random Search optimization in finding the best parameters. The research dataset includes 1,120 historical data of rice harvests in Bambang Village for the 2022–2023 period tested through 70:30 and 60:40 data sharing scenarios. Model performance is evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) metrics. The MAPE metric is used as the main indicator of relative accuracy by measuring the average percentage deviation between predicted values and actual values; a low MAPE value is very significant because it reflects the model has a minimal error rate on a percentage scale, thus providing more precise estimates for farmers. The results showed that both optimization methods successfully identified SVR parameters (C, gamma, epsilon) that followed the data trend. Random Search produced slightly superior R2 performance (reaching 82.20% at a 60:40 ratio), while PSO showed more consistent parameter exploration stability. These findings demonstrate that the integration of machine learning and optimization techniques has great potential in strengthening data-driven agricultural systems to improve food security and farmer welfare.]]>
<![CDATA[Implementation Of Cnn Mobile Netv2 For Classification And Detection Of Diseases In Banana Plants Through Leaf Images ]]>
<![CDATA[Maria Yunita]]>;
<![CDATA[Yustina Yesisanita Yeyen]]>;
<![CDATA[Angie Ray Chanda]]>;
<![CDATA[Elisabeth Elen Noweng]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5510
<![CDATA[Banana plants are vulnerable to disease attacks, especially in remote areas with limited access. Banana farmers struggle to identify and classify types of diseases on banana plants early on due to limited information about the types of diseases and the characteristics of diseases that attack bananas.The purpose of this study is he development of a CNN model with a MobileNet architecture for the classification and detection of diseases through banana leaf images, which can be implemented in an Android application. The method used applies a Convolutional Neural Network (CNN) using the MobileNetV2 architecture that can help classify banana plant diseases. The banana leaf image dataset was obtained independently and additionally from the Kaggle platform up to 4135 images. The images were then divided into 6 classes consisting of healthy leaves, panama disease, moko disease, leaf pests, yellow sigatoka and black sigatoka. The image dataset was then divided again into 3 parts: training data, validation data and test data with a data division of 80:10:10. The results showed that CNN with MobileNetV2 architecture can be used for disease classification and detection with an accuracy rate of 87.26% for the test data, 89,59 for validasi and 92.71% for the training data. This model was successfully implemented on the Android platform using Android Studio to detect banana plant diseases in real time without special tools.]]>
<![CDATA[Transformer-Based Multi-Class Intrusion Detection Using CICIoMT2024 Dataset for Secure IoMT Networks ]]>
<![CDATA[Eko Arip Winanto]]>;
<![CDATA[Sharipuddin Sharipuddin]]>;
<![CDATA[Benni Purnama]]>;
<![CDATA[Nurhadi Nurhadi]]>;
<![CDATA[Lasmedi Afuan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5512
<![CDATA[Internet of Medical Things (IoMT) ecosystems significantly enhance healthcare services but simultaneously expand the attack surface, exposing medical networks to diverse cyber threats such as distributed denial-of-service and spoofing attacks. Existing intrusion detection systems for IoMT are often limited to binary classification and struggle to capture complex multi-class attack behaviors, particularly under highly imbalanced data distributions. This study proposes a deep Transformer-based intrusion detection model as a reproducible baseline for multi-class intrusion detection in IoMT environments. The model is evaluated on the CICIoMT2024 dataset, which comprises 19 traffic classes including benign and multiple attack categories. Data preprocessing involves stratified data splitting, feature normalization, and label encoding to ensure fair evaluation. The proposed baseline employs a six-layer Transformer encoder with eight attention heads and is trained using the AdamW optimizer. Experimental results demonstrate an overall accuracy of 98.76% and a macro F1-score of 0.92, indicating strong detection capability across most attack classes. The model achieves excellent performance on benign traffic and high-volume attacks such as DDoS and DoS, while performance degradation is observed on minority classes, including ARP spoofing, highlighting the impact of class imbalance. These findings establish the proposed Transformer model as a transparent and robust baseline for IoMT intrusion detection research. By providing reproducible performance benchmarks, this work supports future development of hybrid and imbalance-aware detection mechanisms aimed at enhancing real-time security in medical cyber-physical systems.]]>
<![CDATA[Classification of Roronoa Zoro Anime, Cosplay, and Action Figure Images Using VGG16 and Inception V3 with Logistic Regression and Support Vector Machine to Improve Popular Culture Object Recognition ]]>
<![CDATA[Denaldy Oktavian Noor Rizki]]>;
<![CDATA[Imam Yuadi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5516
<![CDATA[The diversity of visual representations of anime characters across anime scenes, cosplay photographs, and action figure images poses challenges for automated image classification due to variations in pose, lighting, background, and visual style. This study aims to develop a robust image classification system for the character Roronoa Zoro using deep learning–based feature extraction combined with classical classification algorithms. The method employs VGG16 and Inception V3 as feature extractors, followed by classification using Logistic Regression and Support Vector Machine. The dataset comprises three classes (anime, cosplay, and action figure), processed through image resizing, normalization, and data augmentation. Performance was evaluated using accuracy, F1-score, Area Under Curve (AUC), Matthews Correlation Coefficient (MCC), confusion matrix, silhouette plot, and multidimensional scaling. The experimental results show that Inception V3 combined with Logistic Regression achieved the best performance, with an AUC of 0.993, accuracy of 95.7%, F1-score of 0.957, and MCC of 0.935, outperforming VGG16 with Logistic Regression, which achieved 91.7% accuracy and an AUC of 0.986. Visualization-based evaluation indicates that Inception V3 produces more separable feature representations, particularly in distinguishing cosplay images from anime and action figure categories. This research demonstrates the effectiveness of multi-model feature extraction and classification for improving recognition performance in character-based image classification tasks and contributes empirically to the application of hybrid deep feature–machine learning approaches in computer vision.]]>
<![CDATA[Hybrid Unsupervised-Supervised Learning for Housing Submarket Segmentation and Price Prediction in Surabaya Urban Areas ]]>
<![CDATA[Rinabi Tanamal]]>;
<![CDATA[Satria Adi Nugraha]]>;
<![CDATA[Nathalia Minoque Kusuma Salma Rasyid Jr]]>;
<![CDATA[Livanty Efatania Dendy]]>;
<![CDATA[Jessica Theijer]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5517
<![CDATA[Surabaya’s rapid population growth, reaching 3.02 million residents, has intensified housing affordability challenges and increased structural variability in residential markets. This study proposes a hybrid machine learning framework that combines unsupervised clustering with supervised classification to identify submarket segments and predict housing price categories. A dataset of 490 properties containing structural, land, ownership, and contextual features was preprocessed and analyzed using K-Means. Cluster quality assessment through elbow inspection and a silhouette score of 0.45 indicated the presence of five meaningful market segments. These segments served as targets for a supervised classification stage that evaluated seven models, optimized via randomized hyperparameter search within a standardized preprocessing pipeline. The RBF-SVM achieved the strongest performance, reaching 97 percent accuracy and a macro-F1 score of 0.97, representing an 8 percent improvement over non-hybrid baselines and outperforming boosted ensembles such as XGBoost. Permutation importance analysis identified number of floors, building orientation, position rank, and ownership status as dominant drivers of segment differentiation. The integration of clustering and classification enhances predictive reliability while improving interpretability, offering a transparent analytical toolkit for housing market assessment. The proposed framework provides actionable insights for developers, appraisers, and policymakers in Surabaya, enabling data-driven identification of submarkets and supporting more equitable housing strategies aligned with SDG 11 on sustainable urban development. The approach is scalable to other Indonesian cities and establishes a foundation for future work incorporating spatial, socioeconomic, or temporal predictors.]]>
<![CDATA[Enhancing Flood Area Segmentation in Remote Sensing Images Using Hybrid Attention Mechanism on DeepLabV3+ with ResNet-50 Backbone ]]>
<![CDATA[Annisa Syifaul Ummah]]>;
<![CDATA[Esti Suryani]]>;
<![CDATA[Herdito Ibnu Dewangkoro]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5523
<![CDATA[Flooding is caused by climate change and urbanization, so rapid and accurate monitoring is essential in supporting emergency response. However, flood segmentation still faces challenges in dense vegetation. This study aims to improve and analyze the performance of the Hybrid Attention Mechanism in the form of Point-wise spatial attention (PSA) and Squeeze-and-Excitation Block (SE Block) in the DeepLabV3+ architecture with the ResNet-50 backbone. The methods used include collecting a dataset of 600 training and 63 validation, data augmentation, model development and Hybrid Attention Mechanism design, hyperparameter optimization, ablation study, and performance evaluation. The ablation results obtained show the best performance with accuracy of 0.9624, F1-score of 0.9618, IoU (Non-Flood) of 0.9323, IoU (Flood) of 0.9208, and mIoU of 0.9265, surpassing previous studies that used Modified U-Net in detecting floods in dense vegetation. This research contributes to the development of a flood segmentation model based on a hybrid attention mechanism, which is more effective in detecting flooded areas in densely vegetated regions.]]>
<![CDATA[Systematic Review of Adaptive User Interfaces in E-Commerce for MSMEs: Gaps and User-Centric Indicators ]]>
<![CDATA[Solehatin Solehatin]]>;
<![CDATA[Sri Ngudi Wahyuni]]>;
<![CDATA[Alva Hendi Muhammad]]>;
<![CDATA[M. Hanafi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5529
<![CDATA[Objective – Observations of research results related to adaptive user interfaces in e-commerce have been widely conducted; however, there is a need for evaluation and assessment of indicators based on user requirements. This study aims to conduct a systematic literature review and bibliometric analysis on adaptive user interfaces for MSMEs, based on existing empirical research. Methodology – The methodology applied is a Systematic Literature Review, using the term “adaptive user interface for MSMEs” in “Article Titles, Abstracts, and Keywords” within the Sciencedirect database, resulting in 5,622 publications from 1998 to 2025. The evaluation was carried out on November 21, 2025. The collected articles were analyzed using bibliometric analysis with VOSviewer software, based on fields of study including computer science, decision science, engineering, social sciences, business, management, accounting, and materials science. Findings – Research on adaptive user interfaces for MSMEs has been extensively conducted in line with the digitalization of the e-commerce sector. The observations sought gaps and indicators in each article. Gaps were identified; however, further research is still needed to provide more specific, comprehensive, and well-founded recommendations. Indicators focus on how to provide ease and comfort for users, as well as offering recommendations to them. Research Limitations – This study used the Sciencedirect database for articles related to adaptive user interfaces for MSMEs. Future research could enhance generalizability by integrating other databases such as the Web of Science.]]>
<![CDATA[A Novel Hybrid CNN Model Integrating Resnet and Inception for Precision Classification of Coffee Beans ]]>
<![CDATA[Rahmat Zulpani]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[Poningsih Poningsih]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5537
<![CDATA[Coffee is one of Indonesia’s key strategic commodities with substantial economic value for farmers and exporters. However, inconsistencies in post-harvest coffee bean quality remain a major challenge due to manual, subjective, and expertise-dependent classification. This study addresses this issue by developing an automated and objective computer vision–based classification system using a hybrid deep learning architecture. The proposed model, named RI-Net, integrates the residual learning capability of ResNet with the multi-scale feature extraction of the Inception module to improve the precision and robustness of coffee bean classification across four roasting levels: Green, Light, Medium, and Dark. The model was trained and evaluated on a locally collected dataset and benchmarked against three standard architectures—ResNet50, InceptionV3, and a Fully Convolutional Neural Network (FCNN). Experimental results show that RI-Net outperforms all baseline models, achieving perfect scores of 100% in accuracy, precision, recall, and F1-score. These findings confirm the effectiveness of combining residual and multi-scale features in capturing subtle visual differences across roasting levels. The study demonstrates the potential of advanced hybrid CNN architectures to enhance post-harvest quality control, supporting faster, more consistent, and standardized classification processes that strengthen the competitiveness of Indonesia’s coffee industry.]]>
<![CDATA[Systematic Review of TinyML at the Edge: Optimization, Applications, and Hardware Ecosystem ]]>
<![CDATA[Very Kurnia Bakti]]>;
<![CDATA[Arif Setyanto]]>;
<![CDATA[Alva Hendi Muhammad]]>;
<![CDATA[Ferry Wahyu Wibowo]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5541
<![CDATA[The Internet of Things (IoT) is growing rapidly, making it even more crucial to deploy Machine Learning (ML) models directly on edge devices with limited resources. TinyML fixes this matter by giving microcontroller-class hardware the ability to think for itself. This makes it less reliant on the cloud and better for latency, energy efficiency, and data privacy. This study offers a comprehensive Systematic Literature Review (SLR) of TinyML research published between 2021 and 2025, in accordance with PRISMA principles. We identified 429 records, removed 326 duplicates, and added 83 studies to the final synthesis. The evaluation examines five research inquiries concerning optimization techniques, streamlined architectures, sophisticated learning frameworks, application sectors, and hardware ecosystems. The findings underscore four key themes: enhancing models, utilizing specialized tools and technology, and adapting strategies. Some of the challenges that keep recurring are broken ecosystems, different benchmarking approaches, and on-device learning that isn't compelling when ideas shift. This research presents an open-access taxonomy that categorizes optimization techniques, application trends, and hardware constraints, thereby laying the foundation for a TinyML research agenda within the informatics community. Future directions highlight the importance of adaptive TinyMLOps pipelines, federated learning, LLM-assisted model design, and NVM‑based computing to support scalable and sustainable edge intelligence. The results underscore the relevance of TinyML for advancing informatics and computer science, particularly in enabling secure, efficient, and environmentally aligned IoT systems that support SDG 9 and SDG 12.]]>
<![CDATA[CCTV-Based River Waste Detection Using a Hybrid CNN–Graph Attention Network with Spatial–Contextual Feature Learning ]]>
<![CDATA[Asep Surahmat]]>;
<![CDATA[Lukas Umbu Zogara]]>;
<![CDATA[Fajar Muttaqi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5544
<![CDATA[River waste accumulation has become a serious environmental problem in urban areas, particularly in highly polluted rivers such as the Angke River in Tangerang, where floating waste disrupts ecological balance and increases flood risk. Conventional computer vision–based detection methods often fail under dynamic river conditions due to water surface reflections, turbulence, occlusion, and visually ambiguous debris. This study aims to improve the accuracy and robustness of river waste detection by proposing a hybrid deep learning framework that integrates convolutional and graph-based spatial–contextual reasoning. The proposed method utilizes a ResNet50 backbone for feature extraction from CCTV imagery, followed by spatial graph construction that models adjacency relationships between image regions. A Graph Attention Network (GAT) is then applied to capture contextual dependencies and refine feature representations prior to classification. Unlike conventional CNN-only or YOLO-based detectors that rely primarily on local visual cues and bounding-box representations, the proposed approach explicitly models spatial–contextual relationships between image regions through graph-based attention mechanisms. Experiments were conducted on 4,200 CCTV image frames collected from the Angke River under varying environmental conditions. The proposed model achieved an accuracy of 92.4%, precision of 91.1%, recall of 93.2%, F1-score of 91.9%, and a mean Average Precision (mAP) of 0.78, outperforming CNN-only and YOLO-based baseline models. These findings highlight the contribution of graph-enhanced visual reasoning to the fields of Computer Vision and Intelligent Surveillance, particularly for real-time environmental monitoring systems operating in complex and dynamic visual environments.]]>
