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
1,002 Documents
<![CDATA[Detection of Endangered Indonesian Species Across Multiple Taxonomic Classes Using Faster R-CNN ]]>
<![CDATA[Mubarok, Moh. Jabir]]>;
<![CDATA[Fitria Haya, Rizky]]>;
<![CDATA[Fitria, Eka]]>;
<![CDATA[Surya Budi, Brilian]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.4793
<![CDATA[Indonesia’s rich biodiversity includes many endangered species across various taxonomic groups. This study presents a Faster R-CNN deep learning model to detect ten endangered Indonesian species, covering birds, reptiles, mammals, and fishes. A custom dataset with diverse images was annotated and used to train the model with transfer learning on the Detectron2 framework. Evaluation using COCO metrics yielded an average precision (AP) of 54.93%, with the Komodo Dragon achieving the highest AP (82.57%) and Wallace’s Standardwing the lowest (30.82%). The model excels at detecting larger, distinct species but has difficulty with smaller or camouflaged ones in complex environments. Training results confirm that transfer learning aids performance despite limited data. Analysis of misclassifications suggests the need for additional data modalities or context to improve accuracy. This work highlights the potential of Faster R-CNN for automated endangered species monitoring in Indonesia and recommends dataset expansion, data augmentation, and model refinement to enhance detection, particularly for challenging species. This study contributes to computer vision applications in conservation, particularly within low-resource biodiversity contexts.]]>
<![CDATA[An Intelligent IoT-Based Hydroponic Irrigation System for Strawberry Cultivation Using Extreme Gradient Boosting Decision Model ]]>
<![CDATA[Bijanto, Bijanto]]>;
<![CDATA[Abidin, Zainal]]>;
<![CDATA[Asy’ari, Fajar Husain]]>;
<![CDATA[Ali, Rabei Raad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5173
<![CDATA[Most existing implementations rely on static rule-based or fuzzy logic control, which lack adaptability to dynamic environmental changes and often require manual tuning by experts. These limitations are particularly challenging for small-scale farmers who face constraints in technical knowledge, infrastructure, and operational flexibility. To address these issues, this study proposes an intelligent hydroponic irrigation system that embeds the Extreme Gradient Boosting (XGBoost) algorithm as a decision-making model. The system collects real-time sensor data including temperature, humidity, and light intensity, and uses the trained XGBoost classifier to determine irrigation needs with binary output (FLUSH or NO). The system was implemented on a vertical hydroponic setup for strawberry cultivation, and evaluated over a 21-day observation period. The results show that the XGBoost-based model was effective in maintaining consistent vegetative growth, with plants in upper-tier pipes achieving an average height above 25 cm by the end of the cycle. This demonstrates that the model could support responsive and resource-efficient irrigation control. Beyond technical performance, the research highlights the urgency of adopting data-driven smart farming systems to ensure sustainable food production, optimize limited resources, and empower small-scale farmers with accessible and scalable solutions. However, the proposed XGBoost model is still limited to local crops; therefore, when introducing new plant types or additional sensor inputs, parameter adjustments and retraining are required to maintain accuracy. Future improvements may include dynamic model retraining and integration with real-time feedback systems to enhance system autonomy and resilience in broader agricultural settings.]]>
<![CDATA[Comparative Analysis of Classification Models for Sales Prediction in E-commerce: Decision Tree, Random Forest, SVM, Naive Bayes, and KNN ]]>
<![CDATA[Purwanto, Eko]]>;
<![CDATA[Cipto Utomo, Bangun Prajadi]]>;
<![CDATA[Permatasari, Hanifah]]>;
<![CDATA[Mohd, Farahwahida]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5224
<![CDATA[The swift expansion of e-commerce has markedly heightened the necessity for precise sales forecasting, essential for efficient marketing tactics and inventory control. This research evaluates five classification models—Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN)—to predict sales outcomes using e-commerce transaction data. The models were assessed utilizing criteria including accuracy, precision, recall, F1-score, AUC, and Log Loss. The findings indicate that Random Forest exceeds the performance of the other models, with an accuracy of 97.5% and an AUC of 0.991, markedly outperforming the alternatives. This study presents a unique contribution by contrasting these classification models in the realm of e-commerce in Indonesia, yielding significant insights for the advancement of more effective predictive algorithms in informatics. The results not only enhance the optimization of marketing strategies but also enrich the comprehension of machine learning applications in sales forecasting. This study underscores the necessity of choosing the appropriate model for enhanced sales forecasting, with considerable ramifications for data-driven decision-making in the e-commerce sector.]]>
<![CDATA[FuelGuard: Fuel Consumption Anomaly Detection and Visual Verification in Logistics Using Isolation Forest, CBIR, and OCR ]]>
<![CDATA[Auliana, Sigit]]>;
<![CDATA[Permana, Basuki Rakhim Setya]]>;
<![CDATA[Darip, Mochammad]]>;
<![CDATA[Roy, Sujan Chandra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5276
<![CDATA[Manual fuel reporting in Indonesian logistics companies, such as PT Balaraja Distribusindoraya, often leads to inefficiency, fraud, and lack of anomaly supervision. This research aims to develop a web-based system that integrates machine learning and computer vision to monitor fuel consumption and detect anomalies in logistics fleets. The proposed system employs Isolation Forest for unsupervised anomaly detection based on fuel volume, travel distance, and fuel ratio, combined with a deep learning–based CBIR module using MobileNetV2 to validate fuel station images, and OCR to extract numerical data from receipts. Following the CRISP-DM methodology, the model was trained and deployed through a Flask-based API and evaluated using black-box and white-box testing. Experimental results show that Isolation Forest achieves the highest anomaly detection performance (F1-Score = 0.81, ROC-AUC = 0.99), CBIR validates official fuel stations with ≥95% similarity, and OCR reaches 97% accuracy in receipt recognition. The novelty of this study lies in its hybrid integration of anomaly detection and visual verification within a single scalable platform. This research contributes to Informatics by providing a framework for hybrid anomaly detection systems that enhance digitalization, transparency, and operational efficiency in the logistics sector.]]>
<![CDATA[Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT ]]>
<![CDATA[Priyatama, Muhammad Abdhi]]>;
<![CDATA[Nugrahadi, Dodon Turianto]]>;
<![CDATA[Budiman, Irwan]]>;
<![CDATA[Farmadi, Andi]]>;
<![CDATA[Faisal, Mohammad Reza]]>;
<![CDATA[Purnama, Bedy]]>;
<![CDATA[Adi, Puput Dani Prasetyo]]>;
<![CDATA[Ngo, Luu Duc]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5299
<![CDATA[Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.]]>
<![CDATA[Comparative Analysis Of Machine Learning Algorithms For Dengue Fever Prediction Based On Clinical And Laboratory Features ]]>
<![CDATA[Sriyanto, Sriyanto]]>;
<![CDATA[Aziz, RZ Abdul]]>;
<![CDATA[Rahayu, Dewi Agushinta]]>;
<![CDATA[Zuriati, Zuriati]]>;
<![CDATA[Abdollah, Mohd Faizal]]>;
<![CDATA[Irianto, Irianto]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5309
<![CDATA[Dengue fever (DF) remains a global health problem requiring accurate early detection to prevent severe complications. This study applies machine learning (ML) algorithms to clinical and laboratory data for improving diagnostic accuracy. Six classifiers were compared: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). The dataset consists of 1,003 patient records with nine feature columns, of which 989 were used after preprocessing. Class distribution was imbalanced, with 67.6% positive and 32.4% negative cases. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. The results indicate that DT achieved the highest performance with 99.4% accuracy, 99.4% precision, 99.7% recall, and 99.6% F1-score, slightly outperforming NN. KNN, LR, and SVM produced comparable results, while NB showed substantially lower accuracy (44.3%) and limited discriminatory power. ROC analysis confirmed these findings, with DT, NN, SVM, and LR achieving AUC values between 0.992 and 0.999, whereas NB performed poorly. These findings highlight the strong potential of ML algorithms, particularly DT, to support medical decision systems, strengthen informatics-based decision support applications, and enhance the accuracy and speed of dengue diagnosis in clinical practice.]]>
<![CDATA[Integration of Squeeze-and-Excitation in Densenet-121 for Classifying Real and AI-Generated Images ]]>
<![CDATA[Hasaniyyah, Nadiyya]]>;
<![CDATA[Khadijah, Khadijah]]>;
<![CDATA[Sutikno, Sutikno]]>;
<![CDATA[Tyas, Zahra Arwananing]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5336
<![CDATA[Recent advancements in generative technologies, such as Generative Adversarial Networks (GANs) and Latent Diffusion Models, have enabled the creation of AI-generated synthetic images that are increasingly indistinguishable from real ones, posing significant challenges for verifying the authenticity of visual content. This study develops a DenseNet-121 model with hyperparameter optimization and the integration of Squeeze-and-Excitation (SE) attention mechanisms at Early, Mid, and Late positions. Experiments were conducted using the CIFAKE dataset with a resolution of 32×32 pixels to compare the baseline Plain model with three SE variants. Hyperparameter optimization was applied to maximize model performance. The results demonstrate that the Plain DenseNet-121 with optimized hyperparameters achieved an accuracy of 98.52%, outperforming the standard configurations reported in previous studies. The integration of SE yielded varied outcomes, where Mid SE attained the highest accuracy of 98.56%, while Early SE (98.45%) and Late SE (98.48%) exhibited greater stability with lower standard deviations. These findings highlight that combining hyperparameter optimization with appropriate SE placement can enhance model performance for classifying real and AI-generated images. Moreover, SE placement at different positions (Early, Mid, Late) has a significant impact on feature representation and generalization in synthetic image classification, which is increasingly important given the growing difficulty of distinguishing real from AI-generated images.]]>
<![CDATA[Analyzing Marketplace Reviews Using Word2Vec, CNN, and Deep K-Means with Sociolinguistic Approaches ]]>
<![CDATA[Fahry, Fahry]]>;
<![CDATA[Miswaty, Titik Ceriyani]]>;
<![CDATA[Harun, Harun]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5340
<![CDATA[This study investigates the effectiveness of deep learning methods in analyzing linguistically diverse customer reviews on Shopee to generate actionable product insights. By integrating Word2Vec, Convolutional Neural Networks (CNN), and Deep K-Means clustering, the proposed workflow moves beyond simple polarity detection toward aspect-based sentiment analysis. Customer reviews were preprocessed and represented using Word2Vec (skip-gram) to capture semantic proximity across informal registers, slang, abbreviations, and code-switching. A one-dimensional CNN then classified reviews into positive and negative sentiments, achieving 93–94% accuracy with balanced F1-scores across both classes. To extract aspect-level insights, reviews were projected into a latent space via an autoencoder and clustered using K-Means, with evaluation metrics (Silhouette ≈ 0.6; DBI ≈ 0.5) confirming adequate cohesion and separation. Positive clusters highlighted product design, durability, and ease of use, while negative clusters emphasized material quality, packaging, and delivery issues. These findings demonstrate that deep learning can adapt to sociolinguistic variation in Indonesian e-commerce discourse while providing structured, socially meaningful insights. This research is significant for the field of Informatics as it advances Natural Language Processing techniques for multilingual and code-switched data, addressing a key challenge in real-world text mining applications. The approach offers practical value for sellers in improving product quality, enhancing customer satisfaction, and refining marketing strategies.]]>
<![CDATA[Implementation and Evaluation of Static Code Analysis to Identify Security and Code Quality Issues in Academic Information Systems ]]>
<![CDATA[Ramdani, Cecep Muhamad Sidik]]>;
<![CDATA[Shofa, Rahmi Nur]]>;
<![CDATA[Anshary, Muhammad Adi Khairul]]>;
<![CDATA[Gufroni, Acep Irham]]>;
<![CDATA[Yahya, Aria Priawan]]>;
<![CDATA[Fazamin Bin Wan Hamzah, Wan Mohd Amir]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5371
<![CDATA[In today's digital era, websites have become a key component of various digital services, from government and education to business. However, many security incidents occur due to undetected source code vulnerabilities, such as vulnerabilities, bugs, and code smells, which can degrade system performance and reliability. Therefore, a systematic approach is needed to detect and prevent these issues as early as possible. This study aims to implement and evaluate the effectiveness of the Static Code Analysis (SCA) method in identifying security and code quality issues in web applications. The tool used was SonarQube, which was then implemented in the SIMAK Universitas Siliwangi. Evaluation and testing were conducted on the tool's ability to detect various types of problems, its level of accuracy, and its ease of integration into the software development process. In this study, the evaluated aspects were bugs, code smells, and vulnerabilities. The results of this study found 23,241 issues, consisting of 2,356 bugs and 20,885 code smells, without any vulnerabilities found. With a problem ratio of 3.84% of the total code lines of 605,130, and a severity classification dominated by issues at the Critical and Major levels, these results provide an overview of the technical condition of the code used in SIMAK Universitas Siliwangi. This research is expected to provide practical contributions for software developers and security teams in continuously improving the quality and security of web applications. The outcomes of this study are expected to offer substantial and actionable contributions toward advancing the overall quality, robustness, and security of software systems. By strengthening these foundational aspects, the research is projected to positively influence the reliability, continuity, and long-term sustainability of academic service delivery within higher-education environments.]]>
<![CDATA[Random Forest and Artificial Neural Network Data Mining for Environmental and Public Health Risk Modeling in Flood-Prone Urban Areas of Indonesia ]]>
<![CDATA[Mahdiana, Deni]]>;
<![CDATA[Ebine, Masato]]>;
<![CDATA[Wibowo, Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.6.5387
<![CDATA[Floods in urban Indonesia pose severe environmental and public health challenges, exacerbating water contamination, vector proliferation, and disease outbreaks. Rapid urbanization, inadequate drainage systems, and climate change have intensified these impacts, emphasizing the need for integrated predictive frameworks. This study aims to develop a Data Mining (DM)-based modeling approach that combines environmental and health indicators to predict flood-related disease risks. Random Forest (RF) and Artificial Neural Network (ANN) algorithms were applied to multi-domain datasets from 30 flood-prone urban sub-districts between 2018 and 2023, encompassing rainfall, drainage density, land use, and water quality variables, integrated with disease incidence data such as diarrhea, dengue, and leptospirosis. The ANN model achieved superior predictive performance (93% accuracy, AUC 0.93) compared to RF (90% accuracy, AUC 0.90), identifying rainfall intensity, drainage density, and coliform contamination as the most influential predictors. These results demonstrate the capability of AI-driven DM techniques to capture complex interdependencies between environmental and health systems. The developed framework contributes to the field of informatics by providing a scalable, data-driven early warning tool for flood-related health risks, supporting evidence-based decision-making in disaster risk management and enhancing public health resilience in rapidly urbanizing regions.]]>
