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[Development of a Strategy for Independent Village Development Based on IDM Predictions Using Linear Regression: A Study of Bi'ih Village, South Kalimantan ]]>
<![CDATA[Artamevia, Mima]]>;
<![CDATA[Lubis, Muharman]]>;
<![CDATA[Mukti, Iqbal Yulizar]]>
<![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.2025.6.5.5265
<![CDATA[Village development in Indonesia still faces inequality due to uneven utilization of technology, resulting in many villages lagging behind economically despite their strong social and environmental potential. This study aims to predict the status of the Village Development Index (IDM) of Bi’ih Village in 2025 and formulate development strategies toward an independent village through the integration of information technology and local wisdom. The method used is linear regression analysis of IDM data from 2017 to 2024, followed by a gap analysis comparing the achievements of Bi’ih Village with those of 11 self-reliant villages in Karang Intan Subdistrict. The prediction results show that the IDM value of Bi’ih Village in 2025 will be 0.7744, placing it in the Advanced category, 0.0411 points below the Independent threshold (>0.8155). The Social Resilience Index (IKS = 0.8782) and Environmental Resilience Index (IKL = 0.8388) have exceeded the Independent threshold, while the Economic Resilience Index (IKE) remains low at 0.6061. The main constraints include limited access to digital markets, manual financial record-keeping, and low digital literacy among village entrepreneurs. The novelty of this research lies in the formulation of a development strategy based on the integration of ICT and local wisdom, with a focus on strengthening digital BUMDes, implementing integrated financial systems, developing upstream–downstream partnerships, and branding local products. This approach demonstrates that digitalization rooted in local wisdom can enhance economic productivity, strengthen resilience, and support sustainable progress toward independent-village status.]]>
<![CDATA[Deep CNN for Wetland Mapping from Satellite Imagery ]]>
<![CDATA[Ramadhan, As`'ary]]>;
<![CDATA[Herteno, Rudy]]>;
<![CDATA[Farmadi, Andi]]>
<![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.2025.6.5.5280
<![CDATA[Wetland loss endangers the ecosystem through loss of biodiversity, carbon sequestration and flood regulation potential. A precise determination of wetlands status is necessary to safeguard for their conservation and ensure sustainable management. Implementation This study aims to assess the performance of deep CNNs in wetland detection using high-resolution Google Earth image data in South Kalimantan province, Indonesia. The work adopts the Chopped Picture Method (CPM) and the use of sliding windows for data augmentation to improve the diversity of the dataset and reduce the computational cost. Two CNN models, VGG-16Net, and LeNet-5, were trained using a dataset comprising 220 satellite images, which we converted into 89,100 patches of 56×56 pixels. Performance was compared using accuracy, precision, recall, and F1-score. Experimental results show good levels of accuracy for the two architectures, but LeNet-5 provided more stable results between test locations, having a F1-score closer to 100% and spending less computational time (≈10s per epoch) than VGG-16Net (≈40s per epoch). These results validate that CPM significantly increases the variety of training data, making it possible for a CNN to correctly identify the vague and irregular shapes of wetlands with high accuracy. In addition to advancing environmental conservation strategies, the study highlights the contribution of informatics to large-scale, automated environmental monitoring, particularly in supporting wetland conservation, sustainable land-use planning, and climate adaptation efforts.]]>
<![CDATA[Performance Comparison of Child Stunting Prediction Support Vector Machine vs Random Forest with Grid Search Optimization ]]>
<![CDATA[Elim, Marthinus Ikun]]>;
<![CDATA[Utami, Ema]]>
<![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.2025.6.5.5285
<![CDATA[Stunting is a serious global health problem, particularly in developing countries. Its prevalence is high in Indonesia, reaching approximately 24.4% among children under five in 2021. This condition, defined as failure to thrive due to chronic malnutrition, repeated infections, and a lack of psychosocial stimulation, has long-term impacts on an individual's cognitive development and productive capacity. This study aims to conduct a comparative analysis of the Support Vector Machine and Random Forest algorithms in predicting stunting in children, with a focus on evaluating the impact of hyperparameter optimization using Grid Search on model performance. This study used the public stunting dataset from Kaggle and included data preprocessing steps such as handling missing values, duplication, encoding, and scaling. The data was then divided into 80% for training, 10% for testing, and 10% for validation. Comprehensive evaluation metrics such as precision, recall, F1-score, and ROC-AUC were also used to assess model performance. Grid Search optimization was applied to both models to find the best hyperparameter combination. Experimental results showed that Grid Search optimization significantly improved the accuracy of the SVM model from 94.29% to 98.37%. Meanwhile, the Random Forest model demonstrated very high performance, achieving 99.59% accuracy both before and after Grid Search optimization. These findings underscore the significant potential of machine learning models in supporting stunting prevention efforts for public health intervention policies. This research contributes to the development of machine learning-based decision support systems for public health, particularly in early detection and intervention strategies for stunting. ]]>
<![CDATA[MSMEs Recommendation System using Item-Based Collaborative Filtering and LightGBM Machine Learning ]]>
<![CDATA[Mar’atuttahirah, Mar’atuttahirah]]>;
<![CDATA[Tunnisa, Khaera]]>;
<![CDATA[Ra, Danang Fatkhur Razak]]>;
<![CDATA[Najwa, Hafizah]]>;
<![CDATA[Fahrisal, Januar]]>
<![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.2025.6.5.5292
<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) face challenges in recommendation systems for digital economy growth, particularly in participatory development and sustainable revenue optimization. This study aims to develop a recommendation system using Item-Based Collaborative Filtering and LightGBM for stock prediction and item recommendation at Kedai Pesisir MSME. Based on 1,229 transaction records from January to July 2025, we performed preprocessing, feature engineering, and LightGBM training to generate daily stock predictions and monthly priorities for August 2025 to January 2026. Evaluation yielded RMSE 0.069, MAE 0.034, and MAPE 1.14%, indicating high accuracy. This advances informatics by providing a scalable AI tool for MSME inventory management and revenue enhancement, supporting strategic decisions in dynamic markets.]]>
<![CDATA[Data Augmentation-Driven Predictive Performance Refinement in Multi-Model Convolutional Neural Network for Cocoa Ripeness Prediction ]]>
<![CDATA[Apriani, Apriani]]>;
<![CDATA[Switrayana, I Nyoman]]>;
<![CDATA[Hammad, Rifqi]]>;
<![CDATA[Irfan, Pahrul]]>;
<![CDATA[Pratama, Gede Yogi]]>
<![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.2025.6.5.5298
<![CDATA[Timely and accurate prediction of cocoa fruit ripeness is critical for optimizing harvest schedules, improving yield quality, and supporting post-harvest processing. Conventional visual inspection methods are prone to subjectivity and inconsistencies, especially when distinguishing among multiple ripeness levels based on fruit age. This study proposes a deep learning approach that leverages multi-model convolutional neural network transfer learning combined with image data augmentation to classify cocoa fruit into four maturity stages derived from fruit age. An augmented dataset of cocoa fruit images was used to fine-tune five well-established pre-trained models: MobileNetV2, Xception, ResNet50, DenseNet121, and DenseNet169. Data augmentation techniques were employed to increase variability and improve model generalization. Model evaluation was conducted using a standard 80:20 training-to-testing split to ensure sufficient data for learning while preserving a representative test set across all ripeness classes. The results demonstrate that DenseNet169 consistently outperformed other models, achieving the highest average accuracy of 85,05%, followed by DenseNet121 84,06%. Across all models, the use of data augmentation led to notable performance gains, highlighting its importance in enhancing predictive capability and reducing overfitting. The proposed framework shows promising potential for automating ripeness classification in agricultural contexts, offering a robust, scalable, and accurate solution for intelligent cocoa harvest management. This work contributes to the growing application of deep learning in precision agriculture, particularly in addressing fine-grained classification problems using limited but enriched visual data.]]>
<![CDATA[Systematic Optimization of Ensemble Learning for Heart Failure Survival Prediction using SHAP and Optuna ]]>
<![CDATA[Setia, Bayu]]>;
<![CDATA[Zaky, Umar]]>
<![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.2025.6.5.5324
<![CDATA[Heart failure (HF) stands as a major global health problem where precise and early prediction of patient prognosis is essential for improving clinical management and patient care. A common obstacle for standard machine learning models in this domain is the prevalent issue of class imbalance within clinical datasets. To overcome this challenge, this study introduces a systematically optimized ensemble learning model for the accurate classification of patient survival. The methodology was applied to a publicly accessible clinical dataset of 299 heart failure patients. Its comprehensive framework included logarithmic transformation, stratified data splitting (80:20), SHAP-based selection of eight key features, and hyperparameter tuning with Optuna over 75 trials, with the specific objective of maximizing the F1-score using 10-fold cross-validation. The performance of three ensemble models (Random Forest, XGBoost, and LightGBM) was refined using decision threshold tuning. The results revealed that the fully optimized Random Forest model yielded superior outcomes, attaining an accuracy of 96.67%, an F1-score of 0.9474, and precision and recall values of 0.95, demonstrating high reliability with only a single instance of a False Negative and False Positive. The study concludes that the systematic application of SHAP, SMOTE, and Optuna within an ensemble framework substantially improves classification performance for imbalanced HF data, surpassing existing benchmarks. This work thus provides a replicable and systematic framework for developing reliable machine learning models from complex, imbalanced medical datasets, contributing a valuable methodology to the field of computational science.]]>
<![CDATA[Predicting Underweight Toddlers in Gorontalo Province Using Supervised Learning Algorithms ]]>
<![CDATA[Yunus, Muhajir]]>;
<![CDATA[Nurdin, St Suryah Indah]]>;
<![CDATA[Fitriah, Fitriah]]>
<![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.2025.6.5.5331
<![CDATA[Malnutrition in toddlers, notably underweight, remains a critical public health issue in Indonesia. According to the 2023 Indonesian Health Survey, the prevalence of underweight among toddlers has reached 15.9%. This condition has a significant impact on children's physical growth, cognitive development, and overall quality of life. This study aims to develop a predictive model for early detection of toddler nutritional status using three supervised machine learning algorithms: Decision Tree C4.5, K-Nearest Neighbor, and Naïve Bayes. The dataset consisted of 9,284 toddler records from Gorontalo Province, comprising eight attributes and one class label indicating nutritional status. Evaluation results showed that the Decision Tree C4.5 algorithm delivered the best performance with 98.56% accuracy. The K-Nearest Neighbor model achieved an accuracy of 97.99%, while the Naïve Bayes model obtained 96.96%. These findings demonstrate that machine learning can be an effective tool for identifying toddlers at risk of undernutrition early in their development. Beyond individual predictions, the proposed model represents a significant advancement in health informatics by providing a scalable decision-support system. This system can enhance the efficiency and precision of public health interventions, enabling faster, data-driven responses to combat malnutrition and improve child health outcomes across broader populations.]]>
<![CDATA[Integrated Fuzzy Logic Model for Smart Water Quality Monitoring and Floating Net Cage Optimization in Barramundi Aquaculture ]]>
<![CDATA[Pramana, Rozeff]]>;
<![CDATA[Alajuri, M Hasbi Sidqi]]>
<![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.2025.6.5.5346
<![CDATA[Water quality and aquatic conditions are critical factors in the success of fish farming with Floating Net Cages (FNCs). However, manual monitoring is often delayed due to limited human resources, irregular measurement schedules, and dependence on manual sampling, which can result in late detection of deteriorating water quality and ultimately increase the risk of fish stress, disease outbreaks, and mortality. This study aims to develop an Internet-based water quality monitoring system, integrated with smartphones and PCs, to support rapid decision-making for FNC relocation when water conditions deteriorate. The system is equipped with sensors for temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, anemometer, and wind direction, and was field-tested for 36 days in sea-based Barramundi aquaculture. Decision-making was implemented using a Fuzzy Inference System (FIS) with input variables: temperature, DO, pH, and anemometer data, while the output variable was the FNC status: “Relocate” or “Remain.” Results indicated that water quality changes occurred across both short-term and long-term intervals, and during a 56-hour fuzzy simulation, 10 data points suggested “Relocate” while 46 data points indicated “Remain.” The novelty of this research lies in the integration of real-time IoT monitoring with fuzzy logic specifically for FNC relocation decision-making, bridging environmental sensing and intelligent decision support. These findings demonstrate that the proposed system is more effective and efficient than conventional methods, contributing to the advancement of intelligent aquaculture technologies.]]>
<![CDATA[Optimizing Data Augmentation Parameters in YOLOv11 for Enhanced Rip Current Detection on Small Datasets from Depok-Parangtritis Coastline ]]>
<![CDATA[Putri, Madina Hayva]]>;
<![CDATA[Zaky, Umar]]>;
<![CDATA[Prabawa, Bayu Argadyanto]]>
<![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.2025.6.5.5352
<![CDATA[Rip currents are powerful ocean currents that can suddenly pull swimmer offshore and are often difficult to recognize visually. However, automatic monitoring technology for detecting rip currents is still limited, while small datasets often lead to overfitting problems and reduce detection accuracy. This study aims to optimize data augmentation parameters in YOLOv11 to improve the mean Average Precision (mAP) value and enable rip current detection even with limited data. The dataset was collected from Google Earth and aerial photographs from the Depok-Parangtritis coastline. Preprocessing includes manual labelling, cropping, and resizing to 640 x 640 pixels. Four augmentation techniques were applied, namely crop (0-10%), rotation (-10% to +10%), brightness adjustment (-10% to +10%), and 1 pixel blur using Roboflow. The dataset was split into 70% training and 30% validation. The YOLOv11 model was then trained and evaluated with precision, recall, and mAP metrics. Results show that data augmentation significantly improves model performance. Dataset 2 without augmentation achieved only 31.8% precision, 32.8% recall, and 23.8% mAP50, while the best model from a combination of the original Dataset 1 and the augmented Dataset 3 reached 90.6% precision, 85.7% recall, and 90.4% mAP50. The integration of YOLOv11 into a web application enables automatic detection in both images and videos with bounding box and confidence score. This study emphasizes the importance of visual variation in the dataset for improving the model generalization and provide a practical foundation of real-time coastal monitoring system.]]>
<![CDATA[Random Forest Machine Learning Analysis of Generative AI’s Impact on Learning Effectiveness in Indonesian Higher Education ]]>
<![CDATA[Sallu, Sulfikar]]>;
<![CDATA[Hendriadi, Hendriadi]]>
<![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.2025.6.5.5365
<![CDATA[Generative Artificial Intelligence (GenAI) has rapidly penetrated Indonesian higher education, creating opportunities for learning innovation while raising concerns about effectiveness and academic integrity. This study develops a machine learning–based quantitative model to analyze the impact of GenAI usage on learning effectiveness, with a particular focus on Informatics students as key digital literacy stakeholders. Data were collected from a simulated survey of 300 students, covering demographics, GPA, exam scores, GenAI usage patterns, digital literacy, motivation, self-efficacy, academic integrity, and institutional support. Preprocessing steps included normalization of continuous variables, one-hot encoding of categorical variables, and feature selection using Recursive Feature Elimination (RFE). Six machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network—were compared to identify the best predictive model. Results show that Random Forest achieved the highest performance, with 87% accuracy and an AUC greater than 0.90, significantly outperforming other algorithms. The most influential predictors were digital literacy, institutional policies, and frequency of GenAI usage, while demographic variables contributed minimally. These findings suggest that GenAI can enhance learning effectiveness in Informatics education when supported by critical digital literacy and ethical awareness. The novelty of this study lies in integrating survey-based educational data with Random Forest machine learning to empirically model GenAI’s role in Indonesian higher education. The results provide practical implications for policymakers, educators, and institutions to design AI-integrated learning strategies that maximize innovation while safeguarding academic integrity.]]>
