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[Comparative Analysis of Hybrid Intelligent Algorithms for Microsleep Detection and Prevention ]]>
<![CDATA[Nurul'aini, Arvina Rizqi]]>;
<![CDATA[Aprilianto, Rizky Ajie]]>;
<![CDATA[Pribadi, Feddy Setio]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4625
<![CDATA[Microsleep is a critical factor contributing to traffic accidents, posing significant risks to road safety. Research by the AAA Foundation for Traffic Safety found that 328,000 sleep-related driving accidents happen annually in the United States, underscoring the widespread and dangerous nature of drowsy driving. These incidents often occur without warning, making them especially hazardous and difficult to prevent through conventional means alone. This research aims to improve the accuracy of microsleep detection by developing a hybrid intelligent algorithms. It compares three intelligent algorithms: Fuzzy Logic (FL), representing scheme A; Fuzzy Logic combined with Artificial Neural Networks (FL-ANN), representing scheme B; and a combination of Fuzzy Logic, ANN, and Decision Trees (FL-ANN-DT), representing scheme C. These methods were evaluated using performance metrics such as MSE, MAE, RMSE, R², and response time. The results indicate that Scheme C (FL-ANN-DT) significantly outperforms the other approaches, achieving an MSE of 5.3617e-32, MAE of 4.3823e-17, R² of 1.0, and an RMSE close to zero, demonstrating near-perfect accuracy. Compared to previous models, this hybrid approach enhances prediction precision while maintaining real-time feasibility. The findings highlight the potential of FL-ANN-DT as an advanced microsleep detection system, contributing to improved road safety and real-time monitoring applications. This system can serve as a proactive safety layer in driver assistance technologies, reducing the risk of fatigue-related accidents and potentially saving lives.]]>
<![CDATA[Detecting Avocado Freshness In Real-Time: A Yolo-Based Deep Learning Approach ]]>
<![CDATA[Febriani, Atika Dwi]]>;
<![CDATA[Kartikasari, Mujiati Dwi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4626
<![CDATA[The increasing consumption of avocados in Indonesia highlights the need for an effective method to ensure fruit freshness. The main problem lies in the absence of an objective and standardized system for assessing avocado freshness, which may lead to consumer dissatisfaction and food waste. This study aims to address the challenge of identifying avocado freshness to ensure suitability for consumption. Conducted from May 23 to June 5, 2024, the research used butter avocado samples sourced from supermarkets. The method employed is the You Only Look Once version 8 (YOLOv8) deep learning algorithm, known for its real-time object detection capabilities. YOLOv8 offers enhanced performance compared to earlier versions through anchor-free detection, improved speed, and accuracy, making it suitable for fast and reliable freshness detection tasks. Avocados were classified based on estimated spoilage time under room and refrigerator temperatures, ranging from "up to 5 days at room temperature and 14 days in refrigeration" to "not fit for consumption." The model was validated using 120 images categorized into six freshness levels. Evaluation results demonstrated high performance, with 98% accuracy, an F1-Score of 0.978, mAP50 of 0.994, and mAP50-95 of 0.972 after 50 training epochs, confirming the model’s robustness. Real-time tests yielded confidence levels of 96% and 94%, further validating its effectiveness in detecting avocado freshness. To facilitate daily use, a mobile application named Avo Freshify was developed. The app accurately identifies the freshness of avocados and provides valuable information for consumers and sellers. This research contributes to the advancement of artificial intelligence and object detection in food quality control and agricultural technology.]]>
<![CDATA[Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF ]]>
<![CDATA[Febriyanti, Alvi Yuana]]>;
<![CDATA[Prasetya, Dwi Arman]]>;
<![CDATA[Trimono, Trimono]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4648
<![CDATA[Stock price prediction is a major challenge in the financial domain due to high volatility and complex movement patterns. Traditional methods such as fundamental and technical analysis often fail to capture the non-linear characteristics and fast-changing market dynamics, highlighting the need for more adaptive approaches. This study proposes a hybrid deep learning model, CNN-LSTM, which combines CNN's local feature extraction capabilities with LSTM’s ability to model long-term temporal dependencies. To incorporate risk management, the model is also integrated with the Value at Risk (VaR) approach using the Cornish-Fisher Expansion (ECF) to estimate potential losses under extreme market conditions. The study utilizes daily historical stock price data of PT Unilever Indonesia Tbk retrieved from Yahoo Finance. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), where the model achieves an MAE of 78.13 and a MAPE of 2.72%, indicating relatively low absolute and relative prediction errors. These results confirm that the CNN-LSTM approach effectively models stock price movements in dynamic market environments, and the integration with VaR-ECF provides a more comprehensive risk estimate. Thus, this approach not only enhances predictive accuracy but also offers valuable decision-support tools for investors in planning investment strategies.]]>
<![CDATA[Implementation of Enhanced Confix Stripping Stemming and Chi-Squared Feature Selection on Classification UIN Walisongo Website with Naïve Bayes Classifier ]]>
<![CDATA[Muhadzib Al-Faruq, Muhammad Naufal]]>;
<![CDATA[Yuniarti, Wenty Dwi]]>;
<![CDATA[Handayani, Maya Rini]]>;
<![CDATA[Umam, Khotibul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4670
<![CDATA[Academic news classification on university websites remains a challenge due to the growing volume of content and lack of efficient categorization systems. At UIN Walisongo Semarang, this problem hinders students, faculty, and the public from easily accessing relevant information. This study aims to develop an automated academic news classification system to address this issue. We applied a Naïve Bayes Classifier model, enhanced with Term Frequency weighting, the Enhanced Confix Stripping Stemmer for Indonesian language preprocessing, and Chi-Squared feature selection to identify the most informative terms. The dataset consisted of 880 academic news articles from UIN Walisongo’s website, split into 704 training and 176 testing documents. The system achieved 95% accuracy on the test set. To evaluate generalizability, we used a separate evaluation set of 12 new articles, obtaining 83.3% accuracy. The preprocessing stage played a vital role in reducing morphological complexity, while Chi-Squared scoring improved the relevance of selected features. This research highlights the importance of robust text classification techniques in academic information systems, particularly in Indonesian language contexts where language morphology poses unique challenges. The proposed model demonstrates strong performance, scalability, and potential for integration into academic portals to improve information retrieval. This study contributes significantly to the field of Natural Language Processing and applied machine learning in academic settings, especially for Indonesian-language content. It provides an effective solution for automated academic content management in institutional information systems.]]>
<![CDATA[Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models ]]>
<![CDATA[Yasin, Feri]]>;
<![CDATA[Firmansyah, Muhammad Raafi'u]]>;
<![CDATA[Aldo, Dasril]]>;
<![CDATA[Amrustian, Muhammad Afrizal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4681
<![CDATA[Accurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS). Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259.52, an RMSE of 154,036.91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain.]]>
<![CDATA[Marketing Analysis of Shoe Products Using Principal Coordinates Analysis and K-Means Clustering Based on the Marketing Mix at Bintang Sepatu Purwokerto MSME ]]>
<![CDATA[Sinaga, Samuel]]>;
<![CDATA[Ananda , Ridho]]>;
<![CDATA[Karima, Halim Qista]]>;
<![CDATA[Tazuddin, Adrus Mohamad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4687
<![CDATA[Bintang Sepatu Purwokerto MSME is a micro, small, and medium enterprise engaged in the production of local shoes. Recently, this MSME faced a significant issue in the marketing aspect, namely the low achievement of sales targets. Consequently, inventory will accumulate in the warehouse. Accordingly, this research aimed to formulate targeted marketing strategies by clustering customers based on demographic and marketing mix influencing purchasing behavior. This study applied principal coordinate analysis (PCoA) and k-means clustering to manage categorical and numerical data types within the dataset comprising 179 customers and 16 attributes.. The PCoA algorithm was utilized to derive object configurations that were subsequently employed in k-means. The clustering result produced three clusters with good clustering quality based on the Silhouette score, namely 0.790, indicating accurate and representative segmentation. Each cluster obtained had a different customer characteristic. The first cluster, comprising 68 customers (38%), was oriented towards fundamental needs and tended to shop traditionally, classified as a segment of conventional rational customers. Additionally, the second cluster, with 70 customers (39%), exhibited planned and stable decision-making, categorized as mature rational customers. Furthermore, the third cluster comprises 41 customers (23%) who are digitally aware and combine conventional shopping approaches with technological utilization, identified as rational consumers. The segmentation results provide a data-driven foundation for designing targeted marketing strategies, thereby potentially increasing sales, supporting the sustainability of MSMEs, and encouraging the application of unsupervised learning techniques in decision-making processes.]]>
<![CDATA[Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management ]]>
<![CDATA[Apnena, Riri Damayanti]]>;
<![CDATA[Ginting, Gerinata]]>;
<![CDATA[Sudrajat, Ari]]>;
<![CDATA[Islam, Hussain Md Mehedul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4689
<![CDATA[This study addresses the problem of predicting delivery status in supply chain data, a critical task for optimizing logistics and operations. The dataset, which includes multiple features like order details, product specifications, and customer information, was pre-processed using oversampling to address class imbalance, ensuring that the model could handle rare cases of late or canceled deliveries. The data cleaning process involved handling missing values, removing irrelevant columns, and transforming categorical variables into numerical formats. After pre-processing and cleaning, five machine learning models were applied: Logistic Regression, Random Forest, SVM, K-Nearest Neighbors (KNN), and XGBoost. Each model was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed that XGBoost outperformed the other models, achieving the highest accuracy and providing the most reliable predictions for the delivery status. This makes XGBoost the best choice for supply chain data analysis in this context. This study contributes to the growing application of machine learning in supply chain optimization by identifying XGBoost as a robust model for delivery status prediction in large datasets. For future research, exploring hybrid models and advanced feature engineering techniques could further improve prediction accuracy and address additional challenges in supply chain optimization, especially in the context of real-time data processing and dynamic supply chain environments. ]]>
<![CDATA[Optimization Of Extreme Learning Machine Models Using Metaheuristic Approaches For Diabetes Classification ]]>
<![CDATA[Sulaeman, Gilang]]>;
<![CDATA[Nur, Yohani Setiya Rafika Nur]]>;
<![CDATA[Paramadini, Adanti Wido]]>;
<![CDATA[Aldo, Dasril]]>;
<![CDATA[Fathoni, M. Yoka]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4690
<![CDATA[Proper classification of diabetes is a significant challenge in contemporary healthcare, especially related to early detection and clinical decision support systems. This study aims to optimize the Extreme Learning Machine (ELM) model with a metaheuristic approach to improve performance in diabetes classification. The data used was an open dataset containing the patient's medical attributes, such as age, gender, smoking status, body mass index, blood glucose level, and HbA1c. The initial process includes data cleansing, one-hot coding for categorical features, MinMax normalization, and unbalanced data handling with SMOTE. The ELM model was tested with four activation functions (Sigmoid, ReLU, Tanh, and RBF) each combined with three metaheuristic optimization strategies, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm. The results of the evaluation showed that the combination of the Tanh activation function with GA optimization obtained the highest accuracy of 87.98% and an F1-score of 0.5489. Overall, GA optimization appears to be superior to all other measurement configurations in consistent classification performance. The main contribution of this study is to offer a systematic approach to select the best combination of activation functions and optimization algorithms in ELM, as well as to provide empirical evidence to support the application of metaheuristic strategies to improve the accuracy of disease classification based on health data. This research has direct implications for the development of a more precise and data-based medical diagnostic classification system for diabetes.]]>
<![CDATA[Comparative Analysis Of Ant Lion Optimization And Jaya Algorithm For Feature Selection In K-Nearest Neighbor (Knn) Based Electricity Consumption Prediction ]]>
<![CDATA[Wahyusari, Retno]]>;
<![CDATA[Sunardi, Sunardi]]>;
<![CDATA[Fadlil, Abdul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4692
<![CDATA[The increase in demand for electrical energy is in line with increasing population, urbanization, industrial deployment, and technology. Accurate prediction of electrical energy consumption plays an important role in planning, analyzing, and managing electricity systems to ensure sustainable, safe, and economical electricity supply. K-Nearest Neighbors (KNN) is a simple and fast prediction algorithm based on the quality and relevance of the features used. This research proposes to improve the accuracy of energy consumption prediction through feature selection based on metaheuristic algorithms, namely Genetic Algorithm (GA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and Jaya Algorithm (JA). The dataset used is Tetouan City Power Consumption, with a preprocessing process of time feature extraction, min-max scaling normalization, and feature selection. The ALO+KNN and JA+KNN combinations delivered the best and most stable prediction performance, while TLBO+KNN performed poorly. GA+KNN showed the worst overall results among all combinations. The evaluation of model performance was based on RMSE, MAPE, and R² metrics. These findings highlight the importance of selecting a feature selection algorithm that aligns well with the characteristics of the model and dataset to enhance prediction accuracy.]]>
<![CDATA[Comparative Analysis of Machine Learning Algorithms with RFE-CV for Student Dropout Prediction ]]>
<![CDATA[Utami, Sekar Gesti Amalia]]>;
<![CDATA[Setiadi, Haryono]]>;
<![CDATA[Rohmadi, Arif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.3.4695
<![CDATA[The high dropout rate of students in higher education is a problem faced by educational institutions, impacting quality assessments and accreditation evaluations by BAN-PT. This study aims to develop an early prediction model of potential dropout students using demographic data with a learning analytics approach. Five classification algorithms are used in this research, namely Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). The dataset used consists of undergraduate student data of Sebelas Maret University in 2013 (n=2476) which is processed through preprocessing techniques, resampling with SMOTE, and validation using K-Fold Cross-Validation. The results showed that the RF model gave the best performance with an accuracy of 96.01%, followed by LGBM (95.26%), DT (91.24%), LR (83.68%), and SVM (83.19%). The use of the Recursive Feature Elimination with Cross-Validation (RFE-CV) feature selection method was able to improve the efficiency of the model by reducing the number of features without significantly degrading performance. The best feature selection was obtained when using 75% features, which provided an optimal balance between the number of features and model accuracy. The most contributing features include IPS_range (Semester GPA range), parents' income, students' regional origin, as well as several other demographic factors. This study contributes to the development of early warning systems in higher education by providing accurate predictive models and identifying key risk factors.]]>
