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[Image-Based Classification of Rice Field Conversion: A Comparison Between MLP and SVM Using Multispectral Features ]]>
<![CDATA[Anisya, Anisya]]>;
<![CDATA[Sumijan, Sumijan]]>;
<![CDATA[Syahrani, Anna]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5179
<![CDATA[The conversion of farmland into non-agricultural purposes has emerged as a pressing concern in many urban regions, including Koto Tangah District, Padang City. In this area, agricultural land experienced a 4% shift in land use between 2022 and 2024. If this trend continues, it could lead to a notable decline in rice production and ultimately threaten food security. This research focuses on examining spatial transformations of rice fields from 2022 to 2024 by utilizing Sentinel-2 satellite imagery along with advanced classification techniques. Vegetation and moisture features were extracted using NDVI, NDWI, texture analysis through GLCM, and Principal Component Analysis (PCA). To classify land cover changes and assess model accuracy, two machine learning approaches were applied: Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The findings reveal a considerable reduction in dense vegetation, indicated by the downward shift of NDVI values in 2024. MLP achieved an accuracy of 82%, outperforming SVM, which reached 71%. Furthermore, MLP obtained a higher F1-score for non-rice field detection (0.75 vs. 0.74) and produced more realistic delineations of rice field boundaries during spatial validation. These outcomes highlight the potential of MLP in monitoring land use conversion, supporting agricultural land conservation, and guiding sustainable urban planning. Moreover, the study contributes to computer science by advancing the use of machine learning for spatio-temporal analysis and reinforcing the role of non-linear models in satellite image classification.]]>
<![CDATA[A BiLSTM-Based Approach For Speech Emotion Recognition In Conversational Indonesian Audio using SMOTE ]]>
<![CDATA[Nur Shabrina, Nariswari]]>;
<![CDATA[Kasyidi, Fatan]]>;
<![CDATA[Ilyas, Ridwan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5183
<![CDATA[Speech Emotion Recognition (SER) identifies human emotions through voice signal analysis, focusing on pitch, intonation, and tempo. This study determines the optimal sampling rate of 48,000 Hz, following the Nyquist-Shannon theorem, ensuring accurate signal reconstruction. Audio features are extracted using Mel-Frequency Cepstral Coefficients (MFCC) to capture frequency and rhythm changes in temporal signals. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) generates synthetic data for the minority class, enabling more balanced model training. A One-vs-All (OvA) approach is applied in emotion classification, constructing separate models for each emotion to enhance detection. The model is trained using Bidirectional Long Short-Term Memory (BiLSTM), capturing contextual information from both directions, improving understanding of complex speech patterns. To optimize the model, Nadam (Nesterov-accelerated Adaptive Moment Estimation) is used to accelerate convergence and stabilize weight updates. Bagging (Bootstrap Aggregating) techniques are implemented to reduce overfitting and improve prediction accuracy. The results show that this combination of techniques achieves 78% accuracy in classifying voice emotions, contributing significantly to improving emotion detection systems, especially for under-resourced languages.]]>
<![CDATA[Comparison of Information Technology Governance Maturity Levels Based on COBIT 2019 at PT Kereta Commuter Indonesia in 2023 and 2024 ]]>
<![CDATA[Purwadi, Purwadi]]>;
<![CDATA[Santoso, Handri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5200
<![CDATA[This study aims to analyze and compare the maturity level of Information Technology (IT) governance at PT Kereta Commuter Indonesia (KCI) between 2023 and 2024 using the COBIT 2019 framework. The background of this study is based on the operational complexity of KCI which serves a high daily passenger volume, so that the information system becomes the backbone of the smooth transportation service. The method used is a descriptive-comparative case study with a mixed approach, through interviews, Likert scale questionnaires, and internal document reviews such as IT audit reports and government regulations. The results of the analysis show a significant and consistent increase, where the level of IT governance maturity which was previously at level 2 (Managed) and 3 (Defined) in 2023, increased to level 3 and 4 (Quantitatively Managed) in 2024. The most prominent improvements were seen in the strategic domain EDM01 (Ensure Governance Framework Setting) and the operational domain DSS01 (Manage Operations), which successfully reached level 4. This success reflects top management's commitment and ongoing internal evaluation in strengthening IT governance strategically and operationally. The research findings confirm that annual evaluations serve as an objective benchmark for identifying governance gaps, developing digital strategies, and determining future IT investment priorities. Overall, this study confirms that regular assessments can improve the effectiveness of data-driven IT transformation and ensure alignment of IT implementation with the company's business objectives.]]>
<![CDATA[IT Governance through Mathematical Modeling: A Quantitative Assessment of Maturity Using Factor Analysis and Structural Equation Modeling ]]>
<![CDATA[Indrajit, Richardus Eko]]>;
<![CDATA[Dazki, Erick]]>;
<![CDATA[Kurniawan, Rido Dwi]]>;
<![CDATA[Dio F, Januponsa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5201
<![CDATA[IT Governance (ITG) ensures an organization's technological capabilities align with its business strategy. Although frameworks like COBIT 2019 offer structured guidelines, many assessment techniques rely on qualitative measures, which can compromise objectivity. This paper proposes a novel quantitative approach that integrates Factor Analysis (FA) and Structural Equation Modeling (SEM) to measure IT Governance maturity. By mapping each COBIT 2019 domain—EDM, APO, BAI, DSS, and MEA—onto a latent construct, organizations gain empirical insights into their governance status. Exploratory and confirmatory factor analyses validate these domains, while SEM reveals the magnitude and significance of each domain's impact on overall IT Governance maturity. A real-world example from a financial services company, "FinServEU," demonstrates how this framework can prioritize improvements, enhance regulatory compliance, and promote continuous monitoring. The results highlight that quantitative ITG modeling provides a reliable basis for informed decision-making and optimal resource allocation, bridging the gap between broad qualitative assessments and actionable strategies. This approach is crucial for the field of informatics and computer science, as it offers a robust, reproducible, and objective framework for evaluating a key aspect of digital transformation, ensuring that technological progress is guided by sound, data-driven principles.]]>
<![CDATA[Automated Property Valuation with Multi-Hazard Risk: Jakarta Metropolitan Area Study ]]>
<![CDATA[Fachrurrozi, Fachrurrozi]]>;
<![CDATA[Parhusip, Hanna Arini]]>;
<![CDATA[Trihandaru, Suryasatriya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5236
<![CDATA[This study crafts a machine learning framework that systematically integrates multi-hazard disaster risk assessments into automated property valuation for the Jakarta Metropolitan Area. The framework addresses 25–30% MAPE typically observed in disaster-prone regions, providing more reliable valuation results. We made 114 prediction features from 42 input variables by using 14,284 property data from Indonesian markets, physical risk data from the Think Hazard platform, and socio-economic data from Central Bureau of Statistics. Elastic Net model performed superior compared to other models which had R² = 0.7922 and a MAPE of 28.27%. We found that some disaster risks had unexpected beneficial effects on property prices. We expected that risks related to the earth (+40.5%) and water (+19.2%) would have positive effects, while risks related to the weather (-66.9%) would have negative effects. These conflicting results suggest that in complex urban markets, the quality of infrastructure, location premiums, and differences in risk perception may outweigh simple risk penalties. The idea gives realistic ideas for property valuation that takes risks into account, but it also points out big problems with how the market judges how likely a disaster is to happen.]]>
<![CDATA[Improving the Performance of Machine Learning Classifiers in Sentiment Analysis of Jenius Application Using Latent Dirichlet Allocation in Text Preprocessing ]]>
<![CDATA[Prasetyo, Vincentius Riandaru]]>;
<![CDATA[Benarkah, Njoto]]>;
<![CDATA[Rahmad, Bayu Aji Hamengku]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5238
<![CDATA[Sentiment analysis aims to classify a person’s opinion into a specific sentiment, such as positive or negative. The choice of preprocessing used can influence the performance of a sentiment analysis model. The Latent Dirichlet Allocation (LDA) method, commonly used for topic modelling, can be employed as an additional preprocessing step to identify relevant words associated with a particular sentiment label. This study aims to assess whether the LDA method, implemented in the preprocessing stage, can enhance the performance of machine learning models, including Naïve Bayes, Decision Tree, KNN, Logistic Regression, and SVM. This study utilized a dataset comprising 1,800 reviews, with 900 labelled as positive and 900 as negative. Words with an LDA score of at least 0.15 were given additional weight in the TF-IDF stage before model training. After the model was developed, evaluation was carried out by calculating accuracy, precision, recall, and F1-score. The use of LDA in preprocessing improved the performance of all classification models by 1-3% across most evaluation metrics. Specifically, the Logistic Regression model achieved the best performance, followed by SVM and KNN. This performance improvement is aligned with the use of LDA to reduce semantic noise and improve feature representation. Furthermore, this research is also helpful for monitoring customer opinions in the digital banking sector, enabling the rapid and accurate identification of priority issues. Further research could explore the comparison of performance with other topic modelling and feature extraction methods, as well as expanding the dataset and utilizing multiclass models.]]>
<![CDATA[IoT-Enabled Real-Time Monitoring and Tsukamoto Fuzzy Classification of Mandar River Water Quality via Web Integration for Sustainable Resource Management ]]>
<![CDATA[Insani, Chairi Nur]]>;
<![CDATA[Arifin, Nurhikma]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5249
<![CDATA[This study presents the design and implementation of a real-time water quality monitoring system that utilizes pH, Total Dissolved Solids (TDS), and turbidity sensors, integrated with an ESP32 microcontroller. Sensor data are processed using the Tsukamoto fuzzy logic method to classify river water suitability into two categories: Suitable and Not Suitable. This approach effectively addresses imprecise and uncertain data, thereby producing more reliable classifications compared to conventional threshold-based methods. System validation was conducted through field testing over seven consecutive days at four different times of the day (morning, midday, afternoon, and evening), with results demonstrating stable performance. Recorded pH values ranged from 7.02 to 9.96, TDS values from 140 to 176 ppm, and turbidity levels between 4.00 and 5.15 NTU, indicating that the Mandar River remains within safe limits for daily use. The novelty of this study lies in the direct implementation of the Tsukamoto fuzzy logic method on a resource-constrained IoT device (ESP32), enabling edge-level classification with low latency and without full reliance on cloud computing. The system is designed to maintain decision reliability even under fluctuating sensor data, thus offering a practical and integrated solution for real-time monitoring. The main contribution of this work to computer science is the demonstration of lightweight embedded intelligent algorithms capable of running on constrained devices, the reinforcement of Explainable AI through transparent linguistic rules, and the integration of IoT with edge computing to support sustainable resource management in real-time.]]>
<![CDATA[Enhanced U-Net Cnn For Multi-Class Segmentation And Classification Of Rice Leaf Diseases In Indonesian Rice Fields ]]>
<![CDATA[Faturrohman, Faturrohman]]>;
<![CDATA[Nurdiawan, Odi]]>;
<![CDATA[Prihartono, Willy]]>;
<![CDATA[Herdiana, Rully]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5258
<![CDATA[Rice is a strategic food crop whose productivity is often threatened by leaf diseases and pests. This study aims to develop an Enhanced U-Net CNN model for multi-class segmentation and classification of rice leaf conditions from field images to support early detection and plant health management. The methodology includes direct field image acquisition of rice leaves, preprocessing for image quality enhancement, expert data labeling, segmentation using a U-Net architecture, and classification using CNN. The dataset was divided into training and testing data with balanced distribution across four classes: Healthy, BrownSpot, Hispa, and LeafBlast. Evaluation results show that the model can identify rice leaf conditions with high accuracy, although signs of overfitting were observed from the performance gap between training and validation data. The implementation of this model is expected to accelerate automatic disease detection in the field, reduce reliance on manual inspection, and support precision agriculture. This study achieved a testing accuracy of 76.36% with a macro-average F1-score of 0.34. While the results indicate limitations in generalization, the proposed Enhanced U-Net CNN demonstrates the feasibility of combining segmentation and classification in field conditions. This research contributes to agricultural informatics by supporting scalable deployment in precision agriculture systems, reducing reliance on manual inspection, and providing a foundation for further optimization studies.]]>
<![CDATA[Classification of Worker Productivity and Resource Allocation Optimization with Machine Learning: Garment Industry ]]>
<![CDATA[Yusuf, A’isya Nur Aulia]]>;
<![CDATA[Alkaf, Zakiyyan Zain]]>;
<![CDATA[Nurdiniyah, Elsa Sari Hayunah]]>;
<![CDATA[Wisudawati, Tri]]>;
<![CDATA[Fawzi, Muhammad Ihsan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5263
<![CDATA[This study presents an integrated predictive–prescriptive framework for improving workforce management in the garment industry by combining machine learning classification with linear programming optimization. Using a publicly available dataset of 1,197 production records, productivity levels were categorized into low, medium, and high classes. Data preprocessing included handling missing values, one-hot encoding of categorical variables, and class balancing using SMOTE. Eleven classification algorithms were evaluated, with LightGBM achieving the highest performance (accuracy 78.3%, weighted F1-score 78.3%, Cohen’s Kappa 63.4%) after hyperparameter tuning via Bayesian Optimization. The optimized model’s predictions were then incorporated into a linear programming model, implemented with PuLP, to maximize the allocation of high-productivity workers across production departments under capacity constraints. The results yielded an allocation plan assigning 117 high-productivity workers, significantly enhancing potential operational efficiency. The novelty of this work lies in integrating an optimized ensemble learning model with mathematical programming for end-to-end productivity classification and resource allocation, a combination rarely explored in labor-intensive manufacturing contexts. This framework offers a scalable decision-support tool for data-driven workforce planning and could be adapted to other manufacturing domains with similar operational structures. ]]>
<![CDATA[Comparison of Transfer Learning Strategies Using MobileNetV2 and ResNet50 for Ecoprint Leaf Classification ]]>
<![CDATA[Hajar, Siti]]>;
<![CDATA[Murinto, Murinto]]>;
<![CDATA[Yudhana, Anton]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2025.6.5.5266
<![CDATA[This research focuses on the classification of leaf types used in ecoprint production through the steaming technique by applying transfer learning on two widely recognized convolutional neural network (CNN) architectures, MobileNetV2 and ResNet50. Leaves have diverse applications in various sectors such as medicine, nutrition, and handicrafts. The study utilized a total of 600 leaf images from 15 species were collected from the surrounding environment and divided into 80% training and 20% testing sets. The aim of this study is to classify leaf types suitable for ecoprint quickly and efficiently, based on transfer learning with two CNN architectures, while incorporating fine-tuning. MobileNetV2 was selected for its computational efficiency, while ResNet50 was chosen for its ability to address the vanishing gradient problem and deliver high accuracy. Fine-tuning was employed to optimize model performance. Experimental results demonstrate that both architectures achieved strong performance, with MobileNetV2 reaching 94.12% accuracy and ResNet50 slightly outperforming it at 94.96%. Confusion matrix evaluation further confirmed these results, yielding accuracy, precision, recall, and F1-score values of 0.94, 0.95, 0.95, and 0.94, respectively. These findings highlight ResNet50’s superior performance over MobileNetV2 while affirming the effectiveness of both models in ecoprint leaf classification.]]>
