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
1,048 Documents
<![CDATA[Performance Comparison of Learned Features from Autoencoder and Shape-Based Hu Moments for Batik Classification ]]>
<![CDATA[Dzulqarnain, Muhammad Faqih]]>;
<![CDATA[Fadlil, Abdul]]>;
<![CDATA[Riadi, Imam]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4827
<![CDATA[Batik classification depends critically on effective feature extraction to capture the unique geometric and visual characteristics of batik patterns. This study compares two distinct feature extraction methods for batik classification: learned features extracted via a convolutional autoencoder, and shape-based handcrafted features derived from Hu Moments. While autoencoders automatically learn complex latent representations that adapt to intricate pattern variations, Hu Moments provide invariant shape descriptors robust to rotation, scaling, and translation. The methodology involves extracting Hu Moment features and autoencoder latent features from the same batik image dataset, followed by evaluation with identical classifiers to ensure a fair comparison. Experimental results reveal key trade-offs: Hu Moments offer robustness and interpretability in capturing shape geometry, whereas autoencoder features better model complex, non-linear patterns. These findings highlight the complementary strengths of classical and learned feature extraction techniques, offering valuable insights for optimizing batik classification. This research advances feature extraction methodologies in cultural heritage image analysis, with broader applicability to pattern-rich domains like batik classification.]]>
<![CDATA[Ambidextrous AI Governance Model for Advancing State-Owned Bank in Indonesia Digital Transformation Through COBIT 2019 Traditional and DevOps ]]>
<![CDATA[Ramdani, Rama Putra]]>;
<![CDATA[Mulyana, Rahmat]]>;
<![CDATA[Adi, Taufik Nur]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4835
<![CDATA[Integrating artificial intelligence into the banking sector accelerates digital transformation, but it also presents governance challenges, particularly in striking a balance between innovation and regulatory compliance, risk management, and operational control. This research proposes an ambidextrous AI governance model by combining two distinct yet complementary mechanisms from COBIT 2019: the structured, control-oriented Traditional framework and the agile, adaptive DevOps Focus Area. This dual approach enables organizations to pursue innovation and maintain governance stability simultaneously. The study investigates BankCo’s, a state-owned bank in Indonesia that is undergoing a systemic digital transformation and applies the Design Science Research (DSR) methodology with a case study approach. Collecting data through five semi-structured interviews with key IT Governance, Risk, and Compliance stakeholders and triangulated with internal policy documents, annual reports, and audit trails. The analysis identified two prioritized Governance and Management Objectives (GMOs), MEA03 (Managed Compliance with External Requirements) and APO12 (Managed Risk), based on design factors, regulatory alignment (POJK No. 11/2022 and SOE Minister Regulation No. PER-2/MBU/03/2023), and agile governance needs. A maturity gap analysis revealed areas for improvement across people, process, and technology dimensions, with the proposed model raising governance capability from 3.55 to 3.95. The proposed model applies multidimensional prioritization through Resource-Risk-Value (RRV) analysis. This study presents a practical and auditable approach to ethical AI governance that strikes a balance between innovation and accountability. The model supports digital transformation in banks and contributes to information systems governance by linking the ethical use of AI with agile yet compliant practices in regulated environments.]]>
<![CDATA[Prediction of Turbidity Removal Time in Electrocoagulation Wastewater Using Random Forest, XGBoost, and Others: A Data-Driven Information System Approach ]]>
<![CDATA[Suakanto, Sinung]]>;
<![CDATA[See, Tan Lian]]>;
<![CDATA[Shaffiei, Zatul Alwani]]>;
<![CDATA[Firdaus, Taufiq Maulana]]>;
<![CDATA[Lubis, Muharman]]>;
<![CDATA[Bayuwindra, Anggera]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4847
<![CDATA[Electrocoagulation is an effective and environmentally friendly technology for treating wastewater by removing contaminants such as turbidity, heavy metals, and organic compounds. Accurately predicting turbidity removal time is essential for optimizing treatment performance and operational efficiency. However, this is challenging due to complex, nonlinear relationships between multiple parameters including current, voltage, electrode configuration, conductivity, and turbidity removal rate. This study aims to develop a predictive framework by comparing six supervised regression models, namely Linear Regression, Polynomial Regression, Random Forest, Support Vector Regression (SVR), XGBoost, and Long Short-Term Memory (LSTM), using key electrocoagulation parameters. After extensive data preprocessing, a dataset of 281 samples was used for training and validation. Among them, Random Forest achieved the best performance (R² = 0.876, RMSE = 601.15). A data-driven information system is proposed to integrate these predictive capabilities for real-time monitoring and control. By improving turbidity prediction accuracy, the system enables the sustainable utilization of water as a valuable asset, even in its wastewater form. The approach enhances decision-making by providing intelligent feedback for process optimization. This research contributes to the advancement of intelligent, sustainable wastewater treatment systems by integrating machine learning prediction models with practical process control applications in informatics.]]>
<![CDATA[A Random Forest and SMOTE-Based Machine Learning Model for Predicting Recurrence in Papillary Thyroid Carcinoma ]]>
<![CDATA[Kusuma, Edi Jaya]]>;
<![CDATA[Nurmandhani, Ririn]]>;
<![CDATA[Pantiawati, Ika]]>;
<![CDATA[Manglapy, Yusthin Meriantti]]>;
<![CDATA[Widianawati, Evina]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4854
<![CDATA[PTC (Papillary Thyroid Carcinoma) is one subtype of thyroid cancer occurred most frequently in thyroid cancer cases. Although the prognosis of this cancer is typically positive, its recurrence remains a key challenge requiring early detection. This study proposes machine learning models to predict PTC recurrence, explicitly addressing the inherent class imbalance in the recurrence data. This study implemented three supervised learning algorithms, namely Random Forest (RF), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM) with the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset. SMOTE was chosen for its capacity to generate synthetic minority class samples while minimizing information loss, thus effectively addressing class imbalance and improving classification outcomes. Model performance was assessed using accuracy, precision, recall (sensitivity), and F1-score. Among all approaches tested, RF with SMOTE demonstrated superior performance, achieving 0.98 accuracy, perfect precision (1.0), high recall (sensitivity) (0.95), and a strong F1-score (0.97), outperforming previous methods including SMOTEENN-based approaches. The result of this study demonstrates SMOTE specifically outperforms SMOTEENN in this clinical context, likely due to better preservation of subtle prognostic indicators with minimal information loss. This improvement suggests SMOTE's effectiveness in preserving valuable decision boundary information while addressing class imbalance in PTC recurrence prediction. These findings establish RF with SMOTE as a robust and well-balanced approach for predicting PTC recurrence, contributing significantly to the development of more precise and responsive AI-driven decision support tools for thyroid cancer.]]>
<![CDATA[Evaluating Software Quality in a Point of Sales System in a Fast-Food Restaurant Using the McCall Model ]]>
<![CDATA[Ramadhina Assidiq, Wahyu Fidi]]>;
<![CDATA[Putro, Fidi Wincoko]]>;
<![CDATA[Amri, Arni Muarifah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4860
<![CDATA[Software quality is an critical aspect in ensuring system performance and user satisfaction. This study evaluates the quality of the system called Sampos. is a system used by internal employees in managing fast food business operations for record transactions. manage raw material stocks and help track daily reports. The evaluation was conducted using the McCall model, which focuses on five primary quality factors: correctness, reliability, efficiency, integrity, and usability. Each factor is assessed through indicators that reflect the system's performance in that aspect. The measurement stage begins by assigning weights to each indicator based on its level of importance. Then. The quality value of each factor is calculated to get a comprehensive picture of system performance. The results of the evaluation showed that the correctness value was 56.2%, reliability 56%, Integrity 47.8%, and usability 46%, which are generally classified as "Pretty Good.". Meanwhile, the value of the efficiency factor is only 38.2%, so it is categorized as "not good." Overall, the Sampos system obtained an average score of 41% - 60%. This indicates that the system requires improvement, especially in the aspect of efficiency. This study contributes to proving that McCall's method can be used to evaluate applications built without documentation and by a single developer. Therefore, this study contributes a practical case study on the application of McCall’s Model as an effective method for identifying and quantifying quality weakness in small-scale operational systems.]]>
<![CDATA[Quantitative Analysis of the Key Factors Driving Cybersecurity Awareness Among Information Systems Users ]]>
<![CDATA[Helmiawan, Muhammad Agreindra]]>;
<![CDATA[Firmansyah, Esa]]>;
<![CDATA[Herdiana, Dody]]>;
<![CDATA[Akbar, Yopi Hidayatul]]>;
<![CDATA[Subiyakto, A’ang]]>;
<![CDATA[Rahman, Titik Khawa Abdul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4861
<![CDATA[Cybersecurity threats are increasingly complex and widespread, posing significant risks to individuals and organizations. However, many studies tend to address the technological or behavioral aspects separately. The study uses a survey-based quantitative approach using PLS-SEM to analyze key factors that influence cybersecurity awareness, including demographics, training, psychological bias, and organizational culture. The findings suggest that several constructs-such as threat awareness, perceived risk, and education-significantly predict cybersecurity awareness and behaviour. Notably, the model yields an R² value of up to 0.703 with a strong path significance (p < 0.05), which underscores the robustness of the relationship. This study offers an integrated perspective on cybersecurity by bridging the psychological, educational, and organizational dimensions. It highlights cybersecurity awareness as a mediating construct that links upstream factors to secure user behavior-a relational structure that has not been explored in previous research.]]>
<![CDATA[Real-Time Traffic Density and Anomaly Monitoring Using YOLOv8, OpenCV and Pattern Recognition for Smart City Applications in Demak ]]>
<![CDATA[Setiaji, Pratomo]]>;
<![CDATA[Triyanto, Wiwit Agus]]>;
<![CDATA[Nurhaliza, Maulin]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4867
<![CDATA[Urban traffic congestion is a persistent issue in medium-sized cities like Demak, leading to delays and potential accidents. This study presents the development of a real-time vehicle density and anomaly detection system using YOLOv8, combined with OpenCV for video analysis, to monitor traffic flow at strategic entry points of Demak City. The system classifies vehicles into four categories (cars, motorcycles, trucks, buses) and determines their direction by detecting crossing lines. A key feature is the recognition of vehicle patterns, particularly the detection of stopped vehicles, flagging anomalies after 30 seconds of stoppage, with tolerance for temporary detection losses. Traffic data is stored in CSV format, enabling periodic analysis and visualization via an interactive graphical user interface (GUI). Evaluation results show the YOLOv8n model achieves 92.5% precision, 88.3% recall, and 89.7% mean average precision (mAP@0.5), demonstrating improved accuracy and speed over previous YOLO versions. Additionally, the vehicle counting accuracy reaches 94.2% when compared with manual annotations. The proposed system provides a reliable solution for real-time traffic monitoring and early anomaly detection, supporting intelligent transportation systems (ITS) and enabling data-driven traffic management decisions. This research contributes to the advancement of real-time video analytics and pattern recognition for urban traffic control and serves as a scientific reference for the development of smart city infrastructures. Furthermore, this study strengthens the application of pattern recognition in intelligent anomaly detection, providing new insights for researchers in the fields of computer science and informatics.]]>
<![CDATA[Sentiment Analysis of Fizzo Novel Application Using Support Vector Machine and Naïve Bayes Algorithm with SEMMA Framework ]]>
<![CDATA[Pambudi, Satrio]]>;
<![CDATA[Setiaji, Pratomo]]>;
<![CDATA[Triyanto, Wiwit Agus]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4875
<![CDATA[The increasing popularity of digital reading platforms in Indonesia, such as Fizzo Novel, has generated many user reviews that can be analyzed to understand their satisfaction. This study analyzes user sentiment toward Fizzo Novel using the SEMMA (Sample, Explore, Modify, Model, Assess) framework, and compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms. A total of 139,759 reviews were collected from the Google Play Store through web scraping. The data was then processed through normalization, tokenization, lexicon-based sentiment labeling, and feature extraction using TF-IDF. To address class imbalance, the SMOTE technique was applied. The results showed that SVM achieved the highest accuracy, exceeding 96%, with a consistent F1-score across all sentiment classes. In contrast, Naïve Bayes recorded lower accuracy (75.82% before SMOTE and 73.63% after SMOTE), along with a decline in performance for the neutral class. SVM proved more reliable in handling large and imbalanced text data. Practically, the results of this study can help application developers such as Fizzo Novel in automatically understanding user opinions. With an accurate sentiment classification model, developers can monitor reviews in real-time, identify issues such as excessive advertising or an unpopular chapter division system, and design feature improvements based on real user needs. This research also provides a foundation for algorithm selection in future large-scale sentiment analysis projects and recommends SVM as the more appropriate choice in this context.]]>
<![CDATA[Comparative Study of BiLSTM and GRU for Sentiment Analysis on Indonesian E-Commerce Product Reviews Using Deep Sequential Modeling ]]>
<![CDATA[Nasution, Khairunnisa]]>;
<![CDATA[Saddami, Khairun]]>;
<![CDATA[Roslidar, Roslidar]]>;
<![CDATA[Akhyar, Akhyar]]>;
<![CDATA[Fathurrahman, Fathurrahman]]>;
<![CDATA[Aulia, Niza]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4878
<![CDATA[Sentiment analysis plays a crucial role in understanding customer perspectives, especially within Indonesian e-commerce platforms. Despite the success of deep learning in high-resource languages, its application to Indonesian sentiment data remains underexplored. Previous studies using models like BERT-CNN or fine-tuned IndoBERT achieved modest results, highlighting the need for more effective architectures for Indonesian language. This study aims to investigate the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) models in classifying buyers’ sentiment from Indonesian product reviews on the PREDECT-ID dataset comprising 5,400 annotated product reviews. Standard NLP preprocessing techniques—including text normalization, tokenization, stopword removal, and stemming—were applied. Both models were trained using Adam and Stochastic Gradient Descent (SGD) optimizers, and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. The GRU model trained with SGD achieved the highest performance, with an accuracy of 94.07%, precision of 93.84%, recall of 94.53%, and F1-score of 94.18%. Notably, the BiLSTM model combined with SGD resulted in competitive results, achieving 93.61% accuracy and 93.84% F1-score. The results confirm that GRU with SGD optimizer, are highly effective for sentiment classification in Indonesian language datasets. By leveraging deep sequential modeling for a low-resource language, this study contributes to the advancement of scalable sentiment analysis systems in underrepresented linguistic domains. The results contribute to the advancement of NLP systems for Indonesian by providing a benchmark for the future development of sentiment analysis tools in low-resource languages.]]>
<![CDATA[Palm Oil Seed Origin Classification Based on Thermal Images and Agricultural Data Using Convolutional Neural Network ]]>
<![CDATA[Natha, Si Gede Ngurah Chandra Adi]]>;
<![CDATA[Wirayuda, Tjokorda Agung Budi]]>;
<![CDATA[Wijaya, Rifki]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.4.4880
<![CDATA[The traceability of palm oil seed origins plays a vital role in ensuring transparency, legality, and sustainability across the palm oil supply chain. Recent advances in deep learning have created new opportunities to improve classification systems by leveraging both visual and contextual data. This study proposes a deep learning-based model for classifying the origin of palm oil seeds by integrating thermal imagery with agricultural data. Two convolutional neural network (CNN) architectures, ResNet50 and MobileNet, were evaluated under three experimental setups: using only thermal images, combining thermal images with agricultural features (socio-economic, soil, and spectral fruit characteristics), and applying hyperparameter tuning to the best-performing model. The results show that ResNet50 consistently outperformed MobileNet, particularly in multimodal configurations. The highest performance was achieved using ResNet50 with the Adam optimizer, a learning rate of 0.001, and a batch size of 16, resulting in training accuracy of 99.75%, validation accuracy of 99.92%, and test accuracy of 100.00%. Evaluation metrics confirmed the model’s robustness with precision, recall, and F1-score all reaching 100.00%. This research highlights the significant potential of combining thermal imagery and agricultural data in CNN-based models for accurate and reliable classification of palm oil seed origins. The approach can support traceability systems in the palm oil industry, offering a scalable and data-driven solution for ensuring supply chain integrity and sustainability.]]>
