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[The Role Of A Decision Support System In Enhancing The Management Of Sexual Violence Cases In Higher Education Using The Saw Method Through An Android-Based Application ]]>
<![CDATA[Wahyuni, Elyza Gustri]]>;
<![CDATA[Wahyudi, Rian Tri]]>;
<![CDATA[Fadhillah, Lalam Fathonah]]>
<![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.2025.6.3.4761
<![CDATA[The rising prevalence of sexual violence in higher education institutions demands urgent attention, highlighting the need for an efficient and responsive reporting system. Android-based applications for reporting sexual violence play a vital role in addressing this issue. This research proposes an application designed to provide ease of access and usability, enabling victims or witnesses to promptly submit reports supported by video, audio, and real-time GPS data. Such empirical evidence increases the likelihood of successful follow-up actions and strengthens legal claims against perpetrators. Timely responses are especially critical for the Sexual Violence Prevention and Handling Task Force (PPKS) to mobilize campus security teams effectively and reduce long-term trauma experienced by victims. An integral component of the application is a Decision Support System (DSS) that utilizes the Simple Additive Weighting (SAW) method to assess the severity of reported cases—categorized into mild, moderate, or severe. This system facilitates faster and more accurate decision-making during the investigation and handling phases. Functional and case testing resulted in 100% success, aligning perfectly with manual calculations and real-world scenarios. The urgency of this research lies in the pressing need for a reporting system that is not only reactive but also proactive in preventing sexual violence. The application demonstrates strong potential to support systemic reform in campus reporting mechanisms, enhance victim trust in reporting processes, and shift the paradigm from reactive intervention to preventive action. Ultimately, this research contributes to building a safer, more responsive, and survivor-centered campus environment.]]>
<![CDATA[Optimization Artificial Neural Network (ANN) Models with Adam Optimizer to Improve Customer Satisfaction Business Banking Prediction ]]>
<![CDATA[Ifriza, Yahya Nur]]>;
<![CDATA[Mandaya, Yusuf Wisnu]]>;
<![CDATA[Sanusi, Ratna Nur Mustika]]>;
<![CDATA[Febriyanto, Hendra]]>;
<![CDATA[Jabbar, Abdul]]>;
<![CDATA[Kamaruddin, Azlina]]>
<![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.2025.6.3.4776
<![CDATA[Customer satisfaction prediction is critical for business banking to retain clients and optimize services, yet existing models struggle with imbalanced data and suboptimal convergence. Traditional approaches lack adaptive learning mechanisms, limiting accuracy in real-world applications. This study developed an optimized Artificial Neural Network (ANN) model using the Adam algorithm to improve prediction accuracy for banking customer satisfaction. We trained an ANN on the Santander Customer Satisfaction Dataset (76,019 entries, 371 features) with Adam optimization. Preprocessing included normalization, removal of quasi-constant features, and an 80-20 train-test split. Adam’s adaptive learning rates and momentum were leveraged to address gradient instability. The model achieved 95.82% accuracy, 99.99% precision, 95.83% recall, a 97.87% F1-score, and 0.82 AUC, outperforming traditional optimizers like SGD. Training loss reduced by 30% with faster convergence. This work demonstrates Adam’s efficacy in handling imbalanced banking data, providing a scalable framework for customer analytics. The results advance computer science applications in fintech by integrating adaptive optimization with deep learning for high-stakes decision-making. This research contributes to the growing body of knowledge in machine learning applications for business analytics and provides a valuable framework for improving customer satisfaction prediction models in various industries and the advancement of deep learning applications in business intelligence, particularly in banking service quality prediction.]]>
<![CDATA[Classification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural NetworkClassification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural Network ]]>
<![CDATA[Arifin, Nurhikma]]>;
<![CDATA[Insani, Chairi Nur]]>;
<![CDATA[Milasari, Milasari]]>;
<![CDATA[Rusman, Juprianus]]>;
<![CDATA[Upa, Samrius]]>;
<![CDATA[Utama, Muhammad Surya Alif]]>
<![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.2025.6.3.4781
<![CDATA[Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.]]>
<![CDATA[Depression Detection using Convolutional Neural Networks and Bidirectional Long Short-Term Memory with BERT variations and FastText Methods ]]>
<![CDATA[Widjayanto, Leonardus Adi]]>;
<![CDATA[Setiawan, Erwin Budi]]>
<![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.2025.6.3.4874
<![CDATA[Depression has become a significant public health concern in Indonesia, with many individuals expressing mental distress through social media platforms like Twitter. As mental health issues like depression are increasingly prevalent in the digital age, social media provides a valuable avenue for automated detection via text, though obstacles such as informal language, vagueness, and contextual complexity in social media complicate precise identification. This study aims to develop an effective depression detection model using Indonesian tweets by combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). The dataset consisted of 58,115 tweets, labeled into depressed and non-depressed categories. The data were preprocessed, followed by feature extraction using BERT and feature expansion using FastText. The FastText model was trained on three corpora: Tweet, IndoNews, and combined Tweet+IndoNews corpus; the total corpus will be 169,564 entries. The best result was achieved by BiLSTM model with 84.67% accuracy, a 1.94% increase from the baseline, and the second best was the BiLSTM-CNN hybrid model achieved 84.61 with an accuracy increase of 1.7% from the baseline. These result indicate that combining semantic feature expansion with deep learning architecture effectively improves the accuracy of depression detection on social media platforms. These insights highlight the importance of integrating semantic enrichment and contextual modeling to advance automated mental health diagnostics in Indonesian digital ecosystems.]]>
<![CDATA[A Study Concentration Selection With a C4.5 Algorithm, KNN, and Naive Beyes ]]>
<![CDATA[Busyro, Muhammad]]>;
<![CDATA[Astuti, Tri]]>;
<![CDATA[Astrida, Deuis Nur]]>;
<![CDATA[Arsi, Primandani]]>;
<![CDATA[Subarkah, Pungkas]]>
<![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.2025.6.4.4157
<![CDATA[The course of concentration is a crucial aspect for students at the university amikom purwokerto.This decision doesn't just affect their academic journey., but also determine their readiness in the face of the working world.Various factors that affect the concentration selection, the challenges that students face, as well as solutions to help them choose concentrations that fit their interests and career goals.There are still many students who have been confused in deciding which courses best fit their interests and career goals..This confusion is often caused by a lack of adequate information and proper guidance. This study attempts to analyze the lecture amikom purwokerto concentration of students in the universities of the use of the method c4.5 algorithm 3, k-neareset naighbors and naïve beyes. Academic student data used as the basis analysis to determine the dominance in the lecture concentration.Of the result of the research uses phon 60,24 % decision is, there are using k-neareset naighbors 75.36 % and use naïve beyes 100,00 % there are, the prediction could be the basis for deciding the lecture the concentration by mainstream student.The result is expected to help the university in recommended it to students study concentration related to the election.]]>
<![CDATA[Spice Type Recognition Based on Shape and Color Features Using K-Nearest Neighbor and Fuzzy Methods ]]>
<![CDATA[Syofyan, Sonia]]>;
<![CDATA[Fitria, Liza]]>;
<![CDATA[Munawir, Munawir]]>
<![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.2025.6.4.4456
<![CDATA[Spices are natural ingredients that play an important role in everyday life, especially in traditional medicine. With a variety of shapes and colors, spices are often difficult to distinguish from one another. This research aims to classify spice types based on shape and color features using K-Nearest Neighbor (K-NN) and Fuzzy methods. This research will limit the recognition of spice types to 10 specific types of spices, namely ginger, turmeric, star anise, coriander, pepper, nutmeg, galangal, cinnamon, cloves, and candlenut. Spice type recognition will be done based on shape, color and texture features extracted using 300 training data images. The application of the K-NN method and Fuzzy logic allows flexible processing of color features (HSV). Fuzzy logic classifies spice color characteristics by generating a color score (color_score), which is then used to better interpret and distinguish spice colors for the classification process between test data and training data by the K-NN method. The test results show that from a total of 100 test data, the system successfully classifies spices with an accuracy rate of 77%.]]>
<![CDATA[Analyzing ChatGPT’s Impact on Graduates’ Communication, Collaboration, and Logical Thinking Skills Using an Extended Technology Acceptance Model ]]>
<![CDATA[Hasri, Raja Alan]]>;
<![CDATA[Miranda, Eka]]>
<![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.2025.6.4.4688
<![CDATA[The rapid rise of ChatGPT in Indonesia—now the third-highest user base worldwide—raises questions about its impact on essential soft skills for new graduates. Recent evidence warns that while ChatGPT supports academic and professional tasks, it may also reduce critical thinking, collaboration, and communication if not properly guided. This study aims to evaluate how ChatGPT usage affects communication, collaboration, and logical thinking skills among recent graduates in Jabodetabek. A cross-sectional survey of 384 respondents was conducted, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The modified Technology Acceptance Model (TAM) demonstrated strong explanatory power, with R² values of 0.830 for Behavioral Intention, 0.699 for Actual Use, and 0.651 for Attitude Toward Use. Hypothesis testing confirmed significant effects, including Perceived Ease of Use on Perceived Usefulness (β = 0.946; t = 172.023; p < 0.001) and Behavioral Intention on Actual Use (β = 0.836; t = 50.416; p < 0.001). Positive attitudes toward ChatGPT were strongly associated with enhanced teamwork, communication, and logical reasoning. This study contributes to the discourse on digital literacy and educational technology in Southeast Asia, demonstrating that ChatGPT can strengthen graduate employability when integrated with proper guidance and ethical use. The findings provide practical implications for computer science and education fields, offering a framework for balancing AI adoption with the preservation of critical human skills.]]>
<![CDATA[Development of an AI Governance Model for Higher Education Using the Capability Maturity Model Integration (CMMI) ]]>
<![CDATA[Walhidayah, Irfan]]>;
<![CDATA[Surendro, Kridanto]]>
<![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.2025.6.4.4709
<![CDATA[The increasing adoption of Artificial Intelligence (AI) in higher education presents strategic opportunities for institutional transformation, while introducing complex challenges related to ethics, accountability, transparency, and regulatory compliance. Responding to the growing complexity of AI implementation in academic environments , this study proposes a governance model for AI named GOVAIHEI (Governance of Artificial Intelligence for Higher Education Institutions), conceptualized using the Capability Maturity Model Integration (CMMI) framework. The model was developed using the Design Research Methodology (DRM), which consists of four stages: Research Clarification, Descriptive Study I, Prescriptive Study, and Descriptive Study II. GOVAIHEI encompasses five primary domains: Data and Information, Technology and Infrastructure, Ethics and Social Responsibility, Regulation and Compliance, and Monitoring and Evaluation. Each domain is articulated into capability areas and measurable practices, assessed using the tiered NPLF scale (Not, Partial, Largely, Fully Achieved) to determine institutional capability and maturity levels. The model was validated through expert judgment by three domain specialists, confirming its relevance, methodological soundness, and alignment with CMMI principles. A web-based evaluation system was also developed using Laravel, PostgreSQL, Redis, and Nginx, enabling structured, efficient, and automated assessments. Implementation in a case study at Institute XYZ revealed an initial maturity level (Level 1) with development goals toward Level 3 (Defined). The findings demonstrate a practical foundation for navigating the multifaceted nature of AI adoption in higher education through a structured and adaptable governance approach, which aligns with the increasing demand for robust digital governance frameworks in technology-driven environments. ]]>
<![CDATA[Evaluation IT Goverment Capabilities of the Facematch RIM Polri Recruitment System Using the COBIT 5 Framework ]]>
<![CDATA[Iswara, Made Wikrama Dana]]>;
<![CDATA[Anggraeni, Sita]]>
<![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.2025.6.4.4752
<![CDATA[One of the technology implementations used by the Government, especially the latest POLRI in the Facematch RIM POLRI system in the recruitment of POLRI new members, has been designed to prevent fraud in the Police recruitment process by recording the faces of prospective members as a valid identity. To determine the capabilities of this system, qualitative data collection was carried out through interviews with related parties and observation of overall system governance activities. The operational implementation of this system has several findings, including the recording process and image quality monitoring mechanism, the continuity of the capture process, to network and infrastructure constraints. The findings are mapped within the COBIT 5 framework domain to determine the gap for improvement in Acceptance System Governance based on Facematch RIM POLRI at POLRI. These findings can contribute to the improvement of IT Governance practices in the POLRI Admissions System in Government in line with the COBIT 5 framework domains and are expected to provide strategic recommendations to overcome the challenges faced and improve the efficiency and effectiveness of the system in supporting organizational goals.]]>
<![CDATA[Improving Extreme Gradient Boosting Model for Heart Disease Prediction Using SMOTE for Class Imbalance ]]>
<![CDATA[Rohmayani, Dini]]>;
<![CDATA[Sugianto, Castaka Agus]]>;
<![CDATA[Perdana, Rangga Satria]]>;
<![CDATA[Nafea, Mohammed Mansoor]]>
<![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.2025.6.4.4753
<![CDATA[The goal of this study is to come up with an intelligent predictive model that can classify the severity of heart disease. The model will employ both XGBoost and oversampling to resolve the problem of data imbalance. In addition, the model will be implemented for real-world application using an interactive interface. The study uses the UCI Heart Disease dataset, which includes many clinical features. Preprocessing involves handling missing values, removal of features with a substantial fraction of missing values, and the use of SMOTE resampling for learning from class-balanced instances. The main classifier that was used for the research purposes was the XGBoost classifier, while the dataset was split 80:20 for training and testing purposes. For ease of individual-level real-time testing of the predictions, the model is implemented through Streamlit. The XGBoost model worked extraordinarily well, with the accuracy standing at 92%, as did precision along with recall, as well as the F1-score, being 92%. These findings clearly outperform other current studies of the same sort that have made use of alternative classifiers. In addition, its deployment using Streamlit makes it even more clinically applicable. Innovation The novelty of the research lies in the combined application of SMOTE with XGBoost, enabling effective classification under imbalanced conditions, along with the real-time implementation using Streamlit for user-level predictions. The model is of high value for early identification and stratification of the severity of heart disease in clinical decision support settings.]]>
