Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
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<![CDATA[Classifying Public Complaints in Denpasar: a Comparative Study of CNN, RNN, LSTM, and Stacking Deep Learning Models ]]>
<![CDATA[Dharmendra, I Komang]]>;
<![CDATA[Wijaya, I Made Pasek Pradnyana]]>;
<![CDATA[Putra, I Made Agus Wirahadi]]>;
<![CDATA[Atmojo, Yohanes Priyo]]>;
<![CDATA[Pratiwi, Luh Putu Safitri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4153
<![CDATA[The process of lodging complaints represents a complex behavioral construct, influenced by the interplay of emotional states, sociocultural factors, and situational contexts. It functions as a pivotal channel for citizens to express dissatisfaction regarding the quality of governmental services. This research aims to optimize public complaint management by leveraging deep learning-based text classification on citizen submissions collected from the Denpasar City Complaint Web Portal. The methodological approach integrates several neural network models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, further enhanced by a Stacking ensemble technique that amalgamates the strengths of each architecture. The dataset consists of 10,302 textual records, categorized into four semantic classes: Complaints, Suggestions, Inquiries, and Information. To improve the robustness and reliability of the classification, advanced preprocessing steps were implemented, including the application of the Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance and the utilization of Term Frequency–Inverse Document Frequency (TF-IDF) for extracting the most informative textual features. Empirical results demonstrate that the Stacking ensemble model significantly outperforms individual baseline models, achieving an accuracy of 77.83%, with recall and F1-score values of 74.38%. These findings highlight the effectiveness of ensemble deep learning approaches in multiclass complaint classification, thereby supporting improvements in public service delivery and fostering greater governmental transparency. Ultimately, this study contributes to the field of automated text classification by illustrating the potential of advanced neural architectures to enhance citizen participation and institutional accountability.]]>
<![CDATA[Comparative Analysis of Face Mask Detection using Lightweight CNN and Bag of Visual Word-based Classifier for Real-Time Surveillance ]]>
<![CDATA[Candradewi, Ika]]>;
<![CDATA[Aldino Ardi S, Bakhtiar]]>;
<![CDATA[Harjoko, Agus]]>;
<![CDATA[Dharmawan, Andi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4461
<![CDATA[Face mask detection has become increasingly important across various sectors, including healthcare, food processing industries, and public safety, to ensure adherence to health and hygiene protocols and minimize the risks of contamination. Manual supervision of mask usage is often inefficient, labor-intensive, and prone to inconsistency. To address this challenge, this study proposes an automated face mask detection system utilizing computer vision technology, designed for real-time monitoring on resource-limited edge devices, such as the Raspberry Pi 4 with a Google Coral USB Accelerator. The system integrates Multi-task Cascaded Convolutional Neural Networks (MTCNN) for face detection and a modified lightweight Convolutional Neural Network (CNN) for binary mask classification. Deployed via a web-based platform, it captures images of non-compliant individuals and triggers alerts. To evaluate model performance, the modified CNN is compared with the Bag of Visual Words (BoVW) method using SVM and MLP classifiers on two datasets: the 12k-Face Mask Dataset (Kaggle) and a newly proposed dataset. The CNN model demonstrated higher classification performance than both BoVW-SVM and BoVW-MLP, with AUC improvements of 49% and 43% on the proposed and 12k-Face Mask datasets, respectively. This study contributes to the advancement of computer vision-based public health monitoring by offering a robust, scalable, and real-time face mask detection system. The findings highlight the practical advantages of deep learning approaches over traditional feature extraction techniques, supporting the development of intelligent, automated surveillance systems and policy enforcement in high-risk environments, which will facilitate future advancements in AI-driven public safety solutions.]]>
<![CDATA[Article Retrieval And Automatic Summarization System Using BERT-Based Neural Network Model On Chatbot ]]>
<![CDATA[Awaluddin, Muhammad Ghazali]]>;
<![CDATA[Aksa, Muhammad]]>;
<![CDATA[Arifky, Reza]]>;
<![CDATA[Bakri, Muhammad Fajar]]>;
<![CDATA[Surianto, Dewi Fatmarani]]>;
<![CDATA[Edy, Marwan Ramdhany]]>;
<![CDATA[Zain, Satria Gunawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4463
<![CDATA[The rapid growth of online scientific publications presents challenges in efficiently filtering relevant information. Many search systems still rely on keyword matching, which is often ineffective in understanding the context of user queries. This study develops a chatbot system based on BERT (Bidirectional Encoder Representations from Transformers) for scientific article retrieval and automatic summarization. The system is designed to comprehend user intent and generate summaries of relevant articles. The evaluation was conducted on a dataset of 506 scientific articles, assessing search accuracy based on topic, abstract, author name, and time range. Results show 100% accuracy in searches by author and abstract, with varying performance in topic-based and time-based searches. This system is expected to enhance the efficiency and relevance of scientific literature retrieval and support the productivity of researchers across various fields.]]>
<![CDATA[Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 ]]>
<![CDATA[Hasanudin, Muhaimin]]>;
<![CDATA[Afiyati, Afiyati]]>;
<![CDATA[Budiarto, Rahmat]]>;
<![CDATA[Wahab, Abdi]]>;
<![CDATA[Jokonowo, Bambang]]>;
<![CDATA[Indrianto, Indrianto]]>;
<![CDATA[Yosrita, Efy]]>;
<![CDATA[Hanifah, Nurul Afif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4574
<![CDATA[This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.]]>
<![CDATA[User Experience Analysis of Learning Management System (LMS) SINAU to Support Learning with MERDEKA Flow Using UX Curve Method ]]>
<![CDATA[Yarsasi, Sri]]>;
<![CDATA[Tahyudin, Imam]]>;
<![CDATA[Hariguna, Taqwa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4579
<![CDATA[The rapid development of information technology has driven transformation in education, including the use of Learning Management Systems (LMS) to facilitate independent and flexible learning aligned with the Merdeka Curriculum. This study aims to evaluate the user experience (UX) of the Sinau LMS at SMA Negeri 1 Sidareja using the UX Curve method, which tracks changes in user perceptions over time. The research involved 20 grade XII students who had used the LMS for at least three months. Data were collected through initial questionnaires, interviews, UX curve drawings, and final questionnaires, focusing on five main UX aspects: General UX, Attractiveness, Ease of Use, Utility, and Degree of Usage. The analysis of 100 curves revealed that more than half of the respondents experienced a decline in user experience quality, particularly in Ease of Use, General UX, and Degree of Usage, due to issues such as an unattractive interface, navigation challenges, and limited feature relevance. Conversely, a minority showed improved perceptions as they adapted and became more familiar with the system. These findings highlight the need for continuous improvement of the LMS's interface design and features to enhance user satisfaction and learning effectiveness. The study contributes theoretically by demonstrating the application of the UX Curve in educational systems and practically by providing recommendations for refining LMS development to better support the Merdeka Curriculum.]]>
<![CDATA[Performance Comparison of SVM in Sentiment Analysis of Israel-Palestine Comments Using Lsa and Word2vec ]]>
<![CDATA[Akbar, Muh. Arsan]]>;
<![CDATA[Syam, Abd. Azis]]>;
<![CDATA[Al Amanah, Muh. Nur Hidayat]]>;
<![CDATA[Risal, Andi Akram Nur]]>;
<![CDATA[Surianto, Dewi Fatmarani]]>;
<![CDATA[Budiarti, Nur Azizah Eka]]>;
<![CDATA[Wahid, Abdul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4601
<![CDATA[This study compares two feature extraction techniques, namely Latent Semantic Analysis (LSA) and Word2Vec, in the sentiment classification of comments related to the Israeli-Palestinian conflict using Support Vector Machine (SVM). The dataset consists of 1000 YouTube comments and 158 news paragraphs, categorized into pro and con Palestinian sentiments. The preprocessing process includes casefolding, special character and stopword removal, lemmatization, and tokenization. The results show that SVM with Word2Vec has better performance than SVM with LSA in the classification of positive and negative comments. SVM model with Word2Vec recorded a precision value of 92% and F1-Score of 93% on negative comments. Meanwhile, SVM with LSA recorded 90% precision and 92% F1-Score. On positive comments, SVM with Word2Vec recorded 92% recall and 93% F1-Score. While SVM with LSA recorded 89% recall and 91% F1-Score. Word2Vec's strength lies in its ability to capture word context and nuance more effectively, thanks to training using richer contextualized comment and news data. In conclusion, although both methods show good ability in sentiment classification, the use of Word2Vec provides more consistent and accurate results. This research contributes to the advancement of sentiment classification methods in the context of complex socio-political issues and can serve as a reference for applying machine learning to more accurate and contextual public opinion analysis.]]>
<![CDATA[Insulator Defect Detection Based On Image Processing Using A Modified YOLOv8n Model ]]>
<![CDATA[Muzaki, Muchamad Arfim]]>;
<![CDATA[Subiyanto, Subiyanto]]>;
<![CDATA[Anantyo, Andika]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4679
<![CDATA[Insulators are critical components in power transmission and distribution systems, where any defects can lead to severe operational failures and power outages. To enhance inspection efficiency, unmanned aerial vehicles (UAVs) are increasingly used for aerial monitoring. However, the quality of images captured by drones is often compromised due to hardware limitations, motion blur, and complex environmental backgrounds, which significantly reduces the performance of deep learning-based defect detection methods. This study proposes an improved insulator defect detection model based on the YOLOv8n architecture, optimized for accuracy and efficiency in low-quality image scenarios and suitable for deployment in resource-constrained environments. The model introduces two major modifications. First, a Slim-Neck module employing Ghost-Shuffle Convolution (GSConv) replaces standard convolutions to substantially reduce computational cost while preserving rich feature representations. Second, an Efficient Multi-Scale Attention (EMA) module is integrated into the neck to enhance multi-scale feature fusion by maintaining per-channel information without dimensionality reduction, improving the model’s ability to extract discriminative features. Experimental results demonstrate that the proposed model achieves a precision of 92.0%, recall of 88.6%, mAP@0.5 of 92.1%, and an inference speed of 161.29 FPS. Furthermore, it reduces parameter count by 10.8% and computational load by 8.6% compared to the baseline, validating its suitability for real-time UAV-based inspections. The model also outperforms existing methods in detecting insulator defects, particularly in challenging conditions involving blur and complex backgrounds.]]>
<![CDATA[Natural Language Understanding for School Bullying Detection and Consultation: A DIET Classifier Approach in RASA Framework ]]>
<![CDATA[Irawan, Yoan Freddy]]>;
<![CDATA[Hadiono, Kristophorus]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4730
<![CDATA[This research presents the development and implementation of a DIET classifier-based chatbot system using the RASA Framework to handle bullying reports at SMP Negeri 3 Ungaran. The system aims to provide 24/7 automated counseling support service, addressing the limitations of traditional human-to-human support systems that often result in delayed responses and reduced user satisfaction. The model was trained using a structured dataset comprising 61 dialogue examples collected through interviews with experienced guidance and counseling teachers, capturing authentic student communication patterns related to bullying issues. The evaluation results demonstrate exceptional performance, achieving 100% accuracy across 12 intent categories, with perfect precision and recall scores. The system successfully distinguishes between various emotional states and counseling needs, providing appropriate responses with high confidence levels. The intent categories include emotional expressions (merasa_dibully, merasa_sedih, merasa_takut), support-seeking behaviors (butuh_nasihat, ingin_bicara_dengan_guru), and conversational elements, ensuring comprehensive coverage of bullying-related communication scenarios. This implementation proves that AI-driven solutions can effectively support educational institutions in providing immediate, accessible counseling assistance while maintaining accuracy in emotional support and bullying prevention. This research contributes to the field of computer science by demonstrating the practical application of natural language understanding frameworks in sensitive educational contexts, advancing AI-driven counseling systems that can be scaled across educational institutions. The study provides a replicable methodology for developing culturally-sensitive AI applications in educational environments, particularly valuable for institutions in developing countries with limited digital mental health resources.]]>
<![CDATA[Enhancing Question Classification in Educational Chatbots Using RASA Natural Language Understanding ]]>
<![CDATA[Christanto, Zaenur Dwi]]>;
<![CDATA[Hadiono, Kristophorus]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4732
<![CDATA[This research develops a chatbot model based on Rasa Framework to understand and respond to questions related to informatics learning, addressing the critical need for personalized AI-driven educational tools in Indonesian secondary education. The model is trained to recognize various patterns of student questions about informatics materials, especially the topic of number conversion. Using Natural Language Understanding (NLU), the chatbot model is developed to process natural language and classify the intent of student questions. Evaluation of the model using the confusion matrix showed good performance with 91.5% accuracy, 94.4% average precision, and 100% recall. The test results showed that the model was able to correctly classify various types of intent, where eight out of nine intents achieved a perfect precision of 100%, with one intent, tutorial_calculation_octal_to_decimal, having a precision of 50%. The 100% recall across all intents demonstrates the model's comprehensive ability to identify all cases requiring responses, ensuring no student queries are missed. This research significantly contributes to computer science education by validating RASA's effectiveness for domain-specific NLU in low-resource educational settings, providing a scalable foundation for AI-based learning assistance tools that can enhance digital literacy and computational thinking skills among junior high school students.]]>
<![CDATA[Buffalo Price Estimation Using YOLOv8 And Image Thresholding ]]>
<![CDATA[Amelia, Amelia]]>;
<![CDATA[Firgiawan, Wawan]]>;
<![CDATA[Sulfayanti, Sulfayanti]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.1.4749
<![CDATA[The skin color pattern of buffaloes can determine their market price, especially for traditional ceremonial purposes that involve buffaloes. Currently, the pricing of buffaloes is still done subjectively by sellers or buyers, resulting in inconsistencies in price determination. This study proposes the development of a system to estimate the price of buffaloes based on their type and the percentage of light and dark skin, specifically for the Saleko buffalo type. The algorithm used to recognize buffalo types is YOLOv8, which was trained to detect four classes: Lotongboko, Saleko, Bonga, and Other types. The model was trained over 100 epochs using the Adam optimizer and hyperparameters. A thresholding method was applied to identify the percentage of black and white on the Saleko buffalo images that were successfully detected by YOLOv8. If the light skin percentage exceeds 80%, the buffalo is estimated to be worth 800 million rupiah. Otherwise, the Saleko buffalo is estimated at 300 million rupiah. The YOLOv8 training achieved a highest mAP value of 97.8%, with steadily decreasing loss and increasing metrics at each iteration, indicating a successful training process with strong detection performance. The price estimation model achieved an accuracy of 76.3% based on 55 tested images. Estimation errors were caused by low image resolution and poor lighting quality. This study provides insights into the application of technology for buffalo price estimation through digital image processing.]]>
