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[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.]]>
<![CDATA[A Morphology Processing Approach For Image Processing In Cancer Diagnosis ]]>
<![CDATA[Hutahaean, Jonner]]>;
<![CDATA[Widhiyasana, Yudi]]>;
<![CDATA[Ramdhani, Algi Fari]]>
<![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.2026.7.1.4783
<![CDATA[Early tumor detection is critical for improving cancer treatment outcomes, enabling less invasive and more cost-effective interventions. However, limited access to pathologists and high patient volumes reduce diagnostic efficiency, particularly in underserved regions, underscoring the urgency for computational support tools. While deep learning has shown promise in tumor detection, it requires extensive annotated datasets, high computational resources, and long processing times, making it less feasible in certain contexts.This study introduces a lightweight image processing approach for detecting tumors in Hematoxylin and Eosin (H&E)–stained histopathology images without deep learning. Using data from the PAIP 2023 Tumor Cellularity challenge, the proposed method applies histogram equalization, bilateral filtering, morphological transformations, bitwise operations, and an improved algorithm adapted from prior research. The method achieves IoU (Intersection of Union) of 0.93 compared to pathologist-determined ground truth. The results indicate that this approach can serve both as a standalone segmentation tool and as a preprocessing stage for deep learning pipelines, enhancing accessibility, reducing computational costs, and supporting broader adoption of computer-aided pathology in resource-limited settings.]]>
<![CDATA[Implementation and Analysis of QR Code Phishing Attacks on Indonesian Internet Banking Using Attack Tree and Time-Based Metrics ]]>
<![CDATA[Yuniati, Shavira Eka]]>;
<![CDATA[Widjajarto, Adityas]]>;
<![CDATA[Hediyanto, Umar Yunan Kurnia Septo]]>
<![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.2026.7.1.4819
<![CDATA[The development of technology in Internet banking services facilitates customers’ financial transactions. However, this can also create opportunities for cybercrime threats, including a quishing attack. A quishing attack is a type of phishing attack that uses a QR Code to redirect victims to a fake website to steal sensitive information. This research formulates an attack tree model for quishing attacks by combining OSINT, social engineering, and QR Code exploitation, structured using data flow diagrams and evaluated with time-based metrics. The attack was simulated as a Proof of Concept (PoC) to realistically depict the stages of exploitation. Results from the experiments show that the fastest attack path using the OSINT tool Truecaller, the social engineering tool SEToolkit, and the QR Code tool Qrencode takes 248.31 seconds. This path is considered more efficient, outperforming the second fastest combination, which uses the OSINT tool Find Mobile Number Location by 25.15 seconds, with a total time of 273.46 seconds. Truecaller’s advantage lies in its ability to obtain data quickly without requiring a geographic location process like the Find Mobile Number Location tool. This approach shows that banking institutions can integrate time-based metric attack trees to assess vulnerability response times, simulate realistic threat scenarios, and develop more effective incident response strategies to prevent unauthorized access during quishing attacks.]]>
<![CDATA[Integrating Digital Governance for Disaster Resilience: A TOGAF 10-Based Enterprise Architecture for Coastal Villages in Eretan Wetan, Indonesia ]]>
<![CDATA[Hasanah, Sofiyatun]]>;
<![CDATA[Fajrillah, Asti Amalia Nur]]>;
<![CDATA[Mukti, Iqbal Yulizar]]>
<![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.2026.7.1.4837
<![CDATA[Indonesia's coastal regions face significant challenges due to climate change and natural disasters such as coastal abrasion, tidal flooding, and high waves, which impact the social and economic sustainability of rural communities. One of the vulnerable areas is Eretan Wetan Village, Kandanghaur Subdistrict, Indramayu Regency, which has a low score of 5.88 for SDGs Goal 13 (Climate Action Village). This studyse aims to design an Enterprise Architecture to support the implementation of a more effective, structured, and sustainable Coastal Disaster-Resilient Village (Destana). The design adopts the TOGAF 10 framework, covering the phases of Preliminary, Architecture Vision, Business Architecture, Data Architecture, Technology Architecture, Opportunities and Solutions, and Migration Planning. The outcome of this study includes an architectural blueprint and IT roadmap, which are expected to serve as a strategic guide for the village government in developing an integrated and adaptive disaster management system. Through this approach, Eretan Wetan Village is expected to enhance disaster preparedness, strengthen stakeholder coordination, and contribute to the achievement of sustainable development goals. This study shows how important it is in the field of information systems to solve real-world problems in rural regions through digital system integration. ]]>
<![CDATA[Integration of Thermal Images and Agricultural Data for Multi-Class Classification of Palm Seed Origin using MobileNet ]]>
<![CDATA[Nurrahman, Yusuf Abidin]]>;
<![CDATA[Wijaya, Rifki]]>;
<![CDATA[Wirayuda, Tjokorda Agung Budi]]>
<![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.2026.7.1.4879
<![CDATA[This research develops a palm kernel origin classification model by combining thermal images and numerical agricultural data using MobileNet architecture. The quality of palm kernels is highly influenced by origin and environmental conditions, but manual visual identification is difficult. Therefore, a machine learning-based approach is applied to improve classification accuracy. The dataset consists of 7.257 thermal images representing 73 seed origin classes, as well as supporting data in the form of soil, fruit, and socioeconomic information collected from plantations in Aceh, Indonesia. The MobileNet model was tested in two scenarios: using only thermal images, as well as a combination of thermal images with agricultural data. Results show that data integration provides significant performance improvement. The best model was obtained from MobileNet V3-Large with the optimal hyperparameter configuration (batch size 16, learning rate 0.001, and optimizer Adam), resulting in test accuracy of 99.04%, validation 97.25%, and training 98.77%. This finding opens up opportunities for the application of real-time classification technology in the plantation environment, especially to support precision and sustainable agriculture.]]>
<![CDATA[Web-Based Attendance and Leave Management System with Sequential Search Implementation at Tondano Religious Court ]]>
<![CDATA[Rorimpandey, Gladly C.]]>;
<![CDATA[Rombon, Natalia Arsel]]>;
<![CDATA[Kainde, Quido C]]>
<![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.2026.7.1.4896
<![CDATA[Based on the problems found in the leave application and attendance recording processes at the Religious Court of Tondano, this research was conducted to address these issues through the implementation of the Sequential Search Algorithm in a web-based application. This application aims to manage leave requests and employee attendance efficiently. The existing manual processes, such as paper-based attendance sheets and leave forms requiring multiple physical signatures, have proven to be inefficient, prone to manipulation, and at risk of data loss. To solve these issues, the application was developed using the Rapid Application Development (RAD) method, which enables a faster process in planning, designing, and deploying the system. The implementation of the Sequential Search Algorithm allows for efficient data retrieval, particularly in searching employee leave data without requiring data sorting, thereby simplifying the search process. In addition, the system includes a location-based attendance feature to prevent fraud, ensuring that employees are present within a defined radius before marking attendance. This online system benefits both permanent and non-permanent employees by providing a more secure, accurate, and accessible record of attendance and leave history. This research contributes to the digital transformation efforts of the Religious Court of Tondano by offering an integrated system that supports internal Administration processes and improves overall service quality through the use of information technology.]]>
<![CDATA[Empirical Evaluation of IndoBERT and LSTM for Sentiment Analysis of Tourism Reviews: A Data-Driven Study on Kenjeran Park ]]>
<![CDATA[Purwanto, Devi Dwi]]>
<![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.2026.7.1.4901
<![CDATA[Tourism plays a pivotal role in Indonesia’s economic and cultural landscape, contributing significantly to job creation, regional development, and international recognition. This study evaluates the performance of IndoBERT, a state-of-the-art Indonesian language model, and Long Short-Term Memory (LSTM) networks for sentiment classification of 2,560 Google reviews of Kenjeran Park in Surabaya, consisting of 54% positive, 28% neutral, and 18% negative sentiments. Preprocessing steps included slang replacement, stemming, stopword removal, and tokenization, with class imbalance addressed through weighted loss adjustments. IndoBERT was fine-tuned using contextual embeddings with a learning rate of 0.00005, while the LSTM model employed a 128-unit architecture trained over 150 epochs with the Adam optimizer. Experimental results show that IndoBERT achieved 87.50% accuracy, 0.7697 precision, 0.7643 recall, and 0.7643 F1-score, outperforming LSTM’s 77.93% accuracy, 0.6826 precision, 0.6812 recall, and 0.6826 F1-score. This research establishes a comparative benchmark of transformer-based and RNN-based architectures for Indonesian tourism review sentiment analysis, introduces a domain-specific preprocessing pipeline with imbalance handling, and provides actionable insights for digital tourism analytics. Beyond its technical contributions, the study highlights the urgency of advancing robust natural language processing approaches for low-resource languages, thereby strengthening the field of informatics and supporting data-driven decision-making in the tourism sector.]]>
<![CDATA[Abstractive Summarization of Indonesian Islamic Stories Using Long Short-Term Memory (LSTM) ]]>
<![CDATA[Savila, Aisya Gusti]]>;
<![CDATA[Supriyono, Supriyono]]>;
<![CDATA[Melani, Roro Inda]]>
<![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.2026.7.1.4918
<![CDATA[The length of narratives in stories often poses a challenge for many readers, especially those with time constraints or difficulty understanding the entire story. In this case, summarization offers a solution, but manual summarization is not always efficient in meeting the need for quick and concise information. This study aims to develop an automatic text summarization system for Islamic stories using the Long Short Term Memory (LSTM) algorithm. The study employs three data splitting scenarios for training and testing: 90:10, 80:20, and 70:30. Testing results show that the highest training accuracy was achieved in the 80:20 scenario with a value of 89.44%. This does not entirely indicate that a smaller proportion of training data will always result in higher accuracy, as this improvement can be influenced by data variation, overfitting conditions, and early stopping performance. Therefore, the data division ratio influences the training process. Although the highest training accuracy was obtained in the 80:20 scenario, the best semantic summary quality was found in the 90:10 scenario. In the 90:10 scenario, the ROUGE-1 evaluation score achieved a precision of 0.4147, a recall of 0.2516, and an F1-score of 0.3027. Meanwhile, ROUGE-2 achieved a precision of 0.1022, a recall of 0.0568, and an F1-score of 0.0684. Meanwhile, ROUGE-L achieved a precision of of 0.2017, recall of 0.1209, and F1-score of 0.1459.]]>
<![CDATA[Comparison of the Accuracy Levels of Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM) Methods in Predicting Gold Jewelry Sales ]]>
<![CDATA[Pandu W, Muhammad Arfianto]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Purwadi, Purwadi]]>;
<![CDATA[Rohmah, Umdah Aulia]]>
<![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.2026.7.1.5139
<![CDATA[Gold has long been recognized as a safe haven asset, especially during economic uncertainty. Accurate prediction of gold jewelry sales is essential for inventory management and business strategy, particularly in high-demand regions such as Imogiri. This study aims to compare the accuracy levels of three machine learning methods—Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM)—in predicting gold jewelry sales using historical transaction data from Toko Emas Parimas. The dataset comprises 4,595 records from January 2022 to December 2024. The research employs data preprocessing, including data cleaning, feature transformation, and normalization, followed by classification into sales categories. Two data-splitting schemes (80:20 and 70:30) were implemented to evaluate model generalization. The models were trained and tested using performance metrics such as accuracy, precision, recall, and F1-score. The results show that Random Forest achieved perfect classification with an accuracy of 1.00 in both schemes, outperforming the other models. Naïve Bayes also performed well with accuracy up to 0.98, while LSTM showed moderate results with accuracy ranging from 0.82 to 0.88. These findings indicate that Random Forest is the most reliable model for sales prediction of gold jewelry, especially for static classification tasks. The study provides practical insights for retailers and decision-makers in selecting suitable analytical models, and it highlights the importance of aligning analytical methods with data characteristics to improve decision support systems in retail.]]>
