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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962 Documents
<![CDATA[Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi ]]>
<![CDATA[Iqbal, Iqbal]]>;
<![CDATA[Hidayat, Nurul]]>;
<![CDATA[Gevano, Daiva Paundra]]>;
<![CDATA[Ilahi, Andhika Putra Restu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5140
<![CDATA[In today's competitive business landscape, a deep understanding of customer behavior and preferences is crucial for strategic success. Customer segmentation emerges as a vital approach to identify distinct customer subgroups, enabling personalized and efficient marketing strategies. However, many companies still struggle to achieve this understanding due to suboptimal data utilization and inaccurate manual grouping methods. To address these challenges, this research proposes and implements a data mining approach using the K-Means Clustering algorithm for automated and measurable customer segmentation. Leveraging the "Customer Personality Analysis" dataset from Kaggle, this study aims to uncover hidden patterns in customer demographics (age, income, marital status, number of children) and purchasing behavior (number and frequency of transactions). A comprehensive data pre-processing pipeline, including handling missing values, feature engineering, irrelevant column removal, categorical transformation, and numerical scaling, ensures data quality and readiness. Using the Elbow Method, four optimal clusters were identified: "Balanced Spenders with Teenagers" (Cluster 0), "Budget-Conscious Families" (Cluster 1), "High-Value Engaged Buyers" (Cluster 2), and "Active Mature Buyers" (Cluster 3). Visualization using Principal Component Analysis (PCA) further confirms significant characteristic differences between these segments. Cluster 2, being the most valuable and responsive segment, requires premium marketing strategies, while Cluster 1, the largest segment, demands a value-oriented approach. The results of this segmentation provide deep strategic insights, enabling companies to allocate marketing resources more efficiently, craft more relevant messages, and ultimately enhance customer satisfaction and business profitability. These findings demonstrate the potential of unsupervised learning in enhancing data-driven customer profiling systems in marketing and business informatics.]]>
<![CDATA[Enhancing Fake News Detection on Imbalanced Data Using Resampling Techniques and Classical Machine Learning Models ]]>
<![CDATA[Abidin, Dodo Zaenal]]>;
<![CDATA[Siswanto, Agus]]>;
<![CDATA[Saputra, Chindra]]>;
<![CDATA[Betantiyo , Betantiyo]]>;
<![CDATA[Nehemia Toscany, Afrizal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5177
<![CDATA[Class imbalance remains a critical challenge in fake news detection, particularly in domains such as entertainment media where class distributions are highly skewed. This study evaluates seven resampling techniques—Random Oversampling, SMOTE, ADASYN, Random Undersampling, Tomek Links, NearMiss, and No Resampling—applied to three classical machine learning models: Logistic Regression, Support Vector Machine (SVM), and Random Forest. Using the imbalanced GossipCop dataset comprising 24,102 news headlines, the proposed pipeline integrates TF-IDF vectorization, stratified 3-fold cross-validation, and five evaluation metrics: F1-score, precision, recall, ROC AUC, and PR AUC. Experimental results show that oversampling methods, particularly SMOTE and Random Oversampling, substantially improve minority class (fake news) detection. Among all model–resampling combinations, SVM with SMOTE achieved the highest performance (F1-score = 0.67, PR AUC = 0.74), demonstrating its robustness in handling imbalanced short-text classification. Conversely, undersampling methods frequently reduced recall, especially with ensemble models like Random Forest. This approach enhances model robustness in fake news detection on skewed datasets and contributes a reproducible, domain-specific framework for developing more reliable misinformation classifiers.]]>
<![CDATA[Improving Model Capability for Sentiment Trend Analysis in Hotel Visitor Reviews with Bi-LSTM Multistage Approach ]]>
<![CDATA[Yanuargi, Bayu]]>;
<![CDATA[Utami, Ema]]>;
<![CDATA[Kusrini, Kusrini]]>;
<![CDATA[Parikesit, Arli Aditya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5185
<![CDATA[This study focuses to improve the sentiment analysis of hotel reviews using Multistage mechanism of two-stage approach based on the Bidirectional Long Short-Term Memory (Bi-LSTM) architecture with 53,000 data from 28 hotels in Yogyakarta that captured from google maps review for hotel in Yogyakarta. Hotel customer reviews often contain mixed sentiment expressions, making it crucial to filter out only sentences with a single dominant sentiment to avoid ambiguity. In the first stage, the model detects sentiment at the token level and counts the number of sentiment expressions in each sentence. Only sentences with a single polarity are passed to the final classification stage. In the second stage, the overall sentiment is classified as positive, negative, or neutral using pooled contextual representations. Experimental results from 30 iterations demonstrate consistently high performance, with precision, recall, and F1-scores above 0.95, and overall accuracy exceeding 96%. The confusion matrix analysis shows strong model performance, although some challenges remain in distinguishing between positive and neutral sentiment. Additionally, sentiment trend analysis of hotel reviews from properties such as Lafayette Boutique Hotel and The Westlake Resort Jogja reveals dynamic shifts in guest perception over time. This multistage mechanism approach proves effectiveness of improving sentiment classification accuracy by avoid the bias on sentiment and also in providing valuable temporal insights for monitoring customer satisfaction.]]>
<![CDATA[Evaluating Synthetic Minority Oversampling Technique Strategies for Diabetes Mellitus Classification using K-Nearest Neighbors Algorithm ]]>
<![CDATA[Riadi, Imam]]>;
<![CDATA[Yudhana, Anton]]>;
<![CDATA[Kurniawan, Gusti Chandra]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5189
<![CDATA[Data-driven classification of Diabetes Mellitus is a crucial strategy in developing medical decision support systems that are both accurate and efficient. A major challenge in this classification task is the imbalanced class distribution, which tends to reduce the model’s sensitivity to positive cases. This research utilizes a dataset of 1,000 patient medical records obtained from the Mendeley Data repository, containing clinical attributes relevant to diabetes diagnosis. This research examines the impact of various K values on the K-Nearest Neighbors (KNN) algorithm when it is combined with the SMOTE oversampling technique to enhance classification performance. The experiment employs a 10-Fold Cross-Validation methodology with five principal assessment metrics: accuracy, precision, recall, F1-score, and Area Under Curve (AUC). Compared to prior studies, this work advances the methodology by applying SMOTE within each fold of the cross-validation process, effectively preventing data leakage and improving model generalizability. Results indicate that the K=3 configuration yields the highest F1-score of 95.13% and recall of 91.83%, while the highest AUC of 96.40% is achieved at K=9 with lower sensitivity. Applying SMOTE within each fold of the cross-validation process preserves evaluation integrity and prevents potential data leakage. The model demonstrates the ability to detect positive cases more effectively while maintaining high precision. These findings highlight that combining KNN with SMOTE and proper validation strategy is a promising approach for developing a reliable early detection system for Diabetes Mellitus that is adaptive to imbalanced clinical data.]]>
<![CDATA[From Logs to Insights in the Pulp & Paper Industry: Generating Structured Alarm Reports Using LLMs and RAG ]]>
<![CDATA[Santoso, Handri]]>;
<![CDATA[Wijaya, Oktavianus Hendry]]>;
<![CDATA[Andriani, Febri]]>;
<![CDATA[Prijantono, Sonny]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5225
<![CDATA[Effective alarm management is essential in industrial environments to ensure operational safety and minimize costly downtime. Traditional rule-based reporting systems often struggle to handle heterogeneous alarm log formats and the complexity of natural language queries, limiting their adaptability in real-world applications. To address these limitations, this study proposes a generative alarm reporting system that integrates Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) framework. The system converts natural language queries into structured JSON filters, enabling efficient retrieval of contextual information from historical alarm logs. Three open-source LLMs—CodeLlama-7B, LLaMA 3.1-8B, and Mistral-7B—were locally deployed and evaluated using both quantitative and qualitative methods. Experimental results show that CodeLlama-7B achieved the best overall performance, with an Exact Match Accuracy of 0.80, a Field Match score of 93.8%, and a 0% Parse Failure Rate, outperforming the other models in reliability and structural consistency. Compared to conventional rule-based approaches, the proposed LLM-RAG integration demonstrates improved relevance, interpretability, and responsiveness in alarm reporting. This work represents the first systematic benchmarking of locally deployed open-source LLMs for industrial alarm management, providing a replicable framework and highlighting their potential to advance intelligent, real-time, and domain-specific reporting in the pulp and paper industry and beyond.]]>
<![CDATA[Data Augmentation Techniques on the Accuracy of Fertile and Infertile Egg Classification Using Convolutional Neural Networks ]]>
<![CDATA[Nurhakim, Bani]]>;
<![CDATA[Solihudin, Dodi]]>;
<![CDATA[Amalia, Dina]]>;
<![CDATA[Arelia, Irly]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5234
<![CDATA[The classification of fertile and infertile chicken eggs is crucial in the poultry industry to ensure optimal incubation efficiency and hatchability. However, the visual similarity between both egg types under candling conditions poses a significant challenge for manual inspection. This study aims to develop a convolutional neural network (CNN) model using the EfficientNetB4 architecture to automatically classify egg fertility based on image data. The dataset comprises candling images of chicken eggs, which underwent preprocessing steps such as resizing, normalization, and histogram stretching to enhance contrast. To improve model generalization, aggressive data augmentation techniques were applied, including rotation, flipping, zooming, and brightness adjustment. The model was trained in two phases—feature extraction and fine-tuning—using transfer learning and class balancing strategies. Evaluation results demonstrated high performance with an F1-score of 0.95 and balanced classification across both classes. The model's interpretability was further enhanced using Grad-CAM visualization, showing relevant activation regions. These findings indicate that the proposed method is effective in automating egg fertility classification and has potential for broader application in agricultural image diagnostics.]]>
<![CDATA[A Decision Tree Model with Grid Search Optimization for Scholarship Recipient Classification ]]>
<![CDATA[Suprapti, Tati]]>;
<![CDATA[Nurhakim, Bani]]>;
<![CDATA[Warni Ayu Hermina, Bintang]]>;
<![CDATA[Syahputra Simbolon, Vrendi Amro]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5235
<![CDATA[This study aims to classify scholarship recipients using the Decision Tree algorithm implemented in RapidMiner. The dataset consists of 1.404 records with socioeconomic and academic attributes. Preprocessing was conducted using two Replace Missing Value operators, where categorical attributes such as No. BANTUAN, No. KKS, and Prestasi were filled with "Tidak Punya," while Kepemilikan Rumah was imputed using the average value. The model was built using a Decision Tree algorithm, optimized with the Optimize Parameters (Grid) operator to determine the best values for maximal depth and confidence. Evaluation was performed using 10-fold Cross Validation to ensure reliability. The results show that the optimized Decision Tree model achieved a high accuracy of 97.72%, with strong precision, recall, and F1-score values in both the "Eligible" and "Not Eligible" classes. These findings demonstrate that the Decision Tree algorithm, when properly optimized and validated, can effectively support decision-making processes in scholarship eligibility classification. The model provides an interpretable and robust tool for educational institutions to evaluate student applications based on critical socioeconomic features, This research contributes to educational data mining by offering a validated and interpretable model that enhances fairness, transparency, and efficiency in the scholarship selection process.]]>
<![CDATA[Clustering and Modeling of Daily Weather Pattern Distribution in Makassar City Using Hybrid DBSCAN-Gaussian Mixture Model ]]>
<![CDATA[Risaldi, Muhammad]]>;
<![CDATA[Safitri, Ayu]]>;
<![CDATA[Nur Risal, Andi Akram]]>;
<![CDATA[Surianto, Dewi Fatmarani]]>;
<![CDATA[Andayani, Dyah Darma]]>;
<![CDATA[Edy, Marwan Ramdhany]]>;
<![CDATA[Firdaus, Firdaus]]>;
<![CDATA[Parenreng, Jumadi M]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5254
<![CDATA[Dynamic and irregular daily weather changes present major challenges in understanding seasonal patterns. Data uncertainty, outliers, and inter-season variability further complicate weather analysis using conventional methods. To address this issue, this study integrates Density-Based Spatial Clustering of Application with Noise (DBSCAN) and Gaussian Mixture Model (GMM) to analyze daily weather patterns in Makassar City. A total of 2,192 daily records from 2019 to 2024, including rainfall, specific humidity, atmospheric pressure, and wind speed, were examined. DBSCAN detected one dominant cluster (2019 data) and 173 outliers. The main cluster was further partitioned by GMM into three sub-clusters representing the wet (511 records, 13.39 mm rainfall), dry (633 records, 0.15 mm), and transition (875 records, 2.53 mm) seasons. GMM identified 1,764 fixed clusters and 255 ambiguous data points, with a log-likelihood of 5091.22 and the highest Silhouette Score of 0.188. Comparative evaluation demonstrated that the hybrid DBSCAN-GMM achieved superior performance (Silhouette Score = 0.1434) compared to DBSCAN or GMM individually. The novelty of this research lies in applying the DBSCAN-GMM integration, which is rarely used in tropical weather analysis, to capture seasonal structure and anomalies adaptively. This study contributes methodologically to clustering-based weather modeling and practically supports applications such as agricultural planning, disaster mitigation, and adaptive climate strategies in tropical regions.]]>
<![CDATA[Designing an AI-Based Village Information System Using Research and Development Approach for Public Governance Modernization in Popalia Village ]]>
<![CDATA[Arafat, Arafat]]>;
<![CDATA[Rasyid, Rasmiati]]>;
<![CDATA[Hestiana, Sry]]>;
<![CDATA[Rendi, Rendi]]>;
<![CDATA[Bantun, Suharsono]]>;
<![CDATA[Sari, Jayanti Yusmah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5257
<![CDATA[The COVID-19 pandemic has highlighted the challenges faced by village administrations in managing public services that remain highly manual, inefficient, and prone to errors. This study aims to design an Artificial Intelligence (AI)-based Village Information System to improve administrative efficiency, accuracy, and accessibility. The research employs a Research and Development (R&D) approach through requirement analysis, system design, prototype development, integration of an AI Generative Model and a Natural Language Processing (NLP) chatbot, followed by functional testing using the Black-box method and usability evaluation with the System Usability Scale (SUS). The results show that functional testing achieved a 95% pass rate and the SUS evaluation scored 87.0, placing the system in the “Excellent” category. These findings indicate that the system effectively automates document creation, validates citizen data, and supports interactive services through an NLP-based chatbot. The study contributes to the modernization of digital village governance in Indonesia by demonstrating how AI integration can reduce administrative workload, minimize errors, and enhance service quality.]]>
<![CDATA[Designing AI - IoE Precision Farming to Create Sustainable Eco-Friendly Hydroponic Greenhouses ]]>
<![CDATA[Sastyawan, Murti Wisnu Ragil]]>;
<![CDATA[Fawzi, Muhammad Ihsan]]>;
<![CDATA[Putera, Radita Dwi]]>;
<![CDATA[Alkaf, Zakiyyan Zain]]>;
<![CDATA[Syhamsudin, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.5.5260
<![CDATA[Conventional greenhouses, while boosting crop yields, face critical sustainability challenges due to high energy consumption and resource inefficiency, particularly in developing nations where manual management prevails. This research addresses these limitations by designing a comprehensive AI-IoE system architecture to create a smart, resource-efficient, and sustainable operational model for eco-friendly greenhouses. The development methodology involved a systematic process of requirements analysis, integrated hardware and software design, prototype assembly, and functional testing. The system utilizes an ESP32 microcontroller as its central control unit, integrating a suite of six sensors comprising light intensity, temperature, humidity, pH, Total Dissolved Solids (TDS), and CO₂ to monitor critical environmental parameters in real-time. This integration utilizes the extensive dataset for AI based predictive analysis, enabling the intelligent forecasting of environmental trends and proactive resource management. The research resulted in a complete system blueprint, including a detailed electronic circuit design, a production-ready Printed Circuit Board (PCB) layout, defined operational control logic, and an intuitive web-based dashboard for remote monitoring and management. This integrated AI-IoE architecture provides a tangible solution that surpasses previous fragmented approaches by offering holistic environmental control. The findings present a significant contribution to precision farming, establishing a scalable and efficient framework to enhance greenhouse productivity and ecological sustainability.]]>
