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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1,048 Documents
<![CDATA[Sales Forecasting Model for Indonesian Clothing MSMEs For Sales Strategy Optimization Using The Long Short-Term Memory Method ]]>
<![CDATA[Fawzi, Muhammad Ihsan]]>;
<![CDATA[Pratomo, Laurensia Claudia]]>;
<![CDATA[Isnawati, Dian]]>;
<![CDATA[Chasanah, Nur]]>;
<![CDATA[Kurniawan, Nadhifa Zahra]]>
<![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.2026.7.1.5339
<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) in the clothing industry are one of the key pillars of the economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, MSMEs face considerable challenges related to market competition, shifting consumer trends, and fluctuating demand. Advances in data analytics and machine learning offer solutions to improve sales forecasting accuracy, thereby supporting more effective business strategies. This study aims to develop a sales forecasting model based on Long Short-Term Memory (LSTM) tailored to the characteristics of clothing MSMEs in Indonesia. The research was conducted at Ananda Kids MSME in Purbalingga, using 30,885 daily transaction records collected over 23 months. The dataset included product categories, sales volume, and revenue, which were further processed through normalization, handling of missing values, and the addition of seasonal features. The LSTM model was designed with 128 neurons and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the LSTM model achieved high accuracy for certain product categories. The “Set” and “Children’s Fashion” categories recorded MAPE values below 10%, demonstrating the model’s effectiveness in forecasting stable sales patterns. In contrast, categories with high volatility, such as accessories, produced larger prediction errors. These results highlight that data quality and sales pattern stability are crucial factors in enhancing model performance. Overall, the study demonstrates that the application of LSTM holds significant potential in supporting strategic decision-making for MSMEs through more accurate sales forecasting. Beyond its practical contributions for business actors, the study also provides a basis for the development of digitalization policies for the MSME sector in Indonesia.]]>
<![CDATA[K-Means Clustering with Elbow Method and Validity Indices for Classifying Student Academic Achievement Based on Knowledge Scores at SDN 48 Kota Jambi ]]>
<![CDATA[Azmi, M. Fikri]]>;
<![CDATA[Abidin, Dodo Zaenal]]>;
<![CDATA[Jasmir, Jasmir]]>
<![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.2026.7.1.5349
<![CDATA[Student performance evaluation at SDN 48 Kota Jambi has been traditionally conducted manually, which is inefficient and often subjective. This study aims to provide an objective classification of students’ academic achievement using data-driven methods. The research applies the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, clustering, and evaluation. The dataset consists of knowledge scores from 152 elementary students across seven subjects, obtained from the Merdeka Curriculum report cards. Data preprocessing included cleaning and normalization to ensure consistency. K-Means clustering was implemented using RapidMiner, with the optimal number of clusters determined through the Elbow Method. Cluster validity was assessed using the Davies–Bouldin Index (1.226) and the Silhouette Coefficient (0.245). The results produced three clusters: high achievers (30.9%), medium achievers (27.0%), and low achievers (42.1%). Centroid analysis indicated that Mathematics and Physical Education were the most discriminative subjects across groups. These findings highlight a substantial proportion of students requiring remedial intervention and support differentiated learning strategies. The contribution of this research lies in applying educational data mining techniques to an elementary school context in Jambi, integrating both quantitative indices and qualitative validation with teachers. The study demonstrates that clustering methods can enhance educational decision-making, providing a basis for adaptive teaching, targeted interventions, and resource allocation in elementary education.]]>
<![CDATA[Optimizing Bag of Words and Word2Vec with Vocabulary Pruning and TF-IDF Weighted Embeddings for Accurate Chatbot Responses in Indonesian Treasury Services ]]>
<![CDATA[Aprianto, Eko]]>;
<![CDATA[Mahdiana, Deni]]>;
<![CDATA[Wibowo, Arief]]>
<![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.2026.7.1.5370
<![CDATA[The high volume of support tickets submitted to the HAI DJPb Service Desk has caused delays and inconsistent response quality in payroll-related inquiries across Indonesian treasury work units (Satker). To improve the accuracy and efficiency of public service responses, this research proposes an optimized text-vectorization framework for chatbot development using a hybrid combination of Bag of Words (BoW), Word2Vec, vocabulary pruning, and TF-IDF weighted embeddings. The dataset consists of 2024 ticket logs, curated FAQs, and questionnaire data related to the Satker Web Payroll Application. The method includes preprocessing (snippet removal, normalization, tokenization, stopword removal, stemming), vocabulary pruning based on empirical frequency thresholds (<5 and >80) while preserving domain-specific technical terms, and semantic weighting through TF-IDF. Four vectorization models—BoW, BoW with pruning, Word2Vec, and Word2Vec + TF-IDF—were evaluated using cosine similarity, response time, and accuracy. Results show that BoW achieved the highest accuracy of 88.32%, while Word2Vec produced the most stable response time with an average of 47.32 ms and a cosine similarity of 0.99. The findings demonstrate that frequency-based representations remain highly effective for structured administrative datasets, while weighted embeddings improve semantic relevance. This study contributes to the field of Informatics by providing an efficient hybrid vectorization framework tailored for Indonesian administrative language, enabling more accurate and scalable chatbot solutions for e-government services.]]>
<![CDATA[IoT-Based Smart Detector with SVM and XGBoost for Real-Time Child Growth Monitoring and Stunting Risk Prediction ]]>
<![CDATA[Mahardika, Fajar]]>;
<![CDATA[Syafirullah, Lutfi]]>;
<![CDATA[Nugroho, Adlan]]>
<![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.2026.7.1.5394
<![CDATA[Stunting is a major public health issue, particularly in developing countries, causing long-term physical and cognitive impairments in children that reduce their quality of life and future productivity. To address this challenge, this study aims to develop an IoT-based smart detection system for child growth monitoring, enabling quicker and more accurate detection of stunting risks. The proposed system combines both hardware and intelligent software components to measure key growth indicators—height, weight, and BMI—using digital sensors and microcontrollers, transmitting the collected data to a cloud platform for real-time analysis. Machine learning algorithms, such as Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), are employed to predict stunting risk. Experimental results show that the XGBoost model outperforms SVM, achieving an accuracy of 80%, precision of 82%, recall of 78%, and F1-score of 79.9%, compared to SVM’s accuracy of 70%, precision of 68%, recall of 65%, and F1-score of 66.4%. This research provides a scalable technological framework for real-time stunting monitoring and early intervention, with the potential for implementation in resource-limited settings. By supporting national stunting reduction initiatives, the system enhances public health innovation and child welfare.]]>
<![CDATA[Multi-Class Real-Time Color Classification of Coffee Beans via Fine-Tuned EfficientNetB0 and Post-Training Quantization ]]>
<![CDATA[Yuliyanti, Siti]]>;
<![CDATA[Maarip, Syamsul]]>
<![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.2026.7.1.5400
<![CDATA[The first problem faced in coffee bean classification is that the manual grading or sorting process still relies heavily on human labor, making it subjective, time-consuming, and prone to errors. Secondly, existing deep learning-based systems often require substantial computing resources, rendering them inefficient for industrial-scale implementation or on limited hardware. The research objective is to develop an efficient, lightweight, and accurate automatic classification model to recognize coffee bean color and support the automation of quality control processes in the coffee post-harvest chain. This study develops an automated system for coffee bean classification based on four color classes: light, medium, green, and dark, utilizing the lightweight EfficientNet model with fine-tuning of smaller versions of EfficientNet (B0–B3). The research stages consist of dataset acquisition, pre-processing, modeling and fine-tuning, as well as model evaluation on the detection system on low-end devices. The main innovation of this research is the efficiency and speed of real-time classification of coffee bean color images using a lightweight CNN model optimized through quantization, which supports field applications with hardware limitations without sacrificing accuracy. Fine-tuning EfficientNetB0 by unfreezing the last 30 layers achieved 97.17% training accuracy and 99.25% validation accuracy with consistent loss reduction, supported by Test-Time Augmentation (TTA) which improves prediction stability to >80% confidence against variations in field image quality. Deployment to TensorFlow Lite (TFLite) with 8-bit quantization resulted in a lighter model that maintained 99.50% accuracy and accelerated inference by up to 6x compared to the original H5 model, and excelled at multi-object detection without sacrificing classification confidence.]]>
<![CDATA[Aspect-Based Sentiment Analysis of Access by KAI Application Reviews Using IndoBERT for Multi-Label Classification Tasks ]]>
<![CDATA[Nur Alfiana, Hilda]]>;
<![CDATA[Doewes, Afrizal]]>;
<![CDATA[Widoyono, Bambang]]>
<![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.2026.7.1.5402
<![CDATA[Ratings and reviews on mobile applications provide valuable insights into user experience and satisfaction with app features and services. However, ratings are subjective and often inconsistent with the content of the reviews. Therefore, a more in-depth analysis of the review content is necessary to identify evaluation points accurately. This study aims to evaluate the performance of IndoBERT in Aspect-Based Sentiment Analysis (ABSA) on Access by KAI application reviews. Data were collected by scraping user reviews from the Google Play Store, then annotated using a hybrid labeling approach. The resulting dataset was used to fine-tune the IndoBERT model across three ABSA tasks: aspect classification, sentiment classification for each aspect, and joint aspect-sentiment classification. We also benchmarked the model against baseline models to demonstrate its effectiveness. The results show that IndoBERT achieved the best performance across all tasks, specifically aspect classification (accuracy 0.928, F1-score 0.785), sentiment classification (accuracy 0.928, F1-score 0.752), and joint aspect-sentiment classification (accuracy 0.962, F1-score 0.549). Overall, IndoBERT successfully outperformed SVM and XGBoost with TF-IDF, BiLSTM with pre-trained IndoBERT embeddings, mBERT, and XLM-R. This study contributes a new dataset that provides resources for further research and development in Indonesian Natural Language Processing (NLP). These findings also highlight the advantages of a monolingual model trained specifically on Indonesian-language data.]]>
<![CDATA[Evaluating Ensemble Versus Non-Ensemble Machine Learning Performance with Preprocessing Techniques for IoT Intrusion Detection on CICIoT2023 ]]>
<![CDATA[Firdaus, Febrian Sabila]]>;
<![CDATA[Hatta, Puspanda]]>;
<![CDATA[Basori, Basori]]>
<![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.2026.7.1.5408
<![CDATA[The rapid expansion of the Internet of Things (IoT) introduces significant security vulnerabilities, exposing networks to sophisticated attacks. Developing effective Intrusion Detection Systems (IDS) is critical, yet many machine learning benchmarks rely on outdated datasets. This study provides a comprehensive comparative evaluation of ensemble and non-ensemble machine learning models for multiclass attack classification using the modern and complex CICIoT2023 dataset. The methodology involves robust preprocessing, including random undersampling to address extreme class imbalance and a hybrid feature selection approach combining Mutual Information (MI) and Random Forest Feature Importance (RFFI). Models, including Naive Bayes, Logistic Regression, SVM, Random Forest, and XGBoost, were evaluated using stratified 5-fold cross-validation (K=5) with default hyperparameters. The results demonstrate that ensemble models consistently and significantly outperform non-ensemble models. XGBoost achieved the highest and most stable performance, yielding a mean F1-score of 0.8889 ± 0.0008 across the K-folds, and a final macro F1-score of 0.8891 on the test set. This research confirms the superiority of ensemble methods for complex IoT traffic and quantitatively highlights the critical role of preprocessing. Notably, scaling was proven essential for non-ensemble models, drastically improving Logistic Regression's F1-score from an unstable 0.6280 to 0.7691.]]>
<![CDATA[Hybrid Heuristic Algorithms for Optimizing University Graduate-Job Matching: A Quantitative Study in Indonesia's 2025 Labor Market ]]>
<![CDATA[Nidauddin, Ikbal]]>;
<![CDATA[Prabowo, Kresno Murti]]>;
<![CDATA[Alim, Abdullah]]>
<![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.2026.7.1.5421
<![CDATA[University graduate unemployment in Indonesia reached critical levels with 1,010,652 unemployed graduates in 2025 (BPS data), representing approximately 15% of national unemployment due to severe skills mismatch between education outcomes and labor market demands. This research develops and validates a novel hybrid heuristic algorithm integrating Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) with adaptive diversity-based switching mechanisms to optimize graduate-job matching through multi-objective competency profile alignment. The quantitative experimental study collected data from 200 university graduates across five academic disciplines and 5 major recruiting companies through structured surveys and competency assessments. The proposed GA-PSO-SA hybrid algorithm with adaptive switching achieved 92.4% matching accuracy (35% improvement over traditional methods), 42% faster convergence compared to single algorithms (10.6s vs. 18.4s for pure GA), and solution quality of 8.9/10. Statistical validation through paired t-tests demonstrated highly significant improvements (p < 0.001, Cohen's d > 2.0) across all comparisons. The system successfully reduces average job search duration by 40% (from 6+ months to 3.6 months) and improves graduate placement success rates by 28%. This research contributes a theoretically-grounded and empirically-validated intelligent recommendation system addressing Indonesia's graduate employment crisis through computational optimization, with implications for national workforce development and recruitment efficiency enhancement.]]>
<![CDATA[Optimized KNN Performance with PCA and K-Fold Cross-Validation for Colorectal Cancer Survival Prediction ]]>
<![CDATA[Manza, Yuke]]>;
<![CDATA[Rosnelly, Rika]]>;
<![CDATA[Furqan, Mhd]]>;
<![CDATA[Riza, Bob Subhan]]>
<![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.2026.7.1.5422
<![CDATA[Colorectal cancer remains a leading cause of global mortality, necessitating effective predictive tools for patient survival. While Machine Learning algorithms like K-Nearest Neighbors (KNN) utilize patient data for prediction, standard KNN implementations often suffer from the curse of dimensionality and overfitting, leading to unreliable performance on complex medical datasets. This study aims to evaluate and optimize the performance of the KNN algorithm by integrating Principal Component Analysis (PCA) for dimensionality reduction and K-Fold Cross-Validation (KFCV) to enhance model stability. The research utilized a quantitative approach on a global colorectal cancer dataset, processing demographic and clinical features through a rigorous pipeline of imputation, encoding, and normalization. Three model configurations were systematically compared: Standard KNN, KNN combined with PCA, and an optimized KNN model utilizing both PCA and KFCV across various neighbor values. The results demonstrate a distinct trade-off between predictive sensitivity and model stability. While the Standard KNN and PCA-enhanced models achieved higher recall, indicating a strong ability to identify survivors in a single data split, the fully optimized KNN+PCA+KFCV model provided the most stable and generalized accuracy with minimal deviation. These findings indicate that while PCA effectively reduces computational complexity without information loss, the integration of cross-validation is crucial for obtaining an honest assessment of model performance. This research contributes to clinical informatics by highlighting the necessity of prioritization between high sensitivity and generalization stability when developing survival prediction models for complex, inseparable medical data.]]>
<![CDATA[Analysis of Static and Contextual Word Embeddings in Capsule Network for Sentiment Analysis of The Free Nutritious Meal Program on Twitter ]]>
<![CDATA[Raditya, Virgi Atha]]>;
<![CDATA[Saragih, Triando Hamonangan]]>;
<![CDATA[Faisal, Mohammad Reza]]>;
<![CDATA[Abadi, Friska]]>;
<![CDATA[Muliadi, Muliadi]]>
<![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.2026.7.1.5424
<![CDATA[Public discourse surrounding Indonesia’s Makan Bergizi Gratis (MBG) program reflects diverse opinions that have not yet been systematically examined using computational methods. This study addresses that gap by evaluating the effectiveness of static and contextual word embeddings within a Capsule Network (CapsNet) framework for sentiment analysis of MBG-related tweets on Twitter. A total of 7,133 Indonesian-language tweets were collected through web crawling, preprocessed, and manually labeled into positive, neutral, and negative categories. Four embedding techniques—Word2Vec, FastText, ELMo, and IndoBERT—were tested under two preprocessing settings, raw and stemming. The experimental results show that Word2Vec on raw text achieved the highest accuracy of 96.17%, while FastText obtained the best performance on stemmed data with 94.10%. These findings indicate that morphological normalization benefits static and subword-based embeddings, whereas contextual models maintain stable performance without extensive fine-tuning. Overall, this study demonstrates the potential of combining CapsNet with appropriate embedding strategies for Indonesian-language sentiment analysis and provides evidence that natural language processing can support data-driven evaluation of public programs such as MBG.]]>
