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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1,174 Documents
<![CDATA[Comparative Analysis of Hyperparameter Optimization Methods for LSTM in Cryptocurrency Price Prediction: An Application to TRX–USD ]]>
<![CDATA[Dasril Aldo]]>;
<![CDATA[Muhammad Raafi'u Firmansyah]]>;
<![CDATA[Muhammad Afrizal Amrustian]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5355
<![CDATA[The rapid growth of cryptocurrencies increases the demand for accurate forecasting models to support investment decisions and automated trading systems. This study analyzes and compares the performance of several hyperparameter optimization methods applied to a Long Short-Term Memory (LSTM) model for predicting the price of TRX–USD. The dataset consists of 2,096 daily historical records obtained from the Binance platform, including open, high, low, close, volume, and percentage change, with the closing price selected as the forecasting target. A baseline LSTM model was evaluated against six optimization techniques: Grid Search, Random Search, Bayesian Optimization (Hyperopt), Optuna, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Experimental results show that GA provides the best performance with an R² score of 0.88, MAE of 0.0123, RMSE of 0.0189, and a validation loss of 0.069. In contrast, Random Search yields the lowest performance, achieving an R² of only 0.2979. These findings highlight significant performance gaps among optimization strategies and demonstrate the superiority of metaheuristic-based approaches over conventional tuning methods. This research contributes to the advancement of computational intelligence by providing empirical evidence on the effectiveness of hyperparameter optimization techniques for deep learning–based time series forecasting, particularly in high-volatility financial environments. ]]>
<![CDATA[Constructing a Part-of-Speech Tagging based on Lexicon and Rule-based for Sundanese Corpus ]]>
<![CDATA[Ade Sutedi]]>;
<![CDATA[Ayu Latifah]]>;
<![CDATA[Novan Rodiansyah]]>;
<![CDATA[Yayat Sudaryat]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5361
<![CDATA[Part-of-Speech (POS) Tagging is the process of annotating word classes (nouns, verbs, adjectives, etc.) in a sentence, which is used as a basis for natural language processing and artificial intelligence. In this study, a corpus of word classes and word class annotating rules for the Sundanese language, which has limited resources, was developed. The experiments were conducted on an annotated corpus consisting of 104,696 tokens collected from Sundanese dictionaries, Sundanese Literature (Carita Pondok, Guguritan, Mantra, Pupujian, Sisindiran, Sajak, and Wawacan), Babasan and Paribasa, and social media X (Twitter). The annotation process is carried out in several stages that combine manual annotation based on cross-lingual transfer from Indonesian POS to Sundanese POS, then adjusted based on the word class rules in Sundanese. The results of this study are a POS annotation corpus containing Sundanese word-tag pairs and a basic rule-based model compared to the HMM and CRF models. The rule-based model achieves an F1-score of 0.867, the CRF model achieves an F1-score of 0.889, while the HMM model attains the highest score with an F1-score of 1.000. Analysis of POS distributions reveals that nouns (KB) consistently dominate across all models, reflecting the noun-rich nature of Sundanese literary texts. It also highlights the challenges of handling unknown words and the need for richer annotated resources, which are related to tag interoperability with Universal POS standards. This research contributes to the development of NLP resources for low-resource languages and provides a methodological foundation for future Sundanese NLP applications.]]>
<![CDATA[Certainty Factor Algorithm Approach for Early Stage of Cattle Disease Diagnosis Using Mobile-Based Expert System ]]>
<![CDATA[Budy Satria]]>;
<![CDATA[Nurfiah Nurfiah]]>;
<![CDATA[Bima Prakasa]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5374
<![CDATA[Cattle are one of the livestock that play a crucial role in meeting the demand for meat and milk, as well as providing a source of income for farmers, particularly in various regions of Indonesia. Diseases in cattle pose a serious problem due to the lack of knowledge about accessing veterinary services, a lack of understanding among farmers, and the high cost and time required for consultations, which are significant obstacles for farmers in identifying diseases in cattle early, potentially leading to death. Limitations in accessing veterinary services, a lack of understanding among farmers, and the high cost and time required for consultations are significant obstacles to treating diseases in cattle. This study aims to assist farmers in diagnosing cattle diseases using an expert system based on the observed symptoms. The application of the expert system employs a certainty factor algorithm approach, utilizing the knowledge base of animal experts in the diagnosis process. This study used 6 types of diseases and 34 lists of symptoms in cattle. Based on the results of implementing the Certainty Factor method, it was concluded that the expert system was able to diagnose cattle diseases, specifically worms, with a confidence level of 90.1504%. This is certainly influenced by the selection of symptoms, the user's confidence value for each symptom, and the combination of the confidence values from experts. In addition, testing was also carried out on the functionality of the expert system built; the results obtained showed that all functionalities run well and as expected. Thus, the final conclusion is that expert systems can be a solution and help farmers diagnose cattle diseases. Suggestions for further research include comparing algorithms to achieve better accuracy and disease identification in specific cattle species.]]>
<![CDATA[Development and Comparative Evaluation of Machine Learning Models using Clinically Relevant Features for Predicting Newborn Patients’ Length of Stay ]]>
<![CDATA[Gandung Triyono]]>;
<![CDATA[Billy Marentek]]>;
<![CDATA[Mohammad Syafrullah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5410
<![CDATA[The Length of Stay (LOS) of newborns is a crucial indicator for healthcare management and hospital resource allocation. However, prior research has yet to systematically compare machine learning models for newborn LOS prediction using clinically pertinent features in developing-country hospital contexts, creating an important methodological and contextual gap. Accurate prediction of LOS is urgently needed to support timely clinical decision-making and prevent overcrowding, inefficiencies, and unnecessary healthcare costs. This study aims to identify factors influencing LOS and develop a predictive model for newborn LOS using several machine learning algorithms. A comparison was conducted among Linear Regression, Random Forest Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The dataset consisted of medical records of newborn patients from three private hospitals in Indonesia. The research included data collection and understanding, data preprocessing, modeling, and evaluation. Experimental results show that Random Forest Regression achieved the best predictive performance, with MAE = 0.019, MSE = 0.011, RMSE = 0.086, and R² = 0.987. Feature importance analysis revealed that gender, referral source, insurance type, and diagnosis were the most influential predictors of LOS. This study contributes to the advancement of machine learning applications in healthcare data analytics and provides evidence-based insights to support neonatal care planning and hospital resource optimization.]]>
<![CDATA[Comparative Analysis of the Performance of Random Forest and CatBoost for Air Quality Prediction Based on Meteorological Factor ]]>
<![CDATA[Nirsal Nirsal]]>;
<![CDATA[Nurchaerani Kadir]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5412
<![CDATA[Air quality in urban centers such as Tangerang City has become an increasingly urgent issue due to the expansion of industrial activities, rapid population growth, and rising vehicle emissions. As a key city within the Greater Jakarta metropolitan area, Tangerang is highly vulnerable to air pollution caused by human activities and varying meteorological conditions. This study aims to assess the performance of two machine learning algorithms, Random Forest and CatBoost, in predicting air quality in Tangerang under two scenarios: models that incorporate meteorological factors and models that exclude them. The dataset includes concentrations of key air pollutants alongside meteorological variables such as temperature, humidity, and wind speed. Model performance was evaluated using MAE, MSE, RMSE, and R². The findings indicate that both algorithms perform excellently when meteorological variables are included. Random Forest achieved an MAE of 0.0099, MSE of 0.000309, RMSE of 0.0152, and an R² of 0.9931, slightly outperforming CatBoost, which recorded an MAE of 0.0135, MSE of 0.000419, RMSE of 0.0170, and an R² of 0.9907. Excluding meteorological variables decreased accuracy for both models, with Random Forest reaching an R² of 0.9519 and CatBoost 0.9487. These results underscore the importance of temperature, humidity, and wind speed in enhancing predictive accuracy. Notably, this study introduces a comparative evaluation of machine learning models in a unique urban context, providing new insights into how meteorological factors influence air quality predictions. The study contributes to the development of adaptive air quality prediction models, supporting sustainable environmental management planning in Tangerang City.]]>
<![CDATA[Integrating Whale Transaction Flow Scoring with LSTM for Bitcoin Trend Forecasting ]]>
<![CDATA[Muhammad Ridhwan Hakiki]]>;
<![CDATA[Kusnawi Kusnawi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5436
<![CDATA[Bitcoin price prediction faces significant challenges due to high volatility and the influence of large holders, known as whales, whose transactions exceeding 500 BTC can affect market behavior. This study develops an LSTM model combining whale transaction sentiment scores with historical Bitcoin OHLC prices to forecast 7-day ahead price movements. The dataset comprises 2,069 whale transactions and 8,761 hourly price observations from April 20, 2024 to April 20, 2025. The scoring mechanism assigns +1 to exchange outflows, -1 to inflows, and 0 to neutral transfers, multiplied by logarithmically normalized transaction amounts. The LSTM architecture consists of two recurrent layers with 128 and 64 memory units, processing 720-hour input sequences to generate 168-hour OHLC forecasts. Training evaluation yielded R² of 0.9386, RMSE of 0.0686, and MAE of 0.0498. Test evaluation produced Mean Absolute Errors ranging from 871.72 USD to 3,482.27 USD across OHLC components. The model correctly predicted upward directional trends but systematically underestimated prices by 2,000-3,000 USD initially and failed to anticipate a 6,422 USD intraday surge on April 22, 2025. Results demonstrate that whale sentiment features enhance directional trend identification but do not enable precise multi-day price point prediction due to sudden market regime changes. These findings contribute empirical evidence that directional sentiment scoring of large-holder transactions provides complementary predictive value beyond conventional price-volume indicators, establishing a methodological foundation for integrating blockchain-native behavioral signals into cryptocurrency forecasting frameworks.]]>
<![CDATA[Classification of Watermelon Flavor Using Artificial Neural Network with Color, Texture, and Shape Features ]]>
<![CDATA[Muh Faqih S Musgamy]]>;
<![CDATA[Muhammad Risaldi]]>;
<![CDATA[Ayu Safitri]]>;
<![CDATA[Andi Baso Kaswar]]>;
<![CDATA[Muhammad Fajar B]]>;
<![CDATA[Jumadi M Parenreng]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5457
<![CDATA[Watermelon (Citrullus vulgaris Schard) is a widely produced fruit due to its high nutritional value and health benefits. However, consumers often experience difficulty in distinguishing sweet and bland watermelons because quality assessment is generally conducted manually and subjectively. To address this issue, this study proposes a watermelon flavor classification system based on visual features, including color, texture, and shape, using an Artificial Neural Network approach with digital image processing. The dataset used in this study consists of 214 images collected from 55 watermelon samples, categorized into sweet and bland classes. The proposed method involves several stages, namely image acquisition, preprocessing, grayscale conversion, segmentation, morphological operations, feature extraction, and classification using a feedforward backpropagation learning algorithm. Various combinations of visual features were evaluated to determine the most effective configuration. Experimental results show that the proposed system achieves an accuracy of 93.67% on training data and 92.85% on testing data, with an average computation time of 0.319 seconds per image. The findings indicate that the integration of Hue Saturation Value color features, texture features derived from the Gray-Level Co-occurrence Matrix, and shape features significantly enhances the accuracy of watermelon flavor classification. This study contributes to the development of an objective, efficient, and non-destructive fruit quality assessment system and demonstrates potential applicability to other types of fruits using a similar approach.]]>
<![CDATA[Explainable Artificial Intelligence Using SHAP and Multilayer Perceptron for Transparent Stunting Risk Prediction in Sukoharjo, Indonesia ]]>
<![CDATA[Nimas Ratna Sari]]>;
<![CDATA[Yuniars Renowening]]>;
<![CDATA[Muhammad Zainul Ma’arif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5481
<![CDATA[Childhood stunting remains a critical public health challenge in Indonesia, with national prevalence at 19.8% in 2024 per SSGI data, hindering human capital development toward Indonesia Emas 2045. This study addresses the opacity of AI models in stunting prediction by integrating machine learning with Explainable AI (XAI) to enhance transparency for non-technical stakeholders. Using a survey dataset of 273 children from Sukoharjo Regency, risk factors encompassing key stunting determinants consist of maternal characteristics, household socioeconomic conditions, sanitation practices, and sociodemographic, were preprocessed via cleaning, label encoding, min-max scaling, and train-test split. Three classifiers; Logistic Regression (LR), Naïve Bayes (NB), and a Multilayer Perceptron (MLP) with ReLU/softmax were trained and evaluated on accuracy, precision, recall, and F1–score. MLP with 16 hidden nodes, achieved the highest performance: 82% accuracy, 87% precision, 82% recall, and 82% F1-score, outperforming baselines. Kernel SHAP was applied to decompose predictions, revealing mother's education, age, number of children, birth length, household size, and income as top influencers. This XAI enhanced framework promotes trust and actionability in public health interventions, advancing informatics by bridging high accuracy neural networks models with interpretable insights for targeted stunting reduction in resource–limited settings.]]>
<![CDATA[VGG-16 Transfer Learning for Accurate Classification of Three Local Durian Varieties Using Leaf Morphology Images ]]>
<![CDATA[Ahmad Haikal Nuqqy Zahhar]]>;
<![CDATA[I Gede Susrama Mas Diyasa]]>;
<![CDATA[Made Hanindya Prami Swari]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5506
<![CDATA[Durian (Durio zibethinus Murr), recognized as the "king of fruits" in Southeast Asia, represents a significant genetic asset for Indonesian agriculture with high economic value. East Java leads national production, contributing 580.5 thousand tons (29.59%) of the total 19.6 million tons in 2024. However, local durian quality faces persistent challenges due to minimal maintenance practices and farmers' limited expertise in variety identification. Manual taxonomic identification based on leaf morphology requires specialized knowledge, is time-consuming, and prone to subjective errors, particularly for three popular Nganjuk varieties—local, montong, and lai—which exhibit similar leaf characteristics. Previous studies have addressed durian classification using fruit images or disease detection on leaves, but a research gap exists for variety classification specifically using leaf images with deep learning approaches. This study implements VGG-16 transfer learning architecture with ImageNet pre-trained weights to classify three durian varieties based on leaf morphology images. A dataset of 600 high-resolution images (2048×2048 pixels, 200 per class) was collected from Nganjuk orchards following standardized protocols and validated by three independent experts (two experienced farmers and one plant taxonomist), achieving substantial inter-annotator agreement (Fleiss' kappa = 0.87). Preprocessing included resizing to 224×224 pixels with bilinear interpolation, normalization to [0,1], and standardization using ImageNet statistics. Data augmentation through random rotation (±30°), horizontal flipping (48.8% probability), contrast adjustment (±50.1%), and width/height shifting (±12%) expanded the dataset fourfold to 2,400 images. Using a 90:10 train-test split (2,160:240), the VGG-16 model trained with Adam optimizer (learning rate 0.001, dropout 0.5, dense layer 256 units) achieved 97.08% accuracy after 4 epochs in 1.11 minutes. Performance metrics demonstrated high precision (0.93-1.00), recall (0.92-1.00), and F1-scores (0.95-0.99) across all classes. This research advances precision agriculture informatics by providing an automated, reliable tool for durian variety identification, supporting farmers in optimal cultivation decisions, quality control, and economic value enhancement while contributing to sustainable agricultural development and the Center for Plant Variety Protection and Agricultural Licensing (PVTPP) registration systems in Indonesia.]]>
<![CDATA[Development of Smart Study Web Application for Classifying Student Material Understanding Levels Using Naive Bayes Classifier ]]>
<![CDATA[Purnomo Hadi Susilo]]>;
<![CDATA[Vita Ihwatin Mujtahidah]]>;
<![CDATA[Nur Qomariyah Nawafilah]]>;
<![CDATA[Azizul Azhar Ramli]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.3.5507
<![CDATA[The rapid development of information and communication technology requires adaptive digital learning systems that are able to evaluate students’ learning outcomes objectively. However, the Smart Study application previously functioned only as a quiz delivery platform and lacked analytical capabilities to assess students’ levels of material understanding, particularly in practical courses such as Computer Networks. This study aims to design and develop a web-based Smart Study application integrated with the Naive Bayes classification algorithm to determine students’ understanding levels based on quiz performance data. The research methodology includes data collection from Informatics Engineering students at Universitas Islam Lamongan, followed by data preprocessing through cleaning and categorical conversion of features, including final score, average response time, response time variability, and correct incorrect response time ratio. The dataset was divided into 80% training data and 20% testing data. The Naive Bayes model was trained and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results show that the proposed model achieved an accuracy of 75%, correctly classifying 15 out of 20 testing samples. The model demonstrated strong performance in identifying the Comprehended class with an F1-score of 0.83, while performance for the Not Comprehended class was lower with an F1-score of 0.55 due to class imbalance. This study contributes to the fields of learning analytics and educational data mining by demonstrating the integration of a simple machine learning method into an e-learning application to support early detection of learning difficulties and data-driven evaluation of digital learning processes in higher education.]]>
