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
1,111 Documents
<![CDATA[Comparative Analysis of GPT-2 Augmentation, ALBERT, and Similarity Measures for Cyberbullying Detection ]]>
<![CDATA[Hidayat, Zidane]]>;
<![CDATA[Cahyono, Hasan Dwi]]>;
<![CDATA[Muslim, Fajar]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5320
<![CDATA[The effectiveness of cyberbullying detection is influenced by the availability of sufficient, diverse, and contextually rich training data, which is often limited in low-resource languages such as Indonesian. To address dataset limitations, researchers have extensively explored data augmentation (DA) as a promising approach to improving model performance. DA generates new data instances by applying transformations to existing data, thereby increasing both dataset size and variability. Prior studies have demonstrated that applying Easy Data Augmentation (EDA) with Support Vector Machine (SVM) classification improved cyberbullying detection performance, even when it faced challenges in capturing semantic and contextual nuances. In this paper, we investigated Indonesian DA methods using the Transformer-based GPT-2 model. The augmented sentences were evaluated and filtered based on context, semantics, diversity, and novelty, with similarity measures such as Euclidean Distance (ED), Cosine Similarity (CS), Jaccard Similarity (JS), and BLEU Score (BLS) ensuring the quality of the augmentation. Furthermore, we compared text classification performance using both SVM and the Transformer-based ALBERT model. Experimental results revealed that incorporating similarity measures and GPT-2 as a DA method failed to improve cyberbullying detection performance, potentially due to the semantic drift introduced by GPT-2 and the inadequacy of similarity measures in capturing nuanced contextual information. However, we found that ALBERT outperformed SVM as a classification model, achieving average F1-scores of 91.77% and 91.72%, respectively. This study contributes to the informatics field by exploring the potential of Transformer-based augmentation and similarity evaluation in enhancing low-resource text classification, while acknowledging the limitations in data quality and model adaptation.]]>
<![CDATA[Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images ]]>
<![CDATA[Danar Dana, Raditya]]>;
<![CDATA[Mulyawan, Mulyawan]]>;
<![CDATA[Bahtiar, Agus]]>;
<![CDATA[Nurdiawan, Odi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5328
<![CDATA[This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.]]>
<![CDATA[Deep Learning-Based Recognition of Indonesian Sign Language (BISINDO) Alphabetic Gestures Using Skeletal Feature Extraction and LSTM ]]>
<![CDATA[Afwan, Teuku M Arief]]>;
<![CDATA[Gernowo, Rahmat]]>;
<![CDATA[Wibawa, Helmie Arif]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5337
<![CDATA[Communication is a fundamental aspect of human life, and for the deaf community, sign language serves as the primary medium of interaction. In Indonesia, the Indonesian Sign Language (BISINDO) is widely used, however, research on automatic BISINDO recognition remains limited due to the scarcity of representative datasets. This study presents the development of a BISINDO recognition system based on deep learning by integrating the Long Short-Term Memory (LSTM) architecture with the MediaPipe Holistic framework. To address data limitations, a custom dataset comprising 866 BISINDO alphabetic gesture videos was collected, involving recordings from both expert and non-expert signers to capture stylistic variations. Extracted skeletal landmark features were processed through a three-layer LSTM network followed by dense layers for sequential modeling and classification. Experimental results show that the proposed model achieved a validation accuracy of approximately 93%, outperforming static image–based methods and demonstrating the effectiveness of skeletal features in representing dynamic gestures. The model also exhibited real-time applicability with promising performance, although challenges such as misclassification of visually similar gestures and dataset imbalance remain. This study contributes to the underexplored field of BISINDO recognition by providing a baseline system and dataset, and further advances the domains of computer vision and human–computer interaction within informatics through an inclusive, data-driven framework for Indonesian Sign Language recognition and future AI-assisted accessibility technologies.]]>
<![CDATA[A Smart System for Non-Invasive Early Detection of Diabetes through Deep Learning-Based Nail Image Analysis and Expert Systems ]]>
<![CDATA[Zulfikri, Muhammad]]>;
<![CDATA[Kusuma, Wirajaya]]>;
<![CDATA[Furqan, Naufal A.]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5347
<![CDATA[Public health in Indonesia faces significant challenges in the early detection of diseases, particularly in areas with limited medical services. Diabetes Mellitus can lead to serious complications, but its detection is often hindered by limited access to invasive and expensive diagnostic methods. This study aims to develop a non-invasive early detection system through nail image analysis using a deep learning method based on EfficientNet-B7 and a rule-based expert system. The system classifies nail images into five categories: Healthy, Beaus lines, Onycholysis, Onychomycosis, and Paronychia. The evaluation results show an accuracy of 97.11% on the test set, demonstrating excellent performance in detecting nail conditions associated with diabetes. The application of the expert system using Forward Chaining and Certainty Factor provides in-depth medical explanations for the model's predictions, making this system a potential solution for diabetes screening that is fast, affordable, and accessible across various healthcare facilities.]]>
<![CDATA[Optimized RoBERTa–DeBERTa Ensemble for Multi-Class Sentiment Analysis on Highly Imbalanced Data ]]>
<![CDATA[Sika, Xaverius]]>;
<![CDATA[Kisbianty, Desi]]>;
<![CDATA[Istoningtyas, Marrylinteri]]>;
<![CDATA[Abidin, Dodo Zaenal]]>;
<![CDATA[Toscany, Afrizal Nehemia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5350
<![CDATA[Multi-class sentiment analysis on highly imbalanced datasets poses substantial challenges for achieving accurate and equitable classification, particularly when neutral sentiments are considerably underrepresented. This study evaluates four fine-tuned transformer models—Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, RoBERTa, and DeBERTa—using a real-world Amazon review dataset comprising over 20,000 user-generated texts. Sentiment labels were derived from star ratings through a standardized mapping scheme. Experimental results show that while BERT achieved the highest overall accuracy (93%), its performance on the minority Neutral class remained limited (F1-score: 0.36). DeBERTa improved Neutral recall to 0.59 but with a slightly lower overall accuracy of 91%. To address this imbalance, two ensemble strategies were explored: a fixed-weight soft voting scheme and an optimized-weight ensemble combining RoBERTa and DeBERTa. The optimized RoBERTa–DeBERTa ensemble yielded the most balanced performance, achieving a Neutral-class F1-score of 0.57 while maintaining 91% overall accuracy. ROC and PR curve analyses further indicate superior sensitivity–precision balance for this optimized ensemble. The findings indicate that adaptive ensemble weighting can substantially enhance minority-class detection under severe imbalance. This study provides a clear methodological contribution by demonstrating the effectiveness of targeted ensemble optimization and offers practical guidance for developing more balanced and reliable sentiment classification systems.]]>
<![CDATA[Evaluating SMOTE Performance for Imbalanced Multi-Label Sentiment Classification in MLSE Usability Testing of Mobile App Reviews ]]>
<![CDATA[Basri, Hasan]]>;
<![CDATA[Purwanti, Wahyu Noviani]]>;
<![CDATA[Alparisi, Ihsan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5351
<![CDATA[Imbalanced data poses a significant challenge in multi-label classification tasks, especially when combining sentiment analysis with usability testing of mobile application reviews. This study investigates the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification performance on a multi-label dataset consisting of 10,000 Indonesian language user reviews from the Google Play store. The classification labels represent a combination of usability criteria and sentiment polarity, with strong imbalance observed across several classes. Three machine learning algorithms SVM, Decision Tree, and Random Forest were evaluated on datasets of increasing sizes (1,000 to 10,000 entries), each tested under both original and SMOTE-balanced conditions using stratified 10-fold cross-validation with accuracy and F1-score as the primary metrics. Experimental results show that SMOTE significantly improves the performance of Decision Tree mainly on smaller datasets but exhibits inconsistent gains as the dataset grows, provides modest and stable improvements for Random Forest, and negatively impacts SVM, whose performance remains consistently better without SMOTE. This study concludes that SMOTE is not a universally effective solution and must be applied selectively based on model characteristics. These findings contribute to the Machine Learning for Software Engineering (ML4SE) domain and the field of informatics by highlighting the importance of aligning resampling techniques with algorithmic behaviour when dealing with highly imbalanced multi-label text classification tasks.]]>
<![CDATA[Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier ]]>
<![CDATA[Solichin, Achmad]]>;
<![CDATA[Zulqan, Helmi]]>;
<![CDATA[Painem, Painem]]>;
<![CDATA[Pradiptha, Anindya Putri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5357
<![CDATA[Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.]]>
<![CDATA[Deep Learning Based MobileNet Optimization For High Accuracy Classification Of Toddler Stunting ]]>
<![CDATA[Wibowo, Anan]]>;
<![CDATA[Sembiring, Rahmat Widia]]>;
<![CDATA[Solikhun, Solikhun]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5382
<![CDATA[This study aims to develop and optimize a MobileNet-based deep learning model for toddler stunting classification using whole-body images. A progressive optimization strategy was applied through three scenarios: (1) a baseline MobileNet feature-extraction model, (2) an optimized fine-tuned model, and (3) a final model enhanced with an adaptive ReduceLROnPlateau scheduler. Using a private dataset of 571 images, the proposed model achieved significant improvements—from 97.47% accuracy in the baseline model to a perfect 100% accuracy, precision, recall, and F1-score in the final scenario. These results highlight the novelty of this study, namely the use of whole-body images combined with progressive MobileNet optimization, which substantially outperforms prior studies relying solely on facial image analysis. The proposed approach demonstrates strong potential as a highly accurate and efficient computational tool for clinical stunting screening.]]>
<![CDATA[Sentiment Analysis Using Bidirectional Encoder Representations from Transformers for Indonesian Stock Price Prediction with Long Short-Term Memory and Gated Recurrent Unit Models ]]>
<![CDATA[Iswavigra, Dwi Utari]]>;
<![CDATA[Setiawan, Very Dwi]]>;
<![CDATA[Ulfa, Mutia]]>;
<![CDATA[Ommr, Brieva]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5383
<![CDATA[The advancement of artificial intelligence based market analytics has driven the need for stock price prediction models capable of representing market behavior both technically and psychologically. This study aims to improve stock price forecasting in the Indonesian capital market by integrating sentiment analysis with deep learning time-series models. It evaluates whether public sentiment can contribute to enhancing prediction accuracy when combined with historical stock data. Textual sentiments were extracted using IndoBERT and converted into positive, negative, and neutral scores, which were then merged with historical stock prices. These data were modeled using LSTM, GRU, and a hybrid LSTM–GRU architecture. Model evaluation was conducted using MSE, MAE, RMSE, and MAPE metrics across six Indonesian stocks ANTM, BBCA, BBRI, SCMA, TLKM, and UNVR. The hybrid LSTM–GRU model produced the lowest prediction errors for BBCA and BBRI, with MSE scores of 0.151 and 1022.062, respectively. GRU delivered the best performance for highly volatile stocks, such as SCMA MAPE 1.65% and UNVR MAPE 0.51%, while LSTM demonstrated the most stable performance for TLKM with an MSE of 606.93 and RMSE of 24.63. Across all cases, sentiment scores improved model responsiveness, particularly during price spikes ANTM mid-2025 and price declines BBRI early year. The integration of sentiment significantly enhances prediction relevance by combining psychological market indicators with technical price trends. This framework provides more reliable decision-making support for investors, strengthens algorithmic trading strategies in Indonesia, and contributes to intelligent financial analytics that reflect local market behavior.]]>
<![CDATA[Early Detection Of Melanoma Skin Cancer Using Gray Level Co-Occurrence Matrix And Ensemble Support Vector Machine: Deteksi Kanker Kulit Berbasis Analisis Fitur dan Metode Ensemble Machine Learning ]]>
<![CDATA[Mustagfirin, Mustagfirin]]>;
<![CDATA[Wijanarko, Rony]]>;
<![CDATA[Rudiyanto, Arif Rifan]]>;
<![CDATA[Hisbana, Abdullah Afnil]]>;
<![CDATA[Farida, Fitrotin Na’imul]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2026.7.2.5389
<![CDATA[Skin cancer is a major global health problem with incidence rates increasing every year. Melanoma, the most aggressive form of skin cancer, requires accurate early detection to reduce mortality risk. Conventional diagnostic methods such as visual examination and biopsy still face limitations in precision and consistency, highlighting the need for more objective and efficient technological approaches. This study proposes a classification method for melanoma using an ensemble of Support Vector Machine (SVM) and Random Forest (RF), supported by feature extraction through the Gray Level Co-occurrence Matrix (GLCM) and dimensionality reduction using Linear Discriminant Analysis (LDA). The research stages include image preprocessing using grayscale conversion to reduce data complexity, followed by GLCM-based texture feature extraction, and LDA transformation to enhance class separability. The classification model is developed using an ensemble voting mechanism that combines predictions from SVM and RF to produce a more stable and robust decision. Experimental results with a 60:40 train–test ratio show that the proposed method achieves an accuracy of 88.75%, outperforming each individual model tested. These findings indicate that the integration of GLCM–LDA features with the SVM-RF ensemble effectively improves melanoma detection performance. Overall, this study provides a significant contribution to the development of early detection systems in health informatics, offering potential improvements in patient safety and survival rates for individuals affected by skin cancer.]]>
