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
63 Documents
Search results for
, issue
"Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026"
:
63 Documents
clear
<![CDATA[Augmentation Strategy and Hyperparameter Optimization Using Optuna for Potato Leaf Disease Classification in Uncontrolled Environment ]]>
<![CDATA[Rofiqi, Harri Kurniawan]]>;
<![CDATA[Noersasongko, Edi]]>;
<![CDATA[Winarno, Sri]]>;
<![CDATA[Soeleman, M. Arief]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.4898
<![CDATA[Image-based classification of potato leaf diseases presents a significant challenge, particularly when data are collected in uncontrolled field environments. While Convolutional Neural Networks (CNNs) and Computer Vision have been widely used for plant disease identification, most previous studies relied on laboratory datasets with uniform lighting and backgrounds, limiting their real-world applicability. This study proposes an integrated framework that combines data augmentation, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and automated hyperparameter optimization through Optuna to enhance the robustness and accuracy of CNN-based models. A total of 3,076 high-resolution potato leaf images representing seven disease classes were evaluated across five CNN architectures and three training scenarios. The MobileNetV3-Large model achieved the best baseline performance with an accuracy of 0.863 and F1-score of 0.868, while Optuna-based optimization further improved performance to 0.895 accuracy, 0.913 precision, 0.906 recall, and 0.904 F1-score, demonstrating the effectiveness of adaptive optimization in improving model generalization. The integration of augmentation, SMOTE, and Optuna resulted in an intelligent and efficient system resilient to environmental variability, showing strong potential for automatic early detection of potato leaf diseases in real agricultural settings. This research contributes to the advancement of Informatics and Artificial Intelligence by promoting adaptive computer vision approaches for smart agriculture and real-world image-based diagnostic systems.]]>
<![CDATA[Enhancing Diagnostic Accuracy of Polycystic Ovary Syndrome Classification in Ultrasound Images Using a Hybrid Deep Learning Model of VGG16 and AlexNet ]]>
<![CDATA[Maisarah, Hj.]]>;
<![CDATA[Soeleman, M. Arief]]>;
<![CDATA[Pujiono, Pujiono]]>;
<![CDATA[Firdaus, Iqbal]]>;
<![CDATA[Firdaus, Gusti Aditya Aromatica]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.4932
<![CDATA[Diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound (USG) imaging still faces a major challenge in the form of inter-observer variability, which can lead to inconsistent diagnostic outcomes and increase the risk of misclassification. This limitation highlights the urgent need for an automated artificial intelligence (AI)–based system capable of performing ultrasound image classification with greater objectivity, accuracy, and consistency. This study aims to develop an automated PCOS classification model based on a hybrid Convolutional Neural Network (CNN) architecture that integrates VGG16 and AlexNet through a feature concatenation mechanism, following preprocessing and data augmentation steps to enhance model generalization. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and specificity as key metrics. Experimental results demonstrate that the VGG16–AlexNet hybrid model achieved the best performance, with an accuracy of 98.26%, precision of 97.90%, recall of 97.90%, F1-score of 97.90%, and specificity of 98.52%. These results outperform other hybrid configurations such as VGG16–MobileNetV2, VGG16–ResNet50, and VGG16–InceptionV3, each of which achieved accuracies above 96%. These findings confirm that combining the feature depth of VGG16 with the computational efficiency of AlexNet enables more comprehensive extraction of spatial and textural patterns in ultrasound images. Consequently, the proposed hybrid model offers a promising AI-driven diagnostic support system that not only enhances the accuracy of PCOS detection but also assists clinicians in making faster, more objective, and consistent medical decisions.]]>
<![CDATA[Improving the Performance of K-Means Algorithm using the Particle Swarm Optimization for Clustering Forest Fire ]]>
<![CDATA[Utami, Putri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.4949
<![CDATA[Forest and land fires are a recurring ecological disaster in Indonesia, particularly in West Kalimantan, where peatlands and tropical climates contribute to high vulnerability. Effective identification of fire-prone areas is critical for mitigation efforts, yet conventional clustering methods such as K-Means suffer from limitations, especially in determining optimal cluster numbers and centroid initialization. This study proposes an enhanced clustering approach by integrating the Particle Swarm Optimization (PSO) algorithm with K-Means to improve the accuracy of hotspot clustering in Mempawah Regency. The research utilizes hotspot and weather datasets from January 2023 to March 2024, incorporating variables such as temperature, humidity, rainfall, and wind speed. Data preprocessing includes normalization using Min-Max Scaling. PSO is applied to determine the optimal number of clusters (K) within a range of 2 to 10 by evaluating Davies-Bouldin Index (DBI), Silhouette Coefficient (SC), and inertia. Experimental results show that the optimal configuration—20 iterations and 20 particles—yields a DBI of 0.897 and an SC of 0.464, indicating standard cluster quality. Visual validation using PCA demonstrates clear cluster separation, supporting the evaluation results. Compared to visual methods like Elbow, which suggest K=3–4, the PSO-KMeans approach identifies K=10 as optimal, providing better clustering performance. This research highlights the effectiveness of swarm intelligence in enhancing spatial data modeling and supports strategic decision-making for local wildfire mitigation efforts.]]>
<![CDATA[Explainable Ensemble Learning for Depression Risk Classification Using Multidomain Behavioral Features ]]>
<![CDATA[Junianto, Erfian]]>;
<![CDATA[Nurkhodijah, Siti]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5009
<![CDATA[Depression is a growing global health concern, particularly among adolescents and university students. Despite the availability of standardized assessments, delays in early detection remain a major barrier to effective treatment. Digital behavioral data holds considerable potential for mental health assessment, but its utilization remains limited due to the absence of integrated and interpretable computational models. This study presents an interpretable machine learning framework for classifying depression risk using multi-domain behavioral features extracted from simulated digital life datasets. Three public datasets were integrated and mapped to five psychological clusters based on DSM-5 criteria: self-regulation, negative affect, cognitive strain, comparison and avoidance, and sleep disturbance. Two ensemble classifiers, Random Forest and XGBoost, were applied and evaluated using 10-fold stratified cross-validation. Depression risk was categorized into three levels: Low, Medium, and High. The Random Forest model achieved the highest accuracy (81%) and macro-averaged F1-score (0.81), showing strong performance especially in identifying transitional Medium-risk users. To enhance transparency, both global and local model interpretations were performed using SHapley Additive exPlanations (SHAP). Results revealed that digital stressors such as excessive screen time and disrupted sleep patterns were prominent in high-risk classifications, while mood stability and mindfulness were protective factors in low-risk groups. The proposed framework offers a scalable and explainable for early depression screening by integrating psychological theory with artificial intelligence methods. The findings contribute to the field of behavioral informatics by demonstrating the practical value of interpretable models in enhancing the reliability, transparency, and applicability of digital mental health systems and personalized behavioral monitoring.]]>
<![CDATA[Exploring Ensemble Architectures on Lung X-Ray Multi-Class Image for Classification Using Convolutional Neural Network and Random Forest ]]>
<![CDATA[Nuriansyah, Devin Garmenta]]>;
<![CDATA[Ayu, Putu Desiana Wulaning]]>;
<![CDATA[Hostiadi, Dandy Pramana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5016
<![CDATA[The lungs are vital organs that play an important role in the respiratory and circulatory systems. Early detection of lung diseases through medical images, especially Chest X-Ray (CXR), is still a challenge due to the limited amount of data and complexity in image interpretation. This research aims to develop an effective image classification approach for lung disease detection by comparing two main methods: direct training using Convolutional Neural Network (CNN) and a hybrid method involving feature extraction from CNN model, feature selection using Chi-Square method, and classification using Random Forest algorithm. To overcome data imbalance and increase variation, data augmentation techniques such as rotation, vertical and horizontal flipping, and zooming are used. Four popular CNN architectures are used in training, namely VGG16, ResNet-50, InceptionV3, and MobileNet. After training, features are extracted and stored in .csv format. Next, feature selection using the Chi-Square method and classification with Random Forest are performed. The experimental results show that direct CNN training achieves high accuracy, with MobileNet reaching the highest performance at 98.83%. However, this approach requires significant computational resources and longer training time. In contrast, the hybrid method offers competitive accuracy with lower computational demands. The findings highlight the potential of combining deep learning and traditional machine learning to create efficient, accurate, and resource-friendly medical image classification systems. This research has significant implications for supporting early diagnosis of lung diseases, reducing diagnostic workload for medical professionals, and enabling the development of deployable AI-assisted healthcare solutions in resource-limited settings.]]>
<![CDATA[An Evaluation of Self-Attentive Sequential Recommendation (SASRec) Algorithm Using Hyperparameter Tuning ]]>
<![CDATA[Wibowo, Agung Toto]]>;
<![CDATA[Hasmawati, Hasmawati]]>;
<![CDATA[Nurrahmi, Hani]]>;
<![CDATA[Salsabila, Imtitsal Ulya]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5158
<![CDATA[Sequential recommendation is a branch of Recommender Systems that aims to predict the next item a user will interact with based on their historical sequence of interactions. The main challenge in SR is to capture both short-term and long-term dependencies among items within a sequence. Self-Attentive Sequential Recommendation (SASRec) is a self-attention-based deep learning model designed to recognize sequential interaction patterns. Despite its effectiveness, the performance of SASRec is highly dependent on hyperparameter configurations, yet comprehensive evaluations remain limited. This research aims to evaluate the influence of SASRec's configuration through hyperparameter tuning on sequential recommendation performance. The hyperparameters used are hidden_size, inner_size, number of attention heads (num_heads), and number of layers (num_layers). The evaluation was conducted on two public datasets with different sparsity characteristics: MovieLens-1M (Sparsity ≈ 95.80%) and Amazon Musical Instruments (Sparsity ≈ 99.99%). In this study, Recall@k and MRR@k were used as performance metrics. The test results showed that hidden_size and inner_size had a significant positive impact on performance, especially on the dense dataset. The optimal hidden_size was obtained at hidden_size = 64 on the Amazon Musical Instrument dataset, and at hidden_size = 256 on the Movielens 1M dataset. The optimal inner_size was obtained at inner_size = 256 on both datasets. Meanwhile, the num_heads and num_layers hyperparameters did not provide a significant performance improvement. Furthermore, in the comparison between SASRec, GRU4Rec, and BERT4Rec, SASRec outperforms GRU4Rec and BERT4Rec in handling highly sparse datasets such as Amazon Musical Instruments obtained average recall@20 = 0.0678, and average MRR@20 = 0.0223.]]>
<![CDATA[Artificial Intelligence in Monetary Response: The Role of Investor Sentiment in the Effectiveness of Bank Indonesia’s Interventions ]]>
<![CDATA[Sumantiawan, Dody Indra]]>;
<![CDATA[Abdillah , M. Zakki]]>;
<![CDATA[Muhammad Kholilurrahman]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5184
<![CDATA[Exchange rate stability is a core pillar of macroeconomic resilience, especially for emerging economies like Indonesia. The effectiveness of Bank Indonesia’s (BI) monetary interventions in stabilizing the Rupiah depends not only on policy instruments but also on market perceptions and investor sentiment. This study examines the relationship between investor sentiment and the effectiveness of BI’s interventions by integrating Natural Language Processing (NLP), event study, and moderated regression analysis. The dataset spans 2023–2025 and includes daily exchange rate data, an investor sentiment index derived from financial forums and business news using VADER and TextBlob algorithms, and BI intervention records. An event study with a ±5 day window evaluates the short-term impact of interventions on exchange rate returns, while moderated regression analyzes the interaction between sentiment and interventions. Results indicate that BI interventions produce short-term exchange rate recovery, with a cumulative average abnormal return (CAAR) of 0.55% on the third day after intervention. Regression findings show that investor sentiment significantly influences Rupiah movements (p < 0.01), and the interaction between sentiment and interventions is also significant (p < 0.05), indicating greater effectiveness under positive or neutral sentiment. These findings underscore that intervention success is closely tied to market psychology. Therefore, BI should incorporate AI-driven sentiment analysis into policy design to enhance intervention effectiveness and strengthen public communication credibility. This study enriches the literature on behavioral macroeconomics and offers a data-driven framework for adaptive monetary policymaking in the digital economy.]]>
<![CDATA[Enhancement of YOLOv9 Model for Traffic Vehicle Detection using Augmentation Techniques ]]>
<![CDATA[Ashari, Imam Ahmad]]>;
<![CDATA[Syafei, Wahyul Amien]]>;
<![CDATA[Wibowo, Adi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5196
<![CDATA[Traffic vehicle detection is a crucial component in developing intelligent transportation systems, with object detection models like YOLO (You Only Look Once) often preferred for their speed and accuracy. However, challenges remain in detecting vehicles under diverse lighting conditions and small object scales, even with advanced models such as YOLOv9. To address these limitations, image augmentation techniques are employed to enhance model robustness by providing broader data variation. This study investigates the impact of multiple image augmentation methods on the YOLOv9t model for traffic vehicle detection. The techniques evaluated include Blur, Brightness Adjustment, Contrast Adjustment, Color Jitter, Cropping, Flipping, Noise Injection, Rotation, Scaling, and Zoom-In. Results reveal that Scaling and Brightness Adjustment significantly improve detection accuracy, achieving mAP50-95 values of 0.450 and 0.449, respectively. Conversely, methods such as Contrast Adjustment, Rotation, and Cropping produced unsatisfactory outcomes, with Contrast Adjustment performing the worst at only 0.167. Without augmentation, the baseline mAP50-95 was 0.378, emphasizing the vital role of augmentation in improving detection performance, especially under challenging conditions. These findings highlight the importance of selecting appropriate augmentation techniques to optimize YOLOv9t performance, with further improvements possible through combining multiple methods. Compared to approaches that solely focus on enhancing model architecture, the proposed augmentation-based strategy proves more effective in addressing real-world challenges, strengthening resilience against lighting variations and small object detection. This contribution supports the development of more accurate and reliable multilabel vehicle detection systems, advancing safer and more efficient intelligent transportation solutions.]]>
<![CDATA[Performance Analysis of Traditional Machine Learning Classifiers on LSTM-Extracted Features for Indonesian Sign Language System Recognition ]]>
<![CDATA[Ho, Patricia]]>;
<![CDATA[Santoso, Handri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5210
<![CDATA[Recognizing affix gestures in the Indonesian Sign Language System (SIBI) remains challenging due to subtle visual differences in hand shape and movement, often resulting in lower classification accuracy compared to other categories. This study aims to evaluate whether lightweight traditional and hybrid classifiers can provide competitive performance to deep learning models for SIBI recognition. Using a dataset of 21,351 gesture videos covering four categories (Affix, Alphabet, Number, and Word), features were extracted from MediaPipe keypoints and processed as frozen LSTM embeddings. Six classifiers (Random Forest, K-Nearest Neighbors, Naïve Bayes, Multilayer Perceptron, Support Vector Machine, and Hidden Markov Model) were evaluated with 5-fold stratified cross-validation using accuracy, precision, recall, and F1-score, with statistical significance tested through Friedman and Nemenyi analyses. Results show that MLP and RF achieved high performance in Alphabet, Number, and Word categories (above 96 percent accuracy), while Affix remained the most difficult, with MLP reaching 81.17 percent, outperforming the 68.17 percent from a prior BiLSTM model. This study provides a benchmark for hybrid model implementation in sign language recognition, showing that while traditional classifiers on deep features are effective and computationally lighter for general gestures, deep architectures remain superior for capturing the fine-grained temporal nuances critical for complex categories like affixes.]]>
<![CDATA[Optimizing Automatic Irrigation Duration for Grapevines in Greenhouses Using Multiple Linear Regression Analysis ]]>
<![CDATA[Dian Pertiwi, Kharisma Monika]]>;
<![CDATA[Alfarabi, Trenady]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52436/1.jutif.2026.7.2.5289
<![CDATA[Greenhouses offer a controllable microclimate for high‑value horticulture, yet manual irrigation and single‑sensor threshold rules remain inefficient and error‑prone for grapevine cultivation in tropical conditions. This study designs and implements an Internet‑of‑Things (IoT) automatic irrigation system that employs an interpretable multiple linear regression (MLR) model as the decision core, using air temperature and soil moisture—acquired via DHT11 and capacitive soil‑moisture sensors—to estimate irrigation duration in real time. The model is trained on greenhouse measurements and deployed for low‑latency edge inference to actuate valves with duration‑to‑volume conversion, enabling precise and adaptive water delivery. Experimental evaluation shows strong predictive performance (MSE = 0.15, MAPE = 1.44%, R² = 0.98), indicating high accuracy and reliable generalization for operational control. The primary contributions are: (i) a lightweight, explainable regression formulation tailored to tropical grapevines that outperforms single‑parameter baselines; (ii) an end‑to‑end, edge‑deployable IoT pipeline that reduces computational and energy costs while maintaining real‑time autonomy; and (iii) an engineering blueprint that is scalable and maintainable for smallholder contexts. The impact for Informatics/Computer Science lies in demonstrating a practical ML‑on‑the‑edge reference design—combining interpretable modeling, sensor fusion, and actuation—that advances sustainable computing for precision agriculture, improves resource efficiency, and supports robust, replicable deployment of smart‑irrigation systems in data and power‑constrained environments.]]>
