JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Nusa Mandiri is a scientific journal containing research results written by lecturers, researchers, and practitioners who have competencies in the field of computer science and technology. This journal is expected to develop research and provide meaningful contributions to improve research resources in the fields of Information Technology and Computer Science. JITK is published by the University of Nusa Mandiri Research Center in open access and free. Each published article has a digital object identifier (DOI): Prefix: 10.33480. The JITK journal has obtained an accreditation value for the SINTA 2, to send scientific articles to JITK, first read the article shipping instructions at the next link. P-ISSN: 2685-8223 & E-ISSN: 2527-4864
IMPLEMENTATION OF RANDOM FOREST FOR ANIMAL PROTEIN CLASSIFICATION THROUGH HYPERPARAMETER OPTIMIZATION
Accurate identification of animal protein types is crucial to ensure food authenticity and safety, particularly in the context of compliance with halal principles. This study aims to implement the Random Forest (RF) algorithm to classify four types of animal protein—broiler chicken, free-range chicken, pork, and beef through hyperparameter optimization using GridSearchCV. The dataset was evaluated using 5-fold cross-validation, and feature importance analysis was conducted to identify the variables that contributed most to classification. Results showed that RF with optimized hyperparameters achieved a test accuracy of 92.81%, with macro-average precision, recall, and F1-score of 93%. The model performed best for the broiler chicken and pork classes, while the beef class exhibited a higher misclassification rate, likely due to the similarity of spectral characteristics among classes. ODOR, CO₂, H₂, NH₃, and VOC were identified as the key indicators for distinguishing animal protein types. This study contributes to halal authentication by integrating FTIR spectral data with optimized Random Forest, enabling efficient and accurate classification. Although RF proved reliable and capable of handling high-dimensional data, the study is limited by dataset size and spectral feature complexity. Future research is recommended to explore deep learning architectures, such as Convolutional Neural Networks (CNN), with larger FTIR datasets to improve model generalization and robustness
Hak Cipta (c) 2026 Ridho Ikhram, Anton Yudhana, Imam Riadi http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT CLASSIFICATION MODEL BASED ON COMPARATIVE STUDIES USING MACHINE LEARNING TECHNOLOGY
The development of social media has generated large amounts of text data, which is a valuable source for sentiment analysis. This study aims to conduct a comparative study of sentiment classification models on Indonesian-language YouTube comments, specifically comparing lexicon-based approaches, traditional machine learning models (Naive Bayes), and deep learning models (LSTM). Data was collected from YouTube videos themed around the youth generation and demographic bonuses, totaling 9,162 comments that underwent comprehensive text preprocessing. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model outperforms Naive Bayes with an accuracy of 78.78% and an average F1-score of 0.79, compared to Naive Bayes, which only achieves an accuracy of 62.08% and an F1-score of 0.54. Although LSTM offers higher performance, the Naive Bayes model remains relevant due to its simplicity and efficiency. This study makes an important contribution to the selection of sentiment classification models for the Indonesian language and suggests the development of hybrid models and the use of contextual features for more optimal results. The LSTM model outperforms Naive Bayes with an accuracy of 82.15% (improved from 78.78% through enhanced regularization) and an average F1-score of 0.84. Comprehensive hyperparameter tuning via grid search and expanded manual annotation (40% of the dataset with κ=0.83) ensures robust model evaluation and reduces labeling bias. The study provides methodologically sound benchmarks for Indonesian sentiment analysis
Hak Cipta (c) 2026 J PRAYOGA, T. Irfan Fajri, Febri Dristyan http://creativecommons.org/licenses/by-nc/4.0
TOGAF ADM - BASED ENTERPRISE ARCHITECTURE FOR TANTAN DIGITAL VILLAGE
The acceleration of digital transformation in rural governance requires an integrated information system to ensure efficient, transparent, and accountable public services. Yet, many villages including Tantan Village in Muaro Jambi Regency still operate with fragmented applications and redundant data. This study proposes a comprehensive enterprise architecture blueprint for Tantan Village using the TOGAF ADM framework, specifically adapted to the operational and institutional characteristics of rural public administration. Employing a qualitative case study approach, the research develops a four-layered architecture encompassing business, data, application, and technology domains. Theoretically, this study advances the understanding of how enterprise architecture can be localized for small-scale government entities; practically, it provides a replicable model that supports sustainable digital village transformation in Indonesia.
Hak Cipta (c) 2026 Bujangdek, Setiawan Assegaff, Jasmir http://creativecommons.org/licenses/by-nc/4.0
SUPPORT VECTOR MACHINE TO CLASSIFY SENTIMENT REVIEWS ON GOOGLE PLAY STORE
This research addresses the "rating-content discrepancy" on the Google Play Store, where numerical star ratings often conflict with the actual sentiment of textual reviews. Utilizing the CRISP-DM framework, the study evaluates the effectiveness of machine learning in resolving these inconsistencies by classifying Instagram user reviews into positive and negative categories. Two primary algorithms were compared using a dataset of 600 reviews. The Support Vector Machine (SVM) model demonstrated high efficacy with an accuracy of 0.84. In contrast, the K-Nearest Neighbors (KNN) model performed poorly, achieving an accuracy of only 0.48. This significant performance gap highlights SVM's superior ability to handle high-dimensional text data through optimal hyperplane separation. The research further integrated the Streamlit library to create an interactive web interface for real-time sentiment prediction and result visualization. Ultimately, this study confirms that a structured CRISP-DM approach combined with SVM provides a robust solution for automated opinion mining, offering a reliable methodology for future data science applications in social media analysis
Hak Cipta (c) 2026 Agus Nursikuwagus, Suherman, Heri Purwanto, Tono Hartono http://creativecommons.org/licenses/by-nc/4.0
PUBLIC SECTOR INNOVATION IN SMART CITIES: A FRAMEWORK OF AMBIDEXTROUS AI GOVERNANCE
The increasing adoption of artificial intelligence (AI) in smart city public services has intensified governance challenges related to accountability, data security, transparency, and risk management. Existing AI governance studies tend to emphasize either innovation enablement or control mechanisms, offering limited guidance on how public institutions can balance both simultaneously. This study addresses this gap by proposing an Ambidextrous AI Governance Framework grounded in COBIT 2019, which systematically integrates exploration-oriented innovation and exploitation-oriented control within public-sector governance. Using a Design Science Research (DSR) approach, the framework was developed and evaluated through a 2025 case study at the Jakarta Provincial Communication and Information Agency. Validation was conducted via expert interviews, document analysis, and simulation-based governance maturity assessments. The findings indicate that baseline AI governance maturity remains at Levels 2–3, while simulation results suggest potential advancement toward Levels 4–5, particularly in risk management (APO12) and security services (DSS05). The study contributes theoretically by operationalizing organizational ambidexterity within public-sector AI governance and practically by offering structured guidance to support secure, transparent, and sustainable AI adoption in smart city environments.
Hak Cipta (c) 2026 Agustinus Fritz Wijaya, Merryana Lestari, Mega Kartika Sari, Rio Ferdiandinata http://creativecommons.org/licenses/by-nc/4.0
TRANSFER LEARNING-BASED CLASSIFICATION OF BELL PEPPER LEAF DISEASES USING VGG16 AND EFFICIENTNETB3 ARCHITECTURES
Diseases affecting pepper leaves can significantly reduce crop productivity and quality, while manual disease identification remains subjective, time-consuming, and prone to error. Therefore, an accurate automated classification system is required to support early disease detection. This study aims to evaluate and compare the performance of a conventional Convolutional Neural Network (CNN) with two transfer learning–based architectures, VGG16 and EfficientNetB3, for classifying pepper leaf images into healthy and bacterial spot classes, as well as to analyze the impact of applying a soft voting ensemble method on classification performance. The dataset was obtained from Kaggle and divided into training, validation, and test sets. Image preprocessing included resizing all images to 224×224 pixels and applying data augmentation to improve model generalization. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that EfficientNetB3 outperforms the conventional CNN and VGG16 models. Furthermore, the application of the soft voting ensemble enhances prediction stability, achieving an accuracy of 99.68% on the test dataset with balanced precision and recall across both classes. These findings demonstrate that the integration of transfer learning and soft voting ensemble methods is an effective approach for image-based pepper leaf disease classification under the experimental conditions, and provides a basis for further validation using more diverse datasets.
Hak Cipta (c) 2026 Siti Nurhasanah Nugraha, Evita Fitri, Muji Ernawati http://creativecommons.org/licenses/by-nc/4.0
DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING
The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
Hak Cipta (c) 2026 Ratih Anggraeni, Purwadi, Pungkas Subarkah http://creativecommons.org/licenses/by-nc/4.0
HYBRID TRANSFER LEARNING AND ADVANCED DATA AUGMENTATION FOR MULTICLASS BRAIN TUMOR CLASSIFICATION USING EFFICIENTNET
Accurate Accurate brain tumor diagnosis from MRI images remains challenging due to dataset limitations, class imbalance, and high morphological variability across tumor types. Existing deep learning approaches often yield suboptimal results when trained on small or imbalanced datasets. This study proposes a hybrid learning strategy that integrates transfer learning with advanced data augmentation to classify four brain tumor categories: glioma, meningioma, pituitary adenoma, and normal tissue. Using a large-scale dataset of 7,023 MRI images, the proposed framework incorporates Mixup, CutMix, and a comprehensive augmentation pipeline with an optimized EfficientNet-B0 architecture. The model achieves a test accuracy of 99.05% with F1-scores of 0.99, representing a 4.05 percentage point improvement over a baseline InceptionV3 model (95.00%) and outperforming ResNet-based approaches (93.80%) reported in previous studies. This quantitative improvement demonstrates the effectiveness of combining modern CNN architectures with advanced augmentation strategies. The streamlined architecture and high accuracy make the method suitable for deployment in resource-constrained healthcare environments. These results indicate that hybrid augmentation and transfer learning can deliver clinically meaningful performance for early brain tumor identification, offering a scalable and practical solution for computer-aided medical diagnosis
Hak Cipta (c) 2026 A M H Pardede, Riki Winanjaya, Juni Ismail http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE EVALUATION OF LIGHTWEIGHT DEEP LEARNING MODELS FOR BORAX-CONTAMINATED MEATBALL IMAGE CLASSIFICATION
Food safety, particularly concerning the use of illegal additives such as borax in processed meat products like meatballs, remains a critical issue in Indonesia. This study analyzes the performance of several lightweight deep learning models based on Convolutional Neural Networks (CNN) and Transformers to classify images of meatballs containing borax, enabling their deployment on resource-constrained devices such as smartphones. Data collection involved capturing 1,429 images of meatballs with and without borax using a smartphone camera under varying lighting conditions and shooting angles. The five main architectures evaluated were ConvNeXt-Nano, Swin-Tiny, ViT-Tiny, MobileViT-XS, and EfficientNet-B0. Hyperparameter optimization was conducted using Optuna, followed by training with a 5-fold cross-validation scheme. Model evaluation metrics included accuracy, precision, recall, F1 score, and inference speed. The results show that MobileViT-XS was the best-performing architecture, achieving 65.93% accuracy, 0.703 precision, 0.706 recall, 0.659 F1 score, and efficient memory consumption (45.94 MB). These findings indicate that a hybrid approach combining the strengths of CNNs and Transformers can achieve an optimal balance between detection accuracy and computational efficiency. Therefore, such models have the potential to be applied as food safety detection systems on devices with limited resources
Hak Cipta (c) 2026 Aryo Michael, Ireve Devi Damayanti http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE STUDY OF GENERATIVE AI TOOLS IN VISUAL COMMUNICATION DESIGN EDUCATION: CREATIVITY AND USABILITY
The integration of generative artificial intelligence (AI) into visual communication design education presents opportunities to enhance creativity and usability in learning. This study compares the effectiveness of three generative AI tools (MidJourney, DALL·E, and Adobe Firefly) in supporting students’ creative outcomes and perceived usability, while also examining their broader pedagogical role in design education. A quasi-experimental design was conducted with 30 undergraduate students who each produced two poster designs: one manually and one with AI assistance. Creativity was evaluated using the Consensual Assessment Technique (CAT) by expert judges, and usability was measured using the System Usability Scale (SUS). Results showed that AI-assisted designs achieved significantly higher creativity scores (M = 4.3 vs. 3.2, p < 0.05) and usability ratings (range M = 74–82) compared to manual designs, with MidJourney rated highest in creativity and Adobe Firefly in usability. These findings provide empirical evidence that generative AI can act as a catalyst for creativity and usability in design education, offering theoretical insights into human–AI co-creation and practical implications for curriculum integration. Limitations include the small sample size and the study’s focus on a single academic program, which may affect generalizability.
Hak Cipta (c) 2026 Yana Erlyana, M Garry Saputra http://creativecommons.org/licenses/by-nc/4.0
DEVELOPING A MICRO-ENTERPRISE E-READINESS FRAMEWORK: A CASE STUDY FROM INDONESIA
Micro-enterprises play a vital role in Indonesia’s economy but continue to face persistent barriers in adopting information and communication technology (ICT). Understanding E-Readiness is essential for implementing effective digital interventions, especially for micro-enterprises. To address the lack of validated frameworks for assessing digital readiness at the micro-enterprise level, this study develops and empirically tests a novel E-Readiness Assessment Framework specifically designed for micro-enterprises. The proposed conceptual model consists of four dimensions: Technology, Organisation, External Environment, and Human Resources, which are derived from established e-readiness models. This study also proposed the measurement indicators that have been adapted to the characteristics of micro enterprises. Using quantitative data from 641 food and beverage (F&B) micro-enterprises in Batu City, Indonesia, exploratory factor analysis (EFA) was applied to evaluate construct validity. Despite the elimination of five indicators due to insufficient communality value, the overall model structure remained statistically valid. Subsequently, factor analysis was succeeded by the calculation of E-Readiness index using weighted aggregation and normalisation methods. The resulting E-Readiness Index for Batu City was 46.47, with 57.10% of enterprises classified as “Not Ready,” primarily due to technological and infrastructural limitations. The proposed model in this study efficiently assesses e-readiness at the micro-enterprise level and is adaptable for application in different regions or business sectors. This model also provides valuable insights for policymakers in formulating targeted digital support initiatives. Future research may consider expanding the scope of indicators and validating the model using confirmatory analysis.
Hak Cipta (c) 2026 Nori Wilantika, Fitri Kartiasih, Ernawati Pasaribu, Aisha Arthamevia, Yunarso Anang, Achmad Nizar Hidayanto http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION
Mango (Mangifera indica Linn.) is a nutrient-rich fruit, yet leaf diseases caused by microorganisms can significantly reduce crop productivity. Early detection is essential to prevent further damage and support effective disease management. This study proposes an optimized mango leaf disease prediction model using a multi-layer perceptron neural network (MLP-NN). Image-based feature extraction is performed using the Inception v3 architecture to obtain high-level color and texture features that improve classification performance. Unlike previous studies that rely solely on manually engineered features or full CNN training, this research introduces a hybrid approach that integrates deep feature extraction with MLP-NN optimization, offering a lightweight yet highly accurate alternative. Several hyperparameter combinations, including activation functions (ReLU, tanh, sigmoid) and optimization algorithms (Adam and SGD), were evaluated using the Orange platform. The optimized MLP-NN model with ReLU and Adam achieved the highest accuracy of 93.5%, demonstrating better stability and training efficiency compared to other configurations. These findings highlight the novelty and advantages of the proposed method, showing improved accuracy with lower computational cost relative to many existing approaches. This study provides an efficient solution for mango leaf disease prediction and supports future development of automated plant disease detection systems
Hak Cipta (c) 2026 Budi Triandi, Lili Tanti http://creativecommons.org/licenses/by-nc/4.0
EVALUATING CLUSTERING METHODS FOR SEMANTIC REPRESENTATION OF DISASTER NEWS USING BERT EMBEDDINGS AND HBDSCAN
Natural disasters that frequently occur in Indonesia demand a fast and accurate information monitoring and analysis system through online news sources. This study aims to identify topic patterns related to natural disasters in Indonesia using news articles from Detik.com through a semantic clustering approach. A total of 1,000 articles were collected, preprocessed, and represented using the Sentence-BERT (SBERT) model to capture contextual relationships between sentences. The vector representations were then clustered using three methods: K-Means, Agglomerative Hierarchical Clustering, and HDBSCAN. The performance of each method was evaluated using the Silhouette Score, Davies–Bouldin (DB) Index, and Calinski–Harabasz (CH) Index. The results show that HDBSCAN achieved the best performance with a Silhouette Score of 0.215, a DB Index of 1.557, and a CH Index of 18.102, outperforming Agglomerative (0.028, 3.945, 29.669) and K-Means (0.055, 3.678, 36.778). Moreover, the HDBSCAN model achieved the highest coherence score of 0.8669, indicating strong semantic consistency within clusters. Five coherent clusters emerged, representing major disaster themes: landslides, earthquakes, tornadoes, flash floods, and volcanic activity. The visualization of word clouds for each cluster reinforced the interpretation of these disaster topics. Overall, the combination of SBERT and HDBSCAN effectively groups news articles based on semantic similarity. These findings highlight the potential of Natural Language Processing (NLP) to enhance data-driven media monitoring, support early warning systems, and strengthen disaster communication and mitigation strategies in Indonesia
Hak Cipta (c) 2026 Ariska Fitriyana Ningrum, Dannu Purwanto, Abdel Nasser Sharkawy http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR PAP SMEAR IMAGE CLASSIFICATION IN CERVICAL CANCER DETECTION
Early detection of cervical cancer through Pap smear image analysis plays a crucial role in reducing mortality rates associated with this disease. This study aims to optimize the VGG16 architecture to improve the classification accuracy of Pap smear images. The proposed method employs transfer learning with pre-trained ImageNet weights, customization of fully connected layers, and data augmentation techniques to enhance the diversity of training images. Experimental results demonstrate a significant improvement in training accuracy, reaching 98.50%, while validation accuracy remained stable at 88.24%, indicating potential overfitting. Performance testing on unseen data yielded an accuracy of 80%, with high precision for the negative class but low recall for the positive class, suggesting a bias toward the majority class. These findings highlight the need for additional strategies, such as data balancing and hybrid method integration, to improve sensitivity to positive cases. This research contributes to the development of adaptive deep learning-based classification models that support clinical decision-making in cervical cancer screening and opens opportunities for further research on model optimization and dataset expansion.
Hak Cipta (c) 2026 Odi Nurdiawan, Heliyanti Susana, Ade Rizki Rinaldi, Ahmad Asyraful Hijrah, Indah Diniarti http://creativecommons.org/licenses/by-nc/4.0
MACHINE LEARNING TO IDENTIFY ELIGIBILITY OF STUDENTS RECEIVING SINGLE TUITION RELIEF
The cost of higher education in Indonesia varies greatly and often becomes a financial burden for students. Socio-economic factors such as parental income, occupation, number of dependents, vehicle ownership, and place of residence influence the determination of single tuition as regulated by the Ministry of Education Regulation No. 55 of 2013. This study aims to classify freshmen eligibility for single tuition relief using five machine learning models: RF, LR, KNN, SVM, and NB. The dataset contains 2000 rows of data with six socio-economic attributes divided into two classes: eligible and ineligible. The data were split into 80% training and 20% testing, and model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Results show that without SMOTE, all models suffer from severe majority-class bias, yielding critically low recall for the minority class SVM = 0.014; NB = 0.004. SMOTE significantly improves minority-class detection, with RF and SVM achieving the highest performance F1-scores of 0.820 and 0.801, and ROC-AUC of 0.966 and 0.990, respectively. SHAP analysis identifies Number of Dependents of Parents as the most influential predictor across all models, highlighting its central role in financial need assessment. These findings demonstrate that combining SMOTE with ensemble or margin-based models enhances classifiying fairness and sensitivity in educational support systems. The future work recommend expanding features to include behavioral, academic, and regional indicators, using multi-institutional data, and exploring deep learning or advanced resampling methods to enhance generalizability and robustness
Hak Cipta (c) 2026 M. Ghofar Rohman, Zubaile Abdullah, Shahreen Kasim, M Ulul Albab http://creativecommons.org/licenses/by-nc/4.0
A HYBRID BERT–GNN FOR DETECTING HOAXES AND NEGATIVE CONTENT IN INDONESIAN SOCIAL MEDIA
The rapid spread of hoaxes on social media threatens public trust and information integrity, especially within the Indonesian digital landscape. This study proposes a hybrid deep learning model that integrates transformer-based semantic representation from IndoBERT with Graph Neural Networks (GNNs) to enhance hoax detection performance. A heterogeneous social graph is constructed to model relationships among posts, users, and news sources, where post node features are extracted from the [CLS] embeddings of a fine-tuned IndoBERT. The GNN component consists of two graph convolutional layers with ReLU activation and dropout, followed by a multilayer perceptron classifier for binary classification. Experiments conducted on the Indonesia False News dataset (Kaggle) employ SMOTE resampling to handle class imbalance and 5-fold stratified cross-validation for robust evaluation across three configurations: BERT-only, GNN-only, and the proposed BERT–GNN hybrid model. The hybrid model achieves an average F1-score of 0.89 ± 0.01 and ROC-AUC of 0.92 ± 0.01, outperforming both single-model baselines while maintaining a balanced precision–recall trade-off. These results confirm that combining contextual semantic understanding with relational graph topology substantially enhances accuracy, robustness, and generalization in detecting hoaxes within Indonesian-language social media content
Hak Cipta (c) 2026 Khairunnisa, Khairunnas, Sutriawan http://creativecommons.org/licenses/by-nc/4.0
HYBRIDIZATION OF FASTTEXT-BLSTM AND BERT FOR ENHANCED SENTIMENT ANALYSIS ON SOCIAL MEDIA TEXTS
The development of internet technology and social media has driven the increasing use of sentiment analysis to understand public opinion. This study aims to improve the classification performance of sentiment analysis by proposing a hybrid model that combines FastText-BLSTM and BERT. The dataset used consists of 900 Indonesian-language Netflix app user reviews obtained through crawling using Google Play Scraper. The research stages include text preprocessing, feature extraction using FastText and BERT, and classification using BLSTM, which are then combined in a concatenation layer to produce a richer feature representation. Experimental results show that the FastText-BLSTM-BERT hybrid model provides the best performance with an accuracy of 94.22%, a precision of 95.98%, a recall of 95.68%, and an F1-score of 95.83%. This achievement is superior to the single models of FastText-BLSTM and BERT. The main novelty of this research lies in the integration of contextual embeddings from BERT with subword-level semantic and sequential representations from FastText-BLSTM, which has not been extensively explored in prior studies on Indonesian sentiment analysis. This hybridization demonstrates significant improvement in model generalization and robustness for low-resource language texts
Hak Cipta (c) 2026 Jasmir, Maria Rosario, Irawan Irawan, Agus Siswanto, Tiko Nur Annisa http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING TRANSFORMER-BASED LEARNING MODEL WITH TABTRANSFORMER FOR PREDICTING ANTIBIOTIC SUSCEPTIBILITY FROM MICROBIOLOGY MEDICAL RECORDS
Antimicrobial Resistance (AMR) has become a growing threat due to the increase in infections that are unresponsive to conventional therapies. Therefore, the development and optimization of Transformer-based Deep Learning using TabTransformer was employed to model the complex interactions between categorical features. This model was trained to predict antibiotic susceptibility at the individual culture level using the Antibiotic Resistance Microbiology Dataset (ARMD). To address the challenge of highly imbalanced data, the methodology applied includes extensive feature engineering to create historical and clinical variables, as well as the use of Focal Loss during training. After optimization, the final model demonstrated excellent discriminatory ability, with an Area Under the ROC Curve (AUC-ROC) of 0.93 and balanced classification performance, yielding a macro average F1-score of 0.82. Interpretability analysis using SHAP confirmed that patient clinical history and prior drug exposure were the most dominant predictive factors. These findings suggest that the Transformer-based Deep Learning architecture using TabTransformer, combined with clinically relevant feature engineering, can produce a reliable and evidence-based predictive tool
Hak Cipta (c) 2026 Feri Sulianta, Endang Amalia, Rosalin Samiharjo, Noval Eka Herdinata http://creativecommons.org/licenses/by-nc/4.0
EVALUATION OF THE EFFECTIVENESS AND USER EXPERIENCE OF THE SALI EDUCATION APPLICATION USING UEQ
CSA (Child Sexual Abuse) poses serious risks to preschool-aged children, yet culturally sensitive and developmentally appropriate learning tools remain scarce in Indonesia. This study evaluates the effectiveness and user experience of SALI, a gamified mobile application designed to foster early childhood body safety awareness. SALI integrates interactive storytelling, animated scenarios, and digital quizzes to help children recognize safe and unsafe touches in an engaging way. A pre–posttest design was applied with 46 children aged 4–6 years selected through purposive sampling. Children’s knowledge was measured using an in-app digital quiz, while parents assessed the app’s usability through the User Experience Questionnaire (UEQ), a standardized and validated instrument. Results showed that mean scores increased from 70.00 (SD = 31.55) in the pre-test to 94.98 (SD = 8.69) in the post-test. The Wilcoxon Signed Rank Test confirmed a significant improvement (Z = ­–5.382, p < 0.001), with an average N-Gain of 0.86 categorized as high. UEQ outcomes indicated positive ratings across all six dimensions, with attractiveness (M = 2.66) and efficiency (M = 2.61) scoring highest, while perspicuity (M = 2.18) scored lowest but remained within a positive range. These findings suggest that SALI not only improves children’s understanding of protective behaviors but also provides a satisfactory user experience for parents. The study contributes to both theory and practice by demonstrating how gamified mobile learning can serve as a foundation for developing adaptive and engaging CSA prevention curricula in early childhood education.
Hak Cipta (c) 2026 Oky Irnawati, Kusmayanti Solecha, Yoseph Tajul Arifin, Sutan Arlie Johan http://creativecommons.org/licenses/by-nc/4.0
RTOS-BASED SYSTEM FOR TODDLER NUTRITIONAL STATUS DETECTION
Determining the nutritional status of toddlers is essential for monitoring growth and preventing long-term health problems. Manual assessment requires significant time and is prone to human error; therefore, an automatic detection system based on height and weight parameters is needed. This study aims to develop a Real-Time Operating System (RTOS)–based system to detect the nutritional status of children aged 24–60 months, capable of managing task priorities, ensuring timely execution, and preventing race conditions using semaphores. The system employs an ultrasonic sensor to measure height, load cell sensors to measure body weight, and a web-based interface to input gender and age. Nutritional classification is determined through Z-score calculations using WHO reference data. Tests conducted on 200 children in various locations showed that the ultrasonic sensor achieved an average absolute error of 0.39 cm, a relative error of 0.409%, and an accuracy of 99.59%, while the load cell sensor achieved an average absolute error of 0.22 kg, a relative error of 1.587%, and an accuracy of 98.41%. The average execution times for the measurement and Z-score computation tasks were 4014.4 ms and 11.31 ms, respectively. The nutritional status classification results showed accuracy levels of 99.5% for Weight-for-Age (W/A), 99.5% for Height-for-Age (H/A), and 97.5% for Body Mass Index-for-Age (BMI/A) compared with manual assessments. The developed system demonstrated reliable performance in measurement and classification, with results consistent with conventional methods, indicating its potential as an efficient and accurate tool to assist healthcare workers in monitoring toddler nutrition status
Hak Cipta (c) 2025 Arif Rahmawan, Rahmi Hidayati, Kartika Sari http://creativecommons.org/licenses/by-nc/4.0
ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS
Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.
Hak Cipta (c) 2025 Zaqi Kurniawan, Rizka Tiaharyadini, Windhy widhyanty , Puguh Jayadi, Windhy widhyanty http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE ANALYSIS OF HYPERPARAMETER OPTIMIZATION TECHNIQUES ON LIGHTGBM FOR ASTHMA PREDICTION
This study presents a comparative study of hyperparameter optimization methods applied to the Light Gradient Boosting Machine (LightGBM) algorithm for asthma prediction. Traditional machine learning models often face limitations in accuracy and generalization capabilities due to suboptimal hyperparameter configurations. To address these challenges, this study evaluates and compares four approaches: Default LightGBM, RandomizedSearchCV, Optuna Optimization, and Bayesian Optimization. Experimental results show that Bayesian Optimization provides the best performance with an accuracy of 78%, a precision of 0.7778, a recall of 0.7778, an F1-score of 0.7778, and an ROC-AUC of 0.975. These findings emphasize the importance of selecting an appropriate optimization strategy to improve model performance in clinical prediction tasks. Overall, this study confirms the effectiveness of Bayesian Optimization in improving the predictive capabilities of LightGBM and provides an important contribution to the development of decision support systems in healthcare, particularly in the diagnosis and management of asthma
Hak Cipta (c) 2025 Zulfian Azmi, Rina Julita, Novica Irawati, Sofyan Pariyasto, Ellanda Purwawijaya http://creativecommons.org/licenses/by-nc/4.0
DEVELOPMENT OF A SMART PARKING SYSTEM USING AUTOMATIC DEBIT AND OPTICAL CHARACTER RECOGNITION
The current parking infrastructure predominantly relies on traditional or semi-automatic mechanisms, leading to significant inefficiencies during peak hours. This study proposes the development of a fully automated smart parking system utilizing locally sourced Indonesian components to reduce dependence on imported parts. The proposed Auto-Debit Smart Parking System incorporates Optical Character Recognition (OCR) for vehicle identification and automated payment, improving both accuracy and operational efficiency. The system consists of two primary modules: server software for gate control and an image-processing host application. Space Vector Pulse Width Modulation (SVPWM) is employed for switching control, and communication is facilitated via wired or wireless channels using the RS232C standard. Vehicle entry and exit are detected by sensors that transmit signals to the Command TX module. To evaluate real world applicability, the system was implemented and tested in various public and commercial environments, including office buildings, shopping malls, and open parking areas.These testing sites represent common urban parking conditions with varying lighting, network connectivity, and traffic density, allowing the system’s adaptability and reliability to be analyzed comprehensively. An experimental research method is adopted, encompassing prototype development, testing, data acquisition, and performance evaluation. The results indicate reduced operational costs and enhanced user convenience, validating the system’s effectiveness in supporting modern, efficient parking management
Hak Cipta (c) 2025 Ninik Sri lestari, Rahmad Hidayat, Herlina Herlina, Sukirno Sukirno http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA
Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
Hak Cipta (c) 2025 Abdullah Ahmad, Dedy Hartama, Solikhun Solikhun, Poningsih Poningsih http://creativecommons.org/licenses/by-nc/4.0
PREDICTIVE MODEL FOR COOPERATIVE LOAN RECIPIENT ELIGIBILITY USING SUPERVISED MACHINE LEARNING
Non-performing loans remain a critical challenge for cooperatives as they can undermine financial stability, erode member trust, and impede institutional growth. This study develops a predictive model for cooperative loan eligibility using supervised machine learning techniques and a novel three-class classification framework, Approved, Consideration, and Rejected, to support more objective and transparent decision-making. A dataset of 1,000 borrower records containing demographic and financial attributes was analyzed using Naive Bayes, Decision Tree, and Random Forest algorithms implemented in RapidMiner. The Random Forest algorithm achieved the best predictive performance with an accuracy of 96.02%, demonstrating its robustness and reliability compared to the other models. The proposed three-class system differentiates this study from conventional binary classification approaches, enabling finer distinctions among borrower categories and promoting fairness in cooperative credit evaluations. The findings provide practical guidance for cooperatives to adopt data-driven, transparent, and accountable decision-making systems that reduce manual bias and strengthen financial inclusion. Overall, the proposed three-class model built through a supervised learning framework offers a reliable, fair, and scalable solution to support sustainable lending practices and enhance risk management in cooperative institutions.
Hak Cipta (c) 2025 Rajunaidi Rajunaidi, Herman Yuliansyah, Sunardi Sunardi, Murinto Murinto http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING DECISION TREE PERFORMANCE WITH RECURSIVE FEATURE ELIMINATION FOR HIGH-DIMENSIONAL MUSHROOM CLASSIFICATION
Classifying mushroom species presents a significant challenge within biological data analysis because of the wide variety of species and their distinct attributes. This research investigates the effectiveness of the Decision Tree classifier for mushroom categorization by comparing two splitting criteria, the Gini Index and Entropy. Additionally, the study employs the Recursive Feature Elimination (RFE) method for dimensionality reduction to enhance model efficiency and performance. The dataset was collected, cleaned, and analyzed exploratorily before feature selection was conducted using RFE. The Decision Tree model was trained and evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that applying RFE improved computational efficiency without compromising model accuracy. The Gini criterion provided more stable results across all metrics, while Entropy demonstrated higher precision in certain cases. Model optimization through parameter tuning produced the best parameter combination at max_depth = 5, min_samples_leaf = 5, and min_samples_split = 10. This study concludes that integrating RFE with the Decision Tree can significantly enhance the performance of high-dimensional dataset classification. The findings are expected to serve as a reference for developing efficient and accurate biological data classification models
Hak Cipta (c) 2025 Safrizal Safrizal, Lili Tanti, Yan Yang Thanri http://creativecommons.org/licenses/by-nc/4.0
CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK
Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
Hak Cipta (c) 2025 Andi Ahmad Taufiq, Hanum Zalsabilah Idham, Muh Fuad Zahran Firman, Andi Baso Kaswar, Dyah Darma Andayani, Muhammad Fajar B, Abdul Muis Mappalotteng, Andi Tenriola http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE STUDY OF TRANSFORMER-BASED MODELS FOR AUTOMATED RESUME CLASSIFICATION
This study presents a comparative evaluation of transformer-based models and traditional machine learning approaches for automated resume classification—a key task in optimizing recruitment workflows. While traditional approaches like Support Vector Machines (SVM) with TF-IDF demonstrated the highest performance (93.26% accuracy and 95% F1-score), transformer models such as DistilBERT and RoBERTa showed competitive results with 93.27% and 91.34% accuracy, respectively, and fine-tuned BERT achieved 84.35% accuracy and an F1-score of 81.54%, indicating strong semantic understanding. In contrast, Word2Vec + LSTM performed poorly across all metrics, highlighting limitations in sequential modelling for resume data. The models were evaluated on a curated resume dataset available in both text and PDF formats using accuracy, precision, recall, and F1-score, with preprocessing steps including tokenization, stop-word removal, and lemmatization. To address class imbalance, we applied stratified sampling, macro-averaged evaluation metrics, early stopping, and simple data augmentation for underrepresented categories. Model training was conducted in a PyTorch environment using Hugging Face’s Transformers library. These findings highlight the continued relevance of traditional models in specific NLP tasks and underscore the importance of model selection based on task complexity and data characteristics
Hak Cipta (c) 2025 Nurul Firdaus, Berliana Kusuma Riasti, Muhammad Asri Safi'ie http://creativecommons.org/licenses/by-nc/4.0
ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY
The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors
Hak Cipta (c) 2025 Aris Gunaryati, Teddy Mantoro, Septi Andryana, Benrahman, Mohammad Iwan Wahyuddin http://creativecommons.org/licenses/by-nc/4.0
WORD2VEC OPTIMALIZATION USING TRANSFER LEARNING IN INDONESIAN LANGUAGE FOR HIGHER EDUCATION
Natural language processing (NLP) in Indonesian faces challenges due to limited linguistic resources, particularly in developing optimal word embedding models. This study optimizes the Word2Vec model for Indonesian in higher education contexts by leveraging transfer learning and lexicon expansion. Using a dataset of 4,463 higher education related tweets consisting of positive and negative sentiment categories, the proposed NewWord2Vec model combined with a Support Vector Machine (SVM) classifier achieved a 4% improvement in word detection accuracy compared to the standard Word2Vec. This enhancement demonstrates better performance in capturing linguistic nuances and sentiment orientation in Indonesian text. However, the model’s applicability remains limited to higher education terminology, and potential biases from transfer learning must be addressed. Future research should expand the dataset to diverse domains and refine the transfer learning process to better capture contextual variations in Indonesian. These findings contribute to advancing NLP applications in Indonesian, particularly for automated assessment systems, recommendation tools, and academic decision-making processes
Hak Cipta (c) 2025 Sri Hadianti, Dwiza Riana, Herdian Tohir, Jarwadi Jarwadi, Tjaturningsih Rosdiana, Evi Sopandi, Dinar Ajeng Kristiyanti http://creativecommons.org/licenses/by-nc/4.0