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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 422 Documents
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Comparative Evaluation of Ensemble Machine Learning Models for Child Stunting Prediction Using Routine Anthropometric Data in Indonesia Mikha Dayan Sinaga; Ratna Sri Hayati; Novriza Rahayu
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29709

Abstract

Child stunting remains a major public health challenge in Indonesia and continues to hinder progress toward the Sustainable Development Goals (SDGs), particularly in child health and nutrition. Early identification of at-risk children is therefore essential to support timely interventions. While previous machine learning studies on stunting prediction commonly incorporate socioeconomic, environmental, and behavioral variables, comparative evaluations based exclusively on routinely collected anthropometric indicators remain limited, particularly within Indonesian primary healthcare settings. This study evaluates the predictive performance of multiple machine learning models for stunting classification using only anthropometric and early-life growth indicators. A dataset consisting of 1,000 child records—including age, birth weight, birth length, current weight, current length, and breastfeeding status—was analyzed using Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting algorithms. The dataset was partitioned using an 80:20 stratified train–test split, while five-fold cross-validation was applied during model development to improve robustness and reproducibility. Experimental results demonstrate that ensemble-based methods outperform single classifiers, with Gradient Boosting achieving the highest predictive performance (accuracy = 0.90, F1-score = 0.90, AUC = 0.93). Feature importance analysis reveals that birth length, birth weight, current weight, and age are among the most influential predictors of stunting risk. These findings suggest that machine learning models built solely on routinely collected anthropometric indicators can provide a practical, scalable, and data-driven approach for early stunting detection in Indonesian primary healthcare systems.
Improving Neutral Sentiment Classification in Indonesian E-Wallet Reviews Using Word2Vec and Easy Data Augmentation (EDA) Muhammad Fattah Edric Camilo; Fatma Indriani; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29770

Abstract

The rapid expansion of digital payments has produced massive volumes of user-generated reviews, making manual analysis impractical. This study focuses on the challenge of neutral sentiment classification in Indonesian e-wallet reviews, where neutral comments often contain ambiguous language and are underrepresented relative to positive and negative classes. A total of 26,537 preprocessed DANA application reviews were used to evaluate whether Word2Vec embeddings and Easy Data Augmentation (EDA) can improve neutral sentiment detection when combined with Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures. Experiments comparing eight model configurations showed that the combination of Word2Vec, EDA, and LSTM achieved the best performance, with 0.861 accuracy, 0.841 macro-F1, and 0.749 F1-score for the neutral class. These findings demonstrate that semantic representations and controlled lexical variation can jointly enhance minority-class recognition in short informal Indonesian text and highlight the importance of aligning embedding strategies with sequence architectures.
An Indonesian Mental Health Chatbot Model Based on A Sequence to Sequence LSTM Nia Ekawati; Nia Ekawati; Imam Riadi; Herman Yuliansyah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30040

Abstract

This research proposes an Indonesian-language mental health chatbot model based on the LSTM Sequence-to-Sequence (Seq2Seq) architecture as an adaptive initial support solution. Unlike static classification models, this generative approach aims to capture emotional dependencies and conversational context through context vectors. The research methodology utilizes the public PSYCHIKA dataset, which includes 5,667 conversation pairs a significant volume for a low-resource language. Evaluation was conducted by comparing 80:20 and 70:30 data split schemes. Experimental results showed the best performance with the 80:20 split, achieving a BLEU-1 score of 0.137, compared to the 70:30 split, which only reached 0.043. The model achieved stable convergence at 15–16 epochs via an early-stopping mechanism without any signs of overfitting. Although training stability was maintained, the low BLEU score confirms that the use of a pure Seq2Seq LSTM without an attention mechanism is not yet sufficient to generate highly fluent responses. These findings provide a reproducible technical baseline for the development of mental health dialogue systems in Indonesia, while also emphasizing the urgency of more advanced architectures to improve the quality of empathy in the future.
Multi-Class Mental Health Classification Based on DASS-21 and Perceived Social Support Using Machine Learning Algorithms Winny Dwita Sumbayak; Didi Supriyadi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30073

Abstract

Mental health issues among students require data-driven approaches for early identification. This study aims to classify students’ mental health levels using the Depression Anxiety Stress Scale (DASS-21) and perceived social support, measured by the Multidimensional Scale of Perceived Social Support (MSPSS), via machine learning algorithms. A supervised classification approach was employed using Random Forest, Support Vector Machine, and Logistic Regression on data collected from 450 respondents. The data were processed through scoring, labeling, encoding, balancing, and stratified 80:20 splitting. Model evaluation was conducted using hold-out testing and 5-fold cross-validation to ensure robust and reliable performance estimation. The results indicate that Random Forest achieved the best performance, with an accuracy of 0.97 on the test set, outperforming Support Vector Machine (0.81) and Logistic Regression (0.83). Improvements in recall and F1-score for minority classes demonstrate the effectiveness of the balancing process. These findings highlight the potential of machine learning for student mental health classification, although further validation on larger and more diverse datasets is required.
Optimizing Small-Data Learning in Elementary Education: An Explainable Restricted Random Forest Approach for Early Warning Supriyanto Supriyanto; Ragil Dian Purnama Putri
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30156

Abstract

The delayed identification of students at risk of academic underperformance frequently undermines the effectiveness of pedagogical interventions. This limitation arises because most traditional models for predicting student performance depend on exhaustive end-of-semester datasets, engendering a latency issue wherein insights emerge too late for timely remediation. To overcome this, we propose an Explainable Early Warning System that forecasts students' Final Year Mathematics Assessment scores using exclusively mid-semester data: Daily Assessments and Mid-Semester Assessments. By utilizing an augmented dataset of 68 students, hybrid data augmentation to fix class imbalance, and a Restricted Random Forest model to prevent overfitting, our method achieves a strong 92.3% classification accuracy on unseen test data. Remarkably, it achieves 100% Recall for the 'Need Guidance' class, ensuring no at-risk students are overlooked. Furthermore, SHAP analysis reveals that, beyond midterm scores, consistency in specific daily tasks, particularly Daily Assessment Chapter 4 significantly impacts failure risk. In conclusion, combining data augmentation with explainable machine learning transforms predictions into actionable pedagogical insights, empowering teachers to execute precise interventions three months prior to the final exam.
ANFIS Kuantisasi Ringan yang Terkoordinasikan di Tepi untuk Prediksi Iklim Mikro Adaptif dalam Sistem IoT yang Dibatasi Energi Eddy Nurraharjo; Ema Utami; Kusrini Kusrini; Kumara Ari Yuana
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30160

Abstract

This study addresses the challenge of deploying collaborative adaptive intelligence on energy-constrained IoT edge nodes, a critical requirement for real-time microclimate prediction in smart agriculture. We propose a novel Edge-Coordinated Lightweight Quantized Adaptive Neuro-Fuzzy Inference System (LQ-ANFIS). The framework combines a quantized fuzzy neural model for local inference on 8-bit microcontrollers (WeMOS D1 Mini) with a lightweight MQTT-based coordination mechanism. This mechanism enables distributed nodes to achieve synchronized adaptation by periodically exchanging only scalar parameters, namely bias and learning rate, through a broker (Raspberry Pi), thereby eliminating the need for cloud infrastructure or heavy model transfers. During a seven-day experimental deployment involving three nodes, each collecting more than 10,000 temperature and humidity samples, the system demonstrated robust prediction accuracy (RMSE ≈ 1.89%, MAE ≈ 1.13%) and high energy efficiency (average power < 100 mW per node). Compared with a conventional uncoordinated ANFIS baseline, the proposed method achieved a 21% improvement in prediction accuracy and a 39% reduction in energy consumption. The results confirmed rapid inter-node bias convergence (σ < 0.05) and low coordination latency (< 50 ms), validating its real-time adaptability at the edge. These findings support scalable, cooperative intelligence for resource-constrained agricultural IoT deployments worldwide.
Geographically Weighted Machine Learning Model for Untangling Spatial Heterogeneity of Dengue Incidence in West Java Auralia Putri Astutiningsih; Gangga Anuraga; Hani Brilianti Rochmanto; Muhammad Athoillah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30181

Abstract

Dengue hemorrhagic fever continues to pose a significant public health challenge, particularly in West Java Province, Indonesia, which consistently reports the highest incidence rates in the country. This study examined the factors influencing dengue fever incidence using Random Forest Regression (RFR) and Geographically Weighted Random Forest (GWRF) methodologies. Utilizing secondary data from 2022 to 2024 across 27 districts/cities, the data from 2022 to 2023 served as training data, while the 2024 data were used for testing. The findings revealed that the optimal RFR model, with ntree = 1000 and mtry = 1, achieved an RMSE of 1796.409, a MAPE of 0.482, and an  of 0.685. Conversely, the GWRF model, which employed an adaptive kernel and an optimal bandwidth of 45 nearest neighbors, exhibited superior performance, with an RMSE of 1,756.713, MAPE of 0.466, and  of 0.700. This enhancement in the model performance suggests that spatial weighting improves the model's capacity to capture spatial heterogeneity. In addition, variations in local feature importance indicate spatial non-stationarity across regions. These results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.
Analisis Komparatif Algoritma Pembelajaran Mesin untuk Mengidentifikasi Keterlambatan Kelulusan Mahasiswa Benny Daniawan; Suwitno Suwitno; Andri Wijaya; Ardiane Rossi Kurniawan Maranto; Junaedi Junaedi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30186

Abstract

Delayed student graduation is a critical issue in higher education because it affects academic planning, student support, and institutional performance evaluation. This study develops a leakage controlled machine learning framework for early identification of students at risk of delayed graduation, using academic records available through the sixth semester. A dataset of 564 students was used, with graduation status defined as on-time for students graduating in the eighth semester or earlier and delayed for those graduating after the eighth semester. To prevent temporal data leakage, post-outcome variables were excluded from the predictor set. Five supervised learning algorithms were evaluated: Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. Preprocessing was performed using one-hot encoding and standardization within a pipeline, and model performance was assessed using stratified five-fold cross-validation. The tuned Random Forest achieved the most balanced performance, with 0.956 accuracy, 0.861 delayed-class precision, 0.805 delayed-class recall, 0.832 delayed-class F1-score, and 0.977 ROC-AUC. The tuned SVM with a threshold of 0.30 achieved higher delayed-class recall (0.857) and ROC-AUC (0.980). Feature-importance analysis indicated that fourth and fifth semester GPAs were the strongest predictors. These findings show that machine learning can support early academic intervention and data driven decision making in higher education.
Optimizing Diabetic Retinopathy Classification Using EfficientNet-B3 with Data Augmentation and Oversampling A A JE Veggy Priyangka; Tuga Mauritsius
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30410

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable blindness among diabetes patients. This study optimizes DR severity classification using EfficientNet-B3 with transfer learning combined with data handling strategies. Using the APTOS 2019 dataset containing 3,662 retinal fundus images across five severity classes, three experimental scenarios were evaluated: (1) baseline CNN, (2) CNN with data augmentation, and (3) CNN with data augmentation and random oversampling. Performance was measured using Quadratic Weighted Kappa (QWK), accuracy, precision, recall, F1-score, and ROC-AUC. Results demonstrate that Scenario III achieves the best performance with QWK of 0.8496 and accuracy of 77.00%, representing significant improvement over baseline (QWK: 0.4998) and augmentation-only models (QWK: 0.5728). The combination of data augmentation and random oversampling effectively addresses class imbalance in medical image datasets. This study provides empirical evidence on combining transfer learning with data balancing strategies for automated DR screening systems.
Edge AI-Based Multimodal Biometric Smart Reader Using YOLOv8 for Integrated Academic Attendance Systems Nurhadi Nurhadi; Emil Naf'an; Desyanti Desyanti; Mustazzihim Suhaidi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30527

Abstract

Attendance systems in vocational education institutions face challenges related to accuracy, security, and susceptibility to manipulation due to the use of single-modality authentication methods. RFID-based systems are vulnerable to card sharing, fingerprint systems suffer from latency during peak usage, and face recognition systems are sensitive to illumination and pose variations. This study proposes an Edge AI-based multimodal biometric smart reader integrating RFID, fingerprint, and YOLOv8-based face recognition for an academic attendance system at SMK Negeri 1 Dumai. The system is implemented on NVIDIA Jetson Nano as an edge computing device and integrated with an academic information system through an IoT-based architecture for real-time attendance monitoring. A decision-level fusion approach using majority voting is applied, where authentication is accepted if at least two of three modalities match. The system is evaluated using accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and response time. Experimental results show that the proposed multimodal system achieves an accuracy of 98.72%, outperforming RFID (89.34%), fingerprint (92.15%), and YOLOv8 face recognition (95.63%). The system also reduces FAR to 0.82% and FRR to 0.91%, with an average response time of 1.47 seconds, making it suitable for real-time deployment. Overall, the proposed Edge AI-based multimodal biometric system demonstrates high accuracy, improved security, and efficient real-time performance, providing a scalable solution for intelligent attendance systems in vocational education environments.

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