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All Journal Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Informatika dan Rekayasa Perangkat Lunak
I Gusti Nyoman Agung Bisma Tatwa
Institut Pertanian Bogor
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Clustering of Provincial Health Vulnerability Levels in Indonesia Using the K-Means Method Okma Arnilia; Sahrial Ihsani Ishak; Tri Widodo; I Gusti Nyoman Agung Bisma Tatwa
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1469

Abstract

This study aims to classify the health vulnerability levels of 38 provinces in Indonesia based on health and socio-economic indicators in 2024, including the number of hospitals, access to adequate sanitation, access to safe drinking water, stunting prevalence, number of health facilities, population size, and the percentage of poor population. The analysis began with data normalization using the z-score method to standardize variable scales and prevent dominance by indicators with larger value ranges. Following normalization, the optimal number of clusters was determined using the Elbow method by examining the decrease in inertia across different k-values. Based on the inertia pattern and cluster stability, the optimal number of clusters was identified as K=4, which adequately represents the variation in health vulnerability. The clustering results were subsequently visualized in a spatial map using Indonesia’s provincial administrative boundaries. The visualization revealed clear geographical variation across regions, with Cluster 1 representing provinces with very good health conditions, Cluster 2 good conditions, Cluster 3 moderate conditions, and Cluster 4 provinces requiring special attention regarding health indicators. These findings provide a comprehensive overview of health vulnerability distribution in Indonesia and are expected to inform policymakers and stakeholders in prioritizing region-based health interventions, strengthening health development strategies, and promoting more equitable national health services.
Application of MobileNetV2 for Coffee Bean Ripeness Classification to Support Smart Agriculture Angga Bayu Santoso; Okma Arnilia; Sahrial Ihsani Ishak; I Gusti Nyoman Agung Bisma Tatwa; Riki Rikardo
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 3 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n3.3155

Abstract

This study addresses the problem of manual classification of coffee bean ripeness, which is often inconsistent and inefficient, potentially affecting product quality and market value. To overcome this issue, the study proposes an automated classification approach using a deep learning model based on MobileNetV2. The objective of this study is to develop an accurate and efficient model for classifying coffee beans into four ripeness levels: Dark, Green, Light, and Medium, while ensuring its applicability in real-world conditions with limited resources. The dataset consists of 1,200 images collected from coffee plantations in Indonesia, processed through resizing, normalization, and data augmentation to improve generalization. The model was trained using transfer learning and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieves an accuracy of 98.33% on the testing dataset, with a precision of 0.985, recall of 0.983, and F1-score of 0.983. When implemented in a web-based application, it achieves 97.8% accuracy with an average response time of 1.8 seconds and is capable of handling up to 100 concurrent users with stable performance. These findings indicate that the proposed model is effective and reliable for supporting automated coffee bean classification and enhancing efficiency in post-harvest processing.
Co-Authors Angga Bayu Santoso Arnilia, Okma Okma Arnilia Riki Rikardo Sahrial Ihsani Ishak Tri Widodo