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All Journal Justek : Jurnal Sains Dan Teknologi Journal of Applied Data Sciences
Cahyono Budy Santoso
Pembangunan Jaya University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics

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Clustering of Data on Vegetable Crop Production in the City of Bandung Using the K-Means Algorithm Ifano Rangga Saputra; Cahyono Budy Santoso
Justek : Jurnal Sains dan Teknologi Vol 8, No 4 (2025): December
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v8i4.35555

Abstract

Vegetable farming supports urban food security, and Bandung City is one of West Java's main horticultural centers. However, vegetable production remains unevenly distributed across its sub-districts. This study analyzes production patterns from 2018–2023 using the K-Means Clustering algorithm. The dataset includes 12 major commodities, and the analysis involves data preprocessing, determining the optimal number of clusters using the Elbow Method and Silhouette Score, applying K-Means, and visualizing results through heatmaps and PCA. The findings reveal three clusters: Cluster 0 dominated by potatoes and the "others" category; Cluster 1 dominated by kale; and Cluster 2 dominated by shallots and petsai. These patterns indicate concentrated and specialized production across specific sub-districts. The study concludes that K-Means effectively identifies multi-commodity production similarities and provides strategic insight for Business Intelligence applications in agricultural planning and policy development.
Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Cahyono Budy Santoso; Marcello Singadji; Denny Ganjar Purnama; Saimam Abdel; Aqila Kharismawardani
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
Co-Authors Aqila Kharismawardani Denny Ganjar Purnama Ifano Rangga Saputra Marcello Singadji Saimam Abdel