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All Journal Compiler Journal of Scientific Research, Education, and Technology
Dewi, Ni Made Cahyani
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Hierarchical Clustering for Rice Planting Season Recommendations in Subak Tabanan Dewi, Ni Made Cahyani; Hidayat, Ahmad Tri
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.897

Abstract

This research applies hierarchical clustering to classify rice harvest periods in Subak Tabanan, Bali, using monthly harvest area data from 2020–2024. The objective is to identify seasonal patterns that can guide planting recommendations for local farmers. Data preprocessing involved standardization and transformation into numerical format suitable for clustering. The analysis focused on three clusters representing rainy season, transitional season, and dry season. The results indicate that most months fall within the rainy season cluster, while transitional months and a single dry month were distinctly identified. The silhouette score value shows moderate clustering performance, indicating that hierarchical clustering is capable of distinguishing planting seasons effectively. Visualization through dendrogram and distribution charts supported the identification of cluster groups. This study contributes to agricultural decision support systems, particularly in improving planting strategies and ensuring rice production sustainability in Subak Tabanan.
K-Means Clustering On Rice Harvest Data For Planting Season Recommendation In Subak Cepaka, Tabanan Dewi, Ni Made Cahyani; Hidayat, Ahmad Tri
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3504

Abstract

The Subak farming system in Tabanan Regency, Bali, is vital as a primary rice granary but faces challenges in determining optimal planting patterns. Planting decisions based only on inherited experience often do not match climate conditions, reducing productivity and increasing crop failure risks. This study implements the K-Means Clustering algorithm on five years of historical rice harvest data (2020–2024) to generate accurate planting season recommendations. Monthly data were analyzed and grouped into three categories: rainy, dry, and transitional seasons. The clustering results were integrated into a mobile application that provides farmers with accessible recommendations through an interactive interface and visualization. The effectiveness of the clustering model was evaluated using the Silhouette Score, which indicated good separation and cohesion among clusters, while efficiency was assessed through processing time and algorithm simplicity, confirming that K-Means performed the task with minimal computational cost. This system enables farmers to make data-driven planting decisions, optimize productivity, and support sustainable food security in Bali.
Co-Authors Hidayat, Ahmad Tri