Hierarchical Clustering for Rice Planting Season Recommendations in Subak Tabanan

Ni Made Cahyani Dewi; Ahmad Tri Hidayat

doi:10.58526/jsret.v4i4.897

Authors

Ni Made Cahyani Dewi Yogyakarta University of Technology, Indonesia
Ahmad Tri Hidayat Yogyakarta University of Technology, Indonesia

DOI:

https://doi.org/10.58526/jsret.v4i4.897

Keywords:

Hierarchical Clustering, Rice Harvest, Planting Season, Subak Tabanan

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.

Downloads

Download data is not yet available.

References

Guevara-Viejó, F., & Martínez, S. (2021). Application of K-Means clustering to assess agricultural production systems. Agronomy, 11(6), 1123. https://doi.org/10.3390/agronomy11061123

Ran, X. (2023). Comprehensive survey on hierarchical clustering. Artificial Intelligence Review, 56(1), 1–30. https://doi.org/10.1007/s10462-022-10366-3

Srinivasan, D. (2025). Energy efficient hierarchical clustering based dynamic data fusion for smart agriculture. Scientific Reports, 15(1), 1–13. https://doi.org/10.1038/s41598-024-85076-7

Jaeger, S. (2023). Cluster analysis: A modern statistical review. Wiley Interdisciplinary Reviews: Computational Statistics, 15(2), e1597. https://doi.org/10.1002/wics.1597

Li, H. Y. (2025). Fast dynamic time warping and hierarchical clustering for crop variety classification. Agronomy, 15(1), 82. https://doi.org/10.3390/agronomy15010082

Boyko, N. I. (2023). Hierarchical clustering algorithm for dendrogram construction and cluster counting. International Journal of Mathematical Models and Methods in Applied Sciences, 17(1), 1–10. https://doi.org/10.5281/zenodo.4567890

Paddo, A. R. (2023). Hierarchical cluster analysis: An in-depth exploration. Sprinkle Data Science Blog. Retrieved from https://www.sprinkledata.com/blogs/hierarchical-cluster-analysis-an-in-depth-exploration

Li, J. (2024). A hierarchical RF-XGBoost model for short-cycle crop yield prediction. Foods, 13(18), 2936. https://doi.org/10.3390/foods13182936

Fang, W. (2024). Assessment of agricultural development level based on hierarchical clustering and principal component analysis. Proceedings of the International Conference on Agricultural Engineering, 1, 1–10. https://doi.org/10.1145/3670085.3670086

Gray, A. (2023). Hierarchical clustering with dot products recovers hidden structures in data. NeurIPS 2023 Conference Proceedings. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2023/hash/6521937507d78f327cd402401be73bf2-Abstract-Conference.html

Singh, J. (2024). A comprehensive review of clustering techniques in data mining. Procedia Computer Science, 185, 1–10. https://doi.org/10.1016/j.procs.2024.03.001

Javadi, H., et al. (2022). Clustering and smoothing pipeline for management zone delineation. Sensors, 22(9), 1–18. https://doi.org/10.3390/s22020645

Rosiana, R., Prihartono, W., & Fathurrohman. (2025). Implementasi algoritma K-Means untuk pengelompokan kecamatan berdasarkan produktivitas tanaman padi di Kabupaten Cirebon. Jurnal Informatika Terapan, 9(1), 1–10. https://doi.org/10.54914/jit.v11i1.1555

Sanela, I., Nazir, et al. (2023). Penerapan metode clustering dengan K-Means untuk memetakan potensi tanaman padi di Sumatera. Journal of System and Informatics, 7(2), 88–95. https://doi.org/10.47065/josyc.v5i1.4523

Shawon, S. M., et al. (2024). Machine learning in agriculture: Crop yield prediction – A comprehensive review. Artificial Intelligence in Agriculture, 15, 74–95. https://doi.org/10.1016/j.atech.2024.100718

Farismana, R. (2024). Penerapan K-Means clustering untuk pemetaan produktivitas padi dan prediksi panen di Kabupaten Indramayu. Jurnal Ilmu Sosial dan Manajemen Review (JISAMAR), 5(2), 100–110. https://doi.org/10.52362/jisamar.v8i3.1572