DOI: https://doi.org/10.14710/jmasif.16.2.76056

Spatiotemporal Analysis of Rice Production Patterns in West Java Using Unsupervised Learning Techniques

ICASEPS, Indonesia

Received: 16 Jul 2025; Revised: 2 Oct 2025; Accepted: 10 Oct 2025; Published: 10 Oct 2025.
Open Access Copyright (c) 2025 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The classification of 23 regencies/cities in West Java (2008–2024) was executed using the K-Means algorithm on a dataset spanning five variables: production, harvested area, productivity, population, and agricultural workforce. K-Means was chosen for its efficiency and ease of interpretability when analyzing large-scale multivariate data across time. Optimal cluster determination involved evaluating the Elbow Method, Silhouette Score, and the Davies-Bouldin Index (DBI). Although K=5 was suggested by the Elbow Method, K=6 was selected because it demonstrated a more stable and representative regional separation, supported by the lowest DBI (0.8221) and a relatively high Silhouette Score (0.4531). Cluster boundaries were further validated through PCA and GIS visualization. The analysis revealed precise regional segmentation. Key findings indicate that Indramayu, Karawang, and Subang regencies are stable, high-production centers, suitable for intensification and modernization. Conversely, regions like Bandung and Garut regencies exhibited dynamic cluster shifts driven by urbanization and climate variability. This segmentation has crucial policy implications: stable areas are suitable for intensification, dynamic areas require adaptive risk-mitigation policies, and urban-influenced regions (Bandung, Bekasi, and Depok cities) must focus on diversification and agricultural innovation. Despite the limitations of K-Means’ inability to capture complex, non-linear clusters, this research highlights the value of integrating spatiotemporal clustering for policy insights. Future research should incorporate climate and land-use data with advanced clustering methods, such as DBSCAN and HDBSCAN. HDBSCAN is more suitable for modeling clusters with varying densities, and time-series approaches should also be integrated. Overall, these results provide an essential, evidence-based framework for targeted agricultural planning.
Fulltext
Keywords: clustering;K-Means;rice production;spatiotemporal;West Java

Article Metrics:

Article Info
Section: Research Article
Language : EN
Recent articles
Artificial Intelligence-Aided In Silico Screening of Syzygium polyanthum Phytochemicals for Antidiabetic Drug Discovery Using ACO (Ant Colony Optimization) Algorithm Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position More recent articles
Most cited articles
Enterprise Architecture Model untuk Aplikasi Government ANALISA PERFORMA METODE COSINE DAN JACARD PADA PENGUJIAN KESAMAAN DOKUMEN ALGORITMA BACK PROPAGATION NEURAL NETWORK UNTUK PENGENALAN POLA KARAKTER HURUF JAWA APLIKASI SISTEM INFORMASI GEOGRAFIS BERBASIS WEB PENYEBARAN DANA BANTUAN OPERASIONAL SEKOLAH Pengenalan Jenis Golongan Darah Menggunakan Jaringan Syaraf Tiruan Perceptron More cited articles
  1. A. Musyafak, A. Susanti, A. Supriyatna, T. H. A. Takariyana, V. S. Bonavia, and Suyati, Outlook Komoditas Pertanian Tanaman Pangan: Padi. Jakarta: Pusat Data dan Sistem Informasi Pertanian, Sekretariat Jenderal Kementerian Pertanian, 2020
  2. E. T. Tosida, F. D. Wihartiko, I. Hermadi, Y. Nurhadryani, and Feriadi, "The Big Data Commodity Management Model for Rice for National Food Policy," J. RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 142–154, Feb. 2020, doi: 10.29207/resti.v4i1.1520
  3. Antara News, "Pemkab Indramayu catat provitas padi capai 7,3 ton per hektare," Antaranews.com, 5 Jul. 2024. [Online]. Available: https://www.antaranews.com/berita/4089546/pemkab-indramayu-catat-provitas-padi-capai-73-ton-per-hektare
  4. Badan Pusat Statistik Provinsi Jawa Barat, "Luas Panen, Produktivitas, dan Produksi Padi Menurut Kabupaten/Kota di Provinsi Jawa Barat," BPS Jawa Barat, 2024. [Online]. Available: https://jabar.bps.go.id/id/statistics-table/..
  5. M. Murdiana and F. Fadli, "Peran irigasi dalam peningkatan produksi padi sawah di Kecamatan Meurah Mulia Kabupaten Aceh Utara," Agrifo: Jurnal Agribisnis Universitas Malikussaleh, vol. 1, no. 2, 2016, doi: 10.29103/ag.v1i2.760
  6. Septi, "Faktor-faktor yang memengaruhi produksi padi di Kabupaten Tapanuli Selatan," PROFJES: Profetik Jurnal Ekonomi Syariah, vol. 2, no. 2, Jul.–Des. 2023. [Online]. Available: http://jurnal.iain-padangsidimpuan.ac.id/index.php/Profetik
  7. M. R. I. Amar, Faktor-faktor yang mempengaruhi hasil produksi padi di Kabupaten Pinrang, Skripsi, Univ. Islam Negeri Alauddin Makassar, 2023
  8. R. P. Hidayat, E. T. Tosida, A. Maesya, and I. P. Solihin, "Support vector regression algorithm for predicting rice production in West Java Province," Proc. Int. Conf. on Informatics, Multimedia, Cyber and Information System (ICIMCIS), IEEE, 2024, doi: 10.1109/ICIMCIS57984.2024.10390639
  9. J. Hutahaean, D. Yusup, and Purwantoro, "Perbandingan metode Linear Regression, Random Forest & K-Nearest Neighbor untuk prediksi produksi hasil panen padi di Provinsi Jawa Barat," JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, Jun. 2024
  10. S. K. Dermoredjo et al., "National food development policies in Indonesia: An analysis of food sustainability and security," BIO Web of Conferences, vol. 119, 05006, 2024, doi: 10.1051/bioconf/202411905006
  11. P. Kurniawati, H. Pratiwi, and Sugiyanto, "Indonesian territory clustering based on harvested area and rice productivity using clustering algorithm," J. Social Sci., vol. 4, no. 1, 2023, doi: 10.46799/jss.v4i1.510
  12. Abirami et al., "Application of K-means Clustering Algorithm in Rice Production of Tamil Nadu, India," Int. J. Environ. Climate Change, vol. 12, no. 11, pp. 1348–1355, 2022, doi: 10.9734/IJECC/2022/v12i1131113
  13. L. Wickramasinghe et al., "Modeling the relationship between rice yield and climate variables using statistical and machine learning techniques," J. Mathematics, vol. 2021, Art. ID 6646126, 2021, doi: 10.1155/2021/6646126
  14. R. Savira, "Optimizing clustering models using principle component analysis for car customers," IJCCS (Indonesian J. Comput. Cybern. Syst.), vol. 18, no. 2, Apr. 2024, doi: 10.22146/ijccs.94744
  15. N. Omar et al., "Integration of principal component analysis and K-means clustering for type 2 diabetes sub-clustering model," AIP Conf. Proc., vol. 3056, no. 1, p. 070001, Apr. 2025, doi: 10.1063/5.0209965
  16. E. Umargono, J. E. Suseno, and S. K. V. Gunawan, "K-Means Clustering Optimization Using the Elbow Method and Early Centroid Determination Based on Mean and Median Formula," Proc. 2nd Int. Seminar on Science and Technology (ISSTEC), Oct. 2020, doi: 10.2991/assehr.k.201010.019
  17. F. Tempola and A. F. Assagaf, "Clustering of Potency of Shrimp in Indonesia With K-Means Algorithm and Validation of Davies-Bouldin Index," Proc. Int. Conf. on Science and Technology (ICST), Dec. 2018, doi: 10.2991/icst-18.2018.148
  18. M. Joseph et al., "Modelling climate variabilities and global rice production: A panel regression and time series analysis," Heliyon, vol. 9, no. 11, e15480, 2023, doi: 10.1016/j.heliyon.2023.e15480
  19. S. M. Algarni et al., "Analyzing the impact of climate change on rice production and strategies for enhancing efficiency, sustainability, and global food security," Int. J. Innov. Res. Sci. Stud., vol. 8, no. 2, pp. 2946–2957, 2025, doi: 10.53894/ijirss.v8i2.5888
  20. K. Suresh et al., "How productive are rice farmers in Sri Lanka? The impact of resource accessibility, seed sources and varietal diversification," Food Security, vol. 15, pp. 1005–1024, 2023, doi: 10.1007/s12571-023-01338-2
  21. H. Zhu et al., "Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery," ISPRS J. Photogramm. Remote Sens., 2023, doi: 10.1016/j.isprsjprs.2023.11.016
  22. Supriatna et al., "Spatio-temporal analysis of rice field phenology using Sentinel-1 image in Karawang Regency West Java, Indonesia," Int. J. GEOMATE, vol. 17, no. 62, pp. 101–106, Oct. 2019, doi: 10.21660/2019.62.8782
  23. R. S. V. Teegavarapu, "Temporal Interpolation Methods," in Imputation Methods for Missing Hydrometeorological Data Estimation, Water Sci. Technol. Libr., vol. 108, Springer, Cham, 2024, doi: 10.1007/978-3-031-60946-6_3
  24. Wongoutong, "The impact of neglecting feature scaling in k‑means clustering," PLOS ONE, vol. 19, no. 12, e0310839, Dec. 2024, doi: 10.1371/journal.pone.0310839
  25. M. Gaido, "Distributed Silhouette: Efficient Computation of the Silhouette Score on Large Datasets," arXiv preprint, arXiv:2303.14102, 2023
  26. B. Sadeghi, "Clustering in geo-data science: Navigating uncertainty to select the most reliable method," Ore Geol. Rev., vol. 181, 106591, 2025, doi: 10.1016/j.oregeorev.2025.106591
  27. J. A. Hartigan and M. A. Wong, "A K-Means clustering algorithm," J. Royal Stat. Soc. Series C (Appl. Stat.), vol. 28, no. 1, pp. 100–108, Mar. 1979, doi: 10.2307/2346830

Last update:

No citation recorded.

Last update: 2025-10-10 12:12:34

No citation recorded.