Identification of Rice Production Clusters in Central Java Province with K-Means Technique

Yudi Wahyu Wibowo, Ihsan Cahyo U, Widi Widayat, Juanita Tria S A, As'ad Nirot A

Abstract


Central Java Province stands as a pivotal agricultural region in Indonesia, characterized by high productivity in paddy and rice cultivation. However, substantial production disparities persist across districts, attributed to varying geographical conditions, agricultural infrastructure, and farmers' cropping patterns. Consequently, the identification of production clusters is imperative for elucidating production patterns and formulating targeted policy interventions. This study aims to classify districts in Central Java based on production metrics using the K-Means Clustering technique implemented in the R statistical environment. Production data across various regions were analyzed to determine optimal clustering patterns. The clustering analysis stratified the study area into four distinct agricultural typologies: optimal performance zones (Cluster 3, n=2), land-based volume producers (Cluster 1, n=7), small-scale efficient producers (Cluster 4, n=15), and priority intervention areas (Cluster 2, n=11). These findings underscore the necessity for differentiated policy strategies addressing the disparities in efficiency and production scales.

Keywords


central java, k-means clustering, r, rice production

Full Text:

PDF

References


A. N. Ayu Mutia, I. Nurlinda, and N. Astriani, “Pengaturan Pembangunan Food Estate pada Kawasan Hutan untuk Mewujudkan Ketahanan Pangan di Indonesia,” Bina Hukum Lingkungan, Vol. 6, No. 2, pp. 224–240, Mar. 2022, DOI: 10.24970/bhl.v6i2.259.

I. Ahmadian, A. Yustiati, and D. Y. Andriani, “Produktivitas Budidaya Sistem Mina Padi untuk Meningkatkan Ketahanan Pangan di Indonesia: A Review.”

P. Pratama Siregar and Z. Almaida Siregar, “Resolusi : Rekayasa Teknik Informatika dan Informasi Penerapan Metode K-Means dalam Mengelompokkan Persebaran Lahan Kritis di Indonesia berdasarkan Provinsi,” Vol. 2, No. 4, pp. 145–151, 2022, [Online]. Available: https://djournals.com/resolusi

N. Salsabila and Hendy Tannady, “Manajemen Distribusi Komoditas Harga Bahan Pangan di Indonesia dengan Metode Least Cost,” Jan. 2021, DOI: 10.31219/osf.io/n37rg.

A. Adiyanto and Y. Arie Wijaya, “Penerapan Algoritma K-Means pada Pengelompokan Data Set Bahan Pangan Indonesia Tahun 2022-2023,” JATI (Jurnal Mahasiswa Teknik Informatika), Vol. 7, No. 2, pp. 1344–1350, Sep. 2023, DOI: 10.36040/jati.v7i2.6849.

A. Rasman, E. Sinta Theresia, and dan M. Fadel Aginda, “Analisis Implementasi Program Food Estate sebagai Solusi Ketahanan Pangan Indonesia,” Holistic: Journal of Tropical Agriculture Sciences Riset, Vol. 1, No. 1, pp. 36–68, 2023, DOI: 10.61511/hjtas.v.

S. Komunikasi Pemerintah dalam Mensosialisasikan Bantuan Pangan Non Tunai di Desa Simpangan Kecamatan Cikarang Utara Fihris Sa and E. Priyanti, “Strategi Komunikasi Pemerintah dalam Mensosialisasikan Bantuan Pangan Non Tunai (BPNT) di Desa Simpangan Kecamatan Cikarang Utara,” j-innovative.org, Accessed: Feb. 16, 2025. [Online]. Available: http://j-innovative.org/index.php/Innovative/article/view/11409

A. Larasati, … R. M.-J. T. I., and undefined 2021, “Utilizing Elbow Method for Text Clustering Optimization in Analyzing Social Media Marketing Content of Indonesian e-Commerce,” jurnalindustri.petra.ac.id, Accessed: Feb. 16, 2025. [Online]. Available: https://jurnalindustri.petra.ac.id/index.php/ind/article/view/23840

M. M. abdoel Wahid, “Determining the Location of RMU, using K-Means Clustering, Evaluate the Location of Existing RMU, using R-Programming,” Journal of Informatics and Telecommunication Engineering, Vol. 6, No. 1, pp. 10–17, Jul. 2022, DOI: 10.31289/jite.v6i1.6126.

T. Tuslaela, R. Rusdiansyah, H. Supendar, and N. Suharyanti, “Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022,” Sinkron, Vol. 9, No. 1, pp. 54–60, Jan. 2024, DOI: 10.33395/SINKRON.V9I1.13169.

D. Steinley, “K-Means Clustering: A Half-Century Synthesis,” British Journal of Mathematical and Statistical Psychology, Vol. 59, No. 1, pp. 1–34, May 2006, DOI: 10.1348/000711005X48266.

E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, “International Journal of Advances in Scientific Research and Engineering (IJASRE) Comprehensive Review of K-Means Clustering Algorithms,” 2021, DOI: 10.31695/IJASRE.2021.34050.

A. Ahmad and L. Dey, “A K-Mean Clustering Algorithm for Mixed Numeric and Categorical Data,” Data Knowl Eng, Vol. 63, No. 2, pp. 503–527, Nov. 2007, DOI: 10.1016/J.DATAK.2007.03.016.

“The Ultimate Guide for Clustering Mixed Data | by Eoghan Keany | Analytics Vidhya | Medium.” Accessed: Nov. 05, 2025. [Online]. Available: https://medium.com/analytics-vidhya/the-ultimate-guide-for-clustering-mixed-data-1eefa0b4743b

F. Zhu, C. Zhu, Z. Fang, W. Lu, and J. Pan, “Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples,” Agronomy 2025, Vol. 15, Page 1220, Vol. 15, No. 5, p. 1220, May 2025, DOI: 10.3390/AGRONOMY15051220.

I. Mujahidin and S. H. Hasanah, “Comparative Analysis of K-Means, K-Medoids, and Fuzzy C-Means for Clustering Provinces in Indonesia based on Rice Production in 2024,” Jurnal Gaussian, Vol. 14, No. 2, pp. 356–365, Oct. 2025, DOI: 10.14710/J.GAUSS.14.2.356-365.

G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom.” [Online]. Available: http://outsights.com/systems/dikw/dikw.htm

A. Fahim, “K and Starting Means for K-Means Algorithm,” J Comput Sci, Vol. 55, p. 101445, Oct. 2021, DOI: 10.1016/J.JOCS.2021.101445.

P. M. Hasugian, B. Sinaga, J. Manurung, and S. A. Al Hashim, “Best Cluster Optimization with Combination of K-Means Algorithm and Elbow Method Towards Rice Production Status Determination,” International Journal of Artificial Intelligence Research, Vol. 5, No. 1, Jun. 2021, DOI: 10.29099/ijair.v6i1.232.

“Luas Panen, Produktivitas, dan Produksi Padi Menurut Kabupaten/Kota di Provinsi Jawa Tengah, 2024 - Tabel Statistik - Badan Pusat Statistik Provinsi Jawa Tengah.” Accessed: May 19, 2025. [Online]. Available: https://jateng.bps.go.id/id/statistics-table/3/WmpaNk1YbGFjR0pOUjBKYWFIQlBSU3MwVHpOVWR6MDkjMyMzMzAw/luas-panen--produktivitas--dan-produksi-padi-menurut-kabupaten-kota-di-provinsi-jawa-tengah.html?year=2024

G. W. Milligan and M. C. Cooper, “A Study of Standardization of Variables in Cluster Analysis,” J Classif, Vol. 5, No. 2, pp. 181–204, Sep. 1988, DOI: 10.1007/BF01897163/METRICS.

“Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning - Alboukadel Kassambara - Google Books.” Accessed: May 19, 2025. [Online]. Available: https://books.google.co.id/books?hl=en&lr=&id=plEyDwAAQBAJ&oi=fnd&pg=PP2&dq=step+cluster+on+R&ots=xeI_lDjR0z&sig=6Fhz-3j15UGpZ1y92EgnLLqvynA&redir_esc=y#v=onepage&q=step%20cluster%20on%20R&f=false

“Cluster Analysis of Charitable Organizations of Ukraine using K-Means Technology,” Revista »Administratie si Management Public« (RAMP), No. 37, pp. 117–131, 2021.

R. Scitovski, K. Sabo, F. Martínez-Álvarez, and Š. Ungar, “Cluster Analysis and Applications,” Cluster Analysis and Applications, pp. 1–271, Jan. 2021, DOI: 10.1007/978-3-030-74552-3/COVER.

D. Gurukumaresan, C. Duraisamy, R. Srinivasan, and V. Vijayan, “Optimal Solution of Fuzzy Assignment Problem with Centroid Methods,” Mater Today Proc, Vol. 37, No. Part 2, pp. 553–555, Jan. 2021, DOI: 10.1016/J.MATPR.2020.05.582.

A. Ahdika, M. D. Kartikasari, S. K. Dini, and I. Ramadhani, “Diversification of Agricultural Areas in Indonesia using Dynamic Copula Modeling and K-Means Clustering,” Sains Malays, Vol. 50, No. 9, pp. 2791–2817, Sep. 2021, DOI: 10.17576/JSM-2021-5009-24.




DOI: https://doi.org/10.32520/stmsi.v15i1.5327

Article Metrics

Abstract view : 11 times
PDF - 3 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.