Clustering Analysis of Subsidized Fertilizer Recipients in 2025 Using K-Means++ and Fuzzy C-Means

Umar Al Faruq; Rini Indriati; Aidina Ristyawan

doi:10.55506/icdess.v3i1.159

Authors

Umar Al Faruq Universitas Nusantara PGRI Kediri
Rini Indriati Universitas Nusantara PGRI Kediri
Aidina Ristyawan Universitas Nusantara PGRI Kediri

DOI:

https://doi.org/10.55506/icdess.v3i1.159

Keywords:

Clustering Analysis, Subsidized Fertilizer, K-Means , Fuzzy C-Means

Abstract

This study aims to analyze and compare the performance of clustering models in grouping subsidized fertilizer recipient data in 2025 to support the efficiency and accuracy of government distribution policy targets. The recipient data were processed through feature selection, data transformation, data type conversion, and missing value handling. The K-Means++ and Fuzzy C-Means (FCM) clustering methods are applied with the optimal number of clusters (K) set at eight (K=8) based on validity metric analysis. The model evaluation results show that the K-Means++ algorithm produces better cluster quality than FCM. The internal validity metric assessment for K-Means++ on the K=8 cluster shows a Silhouette Score of 0.756, a Davies-Bouldin Index (DBI) of 0.241, and a Calinski-Harabasz index (CHI) of 7901. Meanwhile, FCM reports S=0.737, DBI=0.424, and CHI=4903. This comparison clearly shows that K-Means++ has advantages in terms of clearer cluster separation and stability. The conclusion of this study is that the K-Means++ algorithm is the most effective model and is recommended for use in grouping recipients of subsidized fertilizer assistance in 2025. The results of this grouping (8 clusters) can present an accurate and useful profile for policymakers in designing more effective fertilizer allocation and distribution priority strategies.

Downloads

Download data is not yet available.

References

Aldino, A. A., Darwis, D., Prastowo, A. T., & Sujana, C. (2021). Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency. Journal of Physics: Conference Series, 1751(1). https://doi.org/10.1088/1742-6596/1751/1/012038 DOI: https://doi.org/10.1088/1742-6596/1751/1/012038

Daoudi, S., Zouaoui, C. M. A., El-Mezouar, M. C., & Taleb, N. (2021). Parallelization of the K-Means++ clustering algorithm. Ingenierie Des Systemes d’Information, 26(1), 59–66. https://doi.org/10.18280/isi.260106 DOI: https://doi.org/10.18280/isi.260106

Hermantria, I. (2021). Segmentation of Non-Subsidized Fertilizer Consumers in East Nusa Tenggara Province. BALANCE: Economic, Business, Management and Accounting Journal, 18(2), 8. https://doi.org/10.30651/blc.v18i2.7301 DOI: https://doi.org/10.30651/blc.v18i2.7301

Ika Ari Sasmita, Rini Indriati, & M. Najibulloh Muzaki. (2021). Recommendations for Recipients of the Keluarga Harapan Program. Jambura Journal of Electrical and Electronics Engineering, 3, 84–88. DOI: https://doi.org/10.37905/jjeee.v3i2.10943

Kodinariya, T. ., & Makwana, P. . (2021). Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6), 90–95.

Krasnov, D., Davis, D., Malott, K., Chen, Y., Shi, X., & Wong, A. (2023). Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection. Entropy, 25(7). https://doi.org/10.3390/e25071021 DOI: https://doi.org/10.3390/e25071021

Kumar, S., & Singh, M. (2022). Collaborative annealing power K-means++ clustering. Knowledge-Based Systems.

Meng, J., Yu, Z., Cai, Y., & Wang, X. (2023). K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics. Applied Sciences (Switzerland), 13(8). https://doi.org/10.3390/app13084736 DOI: https://doi.org/10.3390/app13084736

Prakash, L. N. C. K., Suryanarayana, G., Ansari, M. D., & Gunjan, V. K. (2022). Instantaneous approach for evaluating the initial centers in the agricultural databases using k-means clustering algorithm. Journal of Mobile Multimedia, 18(1), 43–60. https://doi.org/10.13052/jmm1550-4646.1813 DOI: https://doi.org/10.13052/jmm1550-4646.1813

Putu Gede Subhaktiyasa, Sang Ayu Ketut Candrawati, N. Putri Sumaryani, Ni Wayan Sunita, & Abd. Syakur. (2025). Application of Descriptive Statistics: Quantitative and Qualitative Perspectives. Emasains : Journal of Mathematics and Science Education, 14(1), 96–104. https://doi.org/10.59672/emasains.v14i1.4450 DOI: https://doi.org/10.59672/emasains.v14i1.4450

Seyed Emadedin Hashemi, Fatemeh Gholian-Jouybari, & Mostafa Hajiaghaei-Keshteli. (2023). A fuzzy C-means algorithm for optimizing data clustering. Expert Systems with Applications, 227(120377). DOI: https://doi.org/10.1016/j.eswa.2023.120377

Swain, K. P., Nayak, S. R., Ravi, V., Mishra, S., Alahmadi, T. J., Singh, P., & Diwakar, M. (2024). Empowering Crop Selection with Ensemble Learning and K-means Clustering: A Modern Agricultural Perspective. The Open Agriculture Journal, 18(1). https://doi.org/10.2174/0118743315291367240207093403 DOI: https://doi.org/10.2174/0118743315291367240207093403

Zalfa, D., Nugroho, A., & Harini, D. (2023). Clustering analysis in subsidized fertilizer recipients using the K-Means algorithm. In August (Vol. 7).

Zhang, Liu, Y., & Wang, J. (2023). Cluster Validation Techniques for Unsupervised Learning: A Comparative Study. Expert Systems with Applications.