Implementation of The K-Means Clustering Algorithm for Grouping Oil Palm Land Productivity Levels (Case Study: PTPN IV Regional II, Tanjung Jati Plantation)

Kesya Alya

doi:10.59934/jaiea.v6i1.2735

Authors

Kesya Alya STMIK KAPUTAMA

DOI:

https://doi.org/10.59934/jaiea.v6i1.2735

Keywords:

K-means, Clustering, Plantation, Data mining

Abstract

PTPN IV Regional II Tanjung Jati Estate is a state-owned enterprise engaged in the agribusiness sector, particularly in oil palm plantation management, which is its primary commodity. This study aims to apply the K-Means Clustering algorithm to group oil palm plantation land based on plant age, land area, and production yield. The results show that the plantation land data were successfully grouped into three clusters with different characteristics. Cluster 0 has an average plant age of 10 years, a land area of 7.90 hectares, and a production yield of 174.67 tons per year. Cluster 1 has an average plant age of 22 years, a land area of 17.26 hectares, and a production yield of 347.76 tons per year. Cluster 2 has an average plant age of 9 years, a land area of 21.81 hectares, and a production yield of 481.97 tons per year. The best clustering result was obtained at K = 3 with a Davies-Bouldin Index (DBI) value of 0.241. The clustering results provide information regarding the characteristics of each land group, which can assist the company in understanding the condition of the managed plantation land and support the evaluation process of plantation land data.

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References

M. R. Rahman, R. Kusumawati, and F. Fatimah, “Deteksi Objek Menghitung Pohon Kelapa Sawit Menggunakan Metode Deep Learning Object Detection Tree Counting Palm Oilusing Deep Learning Metodh,” J. Pembang. Drh., vol. 2, no. 1, pp. 45–51, 2023.

M. Sitorus, H. V. N. Sinaga, and R. P. D. Kusumo, “Clusterisasi Perkebunan Kelapa Sawit Berdasarkan Luas Area Produksi Dengan Algoritma K-Means,” J. Informatics Adv. Comput., vol. 3, no. 1, pp. 23–29, 2022.

Hermawati, Data mining, I. Yogyakarta: Andi, 2013.

Han, Kamber, &, and Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann (Elsevier), 2012.

N. S. Astuti, Makmur, I. Karim, Nurlaela, M. A. Abdullah, and Dahniar, “Contribution of oil palm (Elaeis guineensis J.) plantations to farmers’ income in West Sulawesi,” Anjoro Int. J. Agric. Bus., vol. 1, no. 2, pp. 45–51, 2020, doi: 10.31605/anjoro.v1i2.892.

D. S. Rosiana, H. Agustin, H. A. F. Wijaya, D. S. Kurniawan, and B. S. Utomo, “Model Peningkatan Produktivitas Pertanian Berbasis Kelembagaan,” J. Maksipreneur Manajemen, Koperasi, dan Entrep., vol. 14, no. 2, pp. 501–516, 2025, doi: 10.30588/jmp.v14i2.1777.

R. Buaton, M. Zarlis, and V. Yasin, Konsep Data Mining Dalam Implementasi, 1st ed. Bogor: Penerbit Mitra Wacana Media, 2021.

Aprillia, Baskoro, Ambarwati, and & Wicaksana, Belajar Data Mining dengan Rapidminer, 1st ed. Jakarta, 2013.