Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

Trisna Yuniarti, Dahliyah Hayati

Abstract

The oil palm is the most productive plantation product in Indonesia. Government strategies and policies related to oil palm plantations continue to be carried out considering that the plantation area is increasing every year. Segmentation of oil palm plantations based on area, production, and productivity aims to identify groups of potential oil palm plantations in the territory of Indonesia. This segmentation can provide consideration in formulating strategies and policies that will be made by the government. The segmentation method for grouping oil palm plantations uses the K-Means Clustering Data Mining technique with 3 clusters specified. Data mining stages start from data collection until representation is carried out, where 34 data sets are collected, only 25 data sets can be processed further. The results of this grouping obtained three plantation segments, namely 72% of the plantation group with low potential, 20% of the plantation group with medium potential, and 8% of the plantation group with high potential.

Keywords

Centroid, Clustering, Data Mining, K-Means, Oil Palm

Full Text:

PDF

References

F. Verneau, F. La Barbera, M. Amato, and V. Sodano, “Consumers’ concern towards palm oil consumption an empirical study on attitudes and intention in italy,” Br. Food J., vol. 121, no. 9, pp. 1982–1997, 2019.

S. Hutabarat, “Optimalisasi Pemanfaatan Lahan Perkebunan Kelapa Sawit di Riau,” Unri Conf. Ser. Agric. Food Secur., vol. 1, pp. 46–57, 2019.

Direktorat Jenderal Perkebunan, “Statistik Perkebunan Unggulan Nasional 2019-2021,” Angew. Chemie Int. Ed. 6(11), 951–952., 2021.

R. Utami, E. I. Kumala Putri, and M. Ekayani, “Economy and Environmental Impact of Oil Palm Palm Plantation Expansion (Case Study: Panyabungan Village, Merlung Sub-District, West Tanjung Jabung Barat District, Jambi),” J. Ilmu Pertan. Indones., vol. 22, no. 2, pp. 115–126, 2017.

M. H. Siregar, “Data Mining Klasterisasi Penjualan Alat-Alat Bangunan Menggunakan Metode K-Means (Studi Kasus Di Toko Adi Bangunan),” J. Teknol. Dan Open Source, vol. 1, no. 2, pp. 83–91, 2018.

S. Handoko, F. Fauziah, and E. T. E. Handayani, “Implementasi Data Mining Untuk Menentukan Tingkat Penjualan Paket Data Telkomsel Menggunakan Metode K-Means Clustering,” J. Ilm. Teknol. dan Rekayasa, vol. 25, no. 1, pp. 76–88, 2020.

S. P. Tamba, F. T. Kesuma, and Feryanto, “Penerapan Data Mining Untuk Menentukan Penjualan Sparepart Toyota Dengan Metode K-Means Clustering,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 2, no. 2, pp. 67–72, 2020.

F. Indriyani and E. Irfiani, “Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means,” JUITA J. Inform., vol. 7, no. 2, p. 109, 2019.

C. Astria, A. P. Windarto, A. Wanto, and E. Irawan, “Metode K-Means pada Pengelompokan Wilayah Pendistribusian Listrik,” Semin. Nas. Sains Teknol. Inf., pp. 306–312, 2019.

U. B. Mulia, “Jumlah Ritel,” vol. XI, no. 1, pp. 32–44.

M. Jesper, F. Pag, K. Vajen, and U. Jordan, “Annual Industrial and Commercial Heat Load Profiles: Modeling Based on k-Means Clustering and Regression Analysis,” Energy Convers. Manag. X, vol. 10, no. March, p. 100085, 2021.

I. M. Pulungan, M. Fauzan, and A. P. Windarto, “Implementasi Algoritma K-Means Clustering dalam Menentukan Blok Tanaman Sawit Paling Produktif,” in Prosiding Seminar Nasional Riset Information Science (SENARIS), 2019, no. September, pp. 338–348.

R. A. D. Saragih, M. Safii, and H. S. Tambunan, “Penerapan Metode K-Means Clustering Untuk Mengelompokkan Kelapa Sawit Produktif,” in Prosiding SiManTap: Seminar Nasional Matematika dan Terapan, 2019, pp. 362–370.

D. F. Pasaribu, I. S. Damanik, E. Irawan, H. S. Tambunan, and K. Kunci, “Memanfaatkan Algoritma K-Means Dalam Memetakan Potensi Hasil Produksi Kelapa Sawit PTPN IV Marihat,” BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 11–20, 2021.

P. Deepak and A. Jurek-loughrey, “Multi-View Clustering,” pp. 27–53.

Y. Liu, X. Du, and S. Ma, Innovative study on clustering center and distance measurement of K-means algorithm: mapreduce efficient parallel algorithm based on user data of JD mall, no. 0123456789. Springer US, 2021.

T. Yuniarti, I. Surjandari, E. Muslim, and E. Laoh, “Data Mining Approach for Short Term Load Forecasting by Combining Wavelet Transform and Group Method of Data Handling ( WGMDH ),” in In 2017 3rd International Conference on Science in Information Technology (ICSITech), 2017, pp. 53–58.

B. Haihong, Z. Yiqun, S. Jianshuo, and Sh. Fangchu, “Study on power consumption load forecast based on K-means.pdf,” in 2020 7th International Conference on Power and Energy Systems Engineering (CPESE 2020), 26–29 September 2020, Fukuoka, Japan, 2020, pp. 693–700.

DOI: http://dx.doi.org/10.52759/inventory.v2i2.47

Refbacks

  • There are currently no refbacks.