Memanfaatkan Algoritma K-Means Dalam Memetakan Potensi Hasil Produksi Kelapa Sawit PTPN IV Marihat
Abstract
Based on data on the results of oil palm production in PTPN IV Marihat displays several locations with fruit yields that vary in number. For this reason, grouping of potential fruit-producing locations is needed to know which locations produce large or small numbers of palm fruit. The production sharing is usually done based on the location or block of harvesting oil palm fruit. Therefore, a method is needed to facilitate the grouping of fruit producing locations. With the K-Means clustering approach, the division of location groups can be done based on harvested area (Ha), production realization (kg) and harvest year. In this research, clustering of potential fruit-producing areas was carried out using the K-Means algorithm. By using K-Means aims to facilitate the grouping of a block with a lot of fruit production, and low. The result of this research is that C1 (highest) is 14 Harvest Block data, and C2 (lowest) is 11 Harvest Block data.
Downloads
References
Asroni and R. Adrian, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,” vol. 18, no. 1, pp. 76–82, 2016.
A. F. Tanjung, T. M. Diansyah, and Rismayanti, “Pemanfaatan Algortima K-Means Clustering Sebagai Pengamanan Pencurian Buah Kelapa Sawit Se-Distrik Tandun PT . Perkebunan Nusantara V,” vol. 3, no. 4, pp. 351–356, 2019.
A. Lailan and U. Kurnia, “Prediksi Replanting Lahan Kelapa Sawit Dengan Menggunakan Metode K-Nearest Neighbor,” PELITA Inform., vol. 18, pp. 517–522, 2019.
N. Sirait, “Implementasi K-Means Clustering Pada Pengelompokan Mutu Biji Sawit,” J. Pelita Inform., vol. 16, no. 4, pp. 368–372, 2017.
N. Krisandi, Helmi, and B. Prihandono, “Algoritma K-Nearest Neighbor Dalam Klasifikasi Data HAsil Produksi Kelapa Sawit Pada PT. Minamas Kecamatan Parindu,” vol. 02, no. 1, pp. 33–38, 2013.
Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan Pada RSUD Pekanbaru,” vol. 01, pp. 17–24, 2019.
L. Maulida, “Penerapan Data Mining Dalam Mengelompokkan Kunjungan Wisatawan Ke Objek Wisata Unggulan Di Prov. DKI Jakarta Dengan K-Means,” vol. 2, no. 3, pp. 167–174, 2018.
N. Erlangga, Solikhun, and Irawan, “Penerapan Data Mining Dalam Mengelompokan Produksi Jagung Menurut Provinsi Menggunakan Algoritma K-Means,” vol. 3, pp. 702–709, 2019.
I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center Untuk Clustering Program SDP,” vol. 3, no. 1, pp. 87–93, 2018.
Copyright (c) 2021 Deny Franata Pasaribu, Irfan Sudahri Damanik, Eka Irawan, Suhada, Heru Satria Tambunan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
