Open AccessSegmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas
Author(s) -
Trisna Yuniarti,
Dahliyah Hayati
Publication year - 2021
Publication title -
inventory
Language(s) - English
Resource type - Journals
ISSN - 2723-1895
DOI - 10.52759/inventory.v2i2.47
Subject(s) - palm oil , cluster analysis , productivity , government (linguistics) , agricultural science , agricultural engineering , geography , business , computer science , engineering , environmental science , artificial intelligence , economics , linguistics , philosophy , macroeconomics
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.