Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
136
Views
1
Downloads
Implementation of k-Means Clustering Algorithm based on Rice Productivity Level in Subdistrict Area (Case Study: In Special Region of Yogyakarta)
Indrawan Risangaji, Muhammad Nasrun and Anggunmeka Luhur Prasasti
Page: 110-116 | Received 21 Sep 2022, Published online: 21 Sep 2022
Full Text Reference XML File PDF File
Abstract
The need for people to consume rice continues to increase which causes the price of rice in the market to rise and fall in the Yogyakarta Special Region while the government still lacks attention to rice production in each year. This research focuses on making regional groupings in Yogyakarta Special Region based on the level of rice productivity. Data mining process is done by clustering using k-means to classify sub-districts based on production data and land area. Evaluation of the results of clusters uses elbow method and comparison of cluster results with other programs. Regional grouping based on the level of rice productivity produces 3 optimal clusters which are divided into low productivity, medium productivity and high productivity. With the results of this study, it is expected to help the Yogyakarta agricultural service in an effort to increase rice productivity more evenly in each region.
How to cite this article:
Indrawan Risangaji, Muhammad Nasrun and Anggunmeka Luhur Prasasti. Implementation of k-Means Clustering Algorithm based on Rice Productivity
Level in Subdistrict Area (Case Study: In Special Region of Yogyakarta).
DOI: https://doi.org/10.36478/rjasci.2019.110.116
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2019.110.116