Soybean Collect Recommender Based on Distance and Productivity Cluster Using K-means Clustering and Simple Addictive Weighting Method

Mega Wahyu Ningtyas, Universitas Negeri Semarang, Indonesia
Feddy Setio Pribadi, Universitas Negeri Semarang, Indonesia

Keywords

cluster, K-Means, regency, Simple Additive Weighting, soybean

Document Type

Article

Abstract

Soybeans are an essential agricultural product that is one of the primary food sources in Indonesia, such as tempeh, tofu, soy milk, soy sauce, and other preparations. However, production yields, harvested land area, and soybean productivity in each district or city in Central Java Province vary widely. Differences in soybean productivity in each area are due to production factors such as area, use of fertilizers, seeds, and labor. This study tries to provide recommendations for soybean harvesting based on the distance and productivity of an area using K-means clustering and the simple addictive weighting method. In the Central Java Province, 35 regions will be divided into four clusters: the first with high productivity, the second with medium productivity; the third with low productivity; and the fourth with very low productivity. Additionally, based on the fourth cluster clustering results, it will be advised to take soybeans from other clusters by taking the closest distance and cluster members into account. According to the research, four clusters have formed: the first has five members, the second has fourteen, the third has nine, and the fourth has seven. The fourth cluster, which consists of seven members who do not grow soybeans, is advised to buy soybeans from the following regions: Kendal Regency, Klaten Regency, Magelang Regency, Batang Regency, and Brebes Regency.

First Page

Last Page

Page Range

86-95

Issue

Volume

Digital Object Identifier (DOI)

10.21831/elinvo.v8i1.53208

Source

https://journal.uny.ac.id/index.php/elinvo/article/view/53208

Recommended Citation

M. W. Ningtyas and F. S. Pribadi, "Soybean Collect Recommender Based on Distance and Productivity Cluster Using K-means Clustering and Simple Addictive Weighting Method,", vol. 8, no. 1, pp. 86 - 95, Dec 2023.

The definitive version is available at https://doi.org/10.21831/elinvo.v8i1.53208

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