Digitizing Paddy Harvest Productivity Based on K-Means Clustering

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Fitri Marisa
Abi Zahma
Adrianus Muit Bau
Egy Noviansa
Adi Semri Neno
Almaukar Anastasia

Abstract

Paddy as an ingredient of staple food by people in Indonesia includes in East Java Province. Therefore attention to the production of paddy in East Java is necessary, and this attention will give a piece of knowledge about which region produces paddy optimally or less optimal. This study aim is to do a clustering about paddy production in each region in East Java. K-Means algorithm uses to do clustering. The result is 3 clusters obtained, high, medium, and less productivity cluster. There are six regions in high productivity cluster, 20 regions in medium productivity cluster, and 12 regions in less productivity cluster.

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How to Cite
[1]
F. Marisa, A. Zahma, A. Muit Bau, E. Noviansa, A. Semri Neno, and A. Anastasia, “Digitizing Paddy Harvest Productivity Based on K-Means Clustering ”, SMARTICS, vol. 7, no. 1, pp. 21-26, Apr. 2021.
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