K-Means Clustering Algorithm Approach in Clustering Data on Cocoa
Production Results in the Sumatra Region

K-Means Clustering Algorithm Approach in Clustering Data on Cocoa
Production Results in the Sumatra Region

K- Means Clustering Algorithm, Gaussian Mixture Model, Data Mapping, Cocoa Farm

Cocoa agricultural production in Indonesia is currently very low while demand continues to increase every year, so it is very
important to build a model that can categorize cocoa farming data. The main objective of this research is to analyze agricultural
data using data mining techniques that specifically use the K-Means Clustering algorithm, and Gaussian Mixture Models. In
this research, we used quantitative research because it measure number-based data. The results of cocoa production so far
still depend on land area, then the number of cocoa trees has a significant effect on the amount of production so it is very
important for the government and researchers to develop technologies that can increase cocoa production yields where the
demand for cocoa is currently very high in demand worldwide because it can classify the cocoa quality from good quality to
poor quality. Based on testing the K-Means Clustering and Gaussian Mixture Model algorithms on data on cocoa production
in four provinces, namely North Sumatra, West Sumatra, Lampung and Aceh which were optimized by the Silhouette method,
it produced cluster values of 2, 3 and 4. second with a value of 59.8%.

Mawaddah Harahap1
, Arief Wahyu Dwi Ramadhanu Zamili 2
, Muhammad Arie Arvansyah3
, Erwin Fransiscus
Saragih4
, Selwa Rajen5
, Amir Mahmud Husein

University of Prima Indonesia

27-12-2022

Fajar bagus W

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Indonesia

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