Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara

https://doi.org/10.22146/ijccs.83195

Mutammimul Ula(1*), Gita Perdinanta(2), Rahmad Hidayat(3), Ilham Sahputra(4)

(1) Information System, Malikussaleh University, Lhokseumawe
(2) Information System, Malikussaleh University, Lhokseumawe
(3) Department of Information Technology and Computer, Politeknik Negeri Lhokseumawe, Lhokseumawe
(4) Information System, Malikussaleh University, Lhokseumawe
(*) Corresponding Author

Abstract


PT. Perkebunan Nusantara 1 is engaged in oil palm production with a total land area of 1,144 Ha. The formulation of this research can determine productive land clusters based on land area, number of trees, number of stages, and palm oil production. Methodological steps include plantation area data and oil palm production data. This study can compare the C-means and K-means groups. As for predictions using the Backpropagation Neural Network (BPNN) algorithm and Fuzzy time series for production results. The results of grouping Cot girek palm oil production data for the 2019-2022 period from January to December were 1,365,530, while in 2022 it reached 1,768,720. The analysis used a land grouping method of 1,144 hectares, which resulted in 800.4 hectares of productive land and 343.6 hectares of less effective land. The results of the C-menas clustering model are more than K-meas with shorter iterations while for predictions it has an accuracy rate of 90.77%. As a comparison, the level of accuracy of the fuzzy time series is 81.27%. The results of this study can be used as recommendations for companies in the analysis of productive land grouping analysis and forecast results from these lands.


Keywords


Clustering; K-means; C-Means; Prediction; Backpropagation

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DOI: https://doi.org/10.22146/ijccs.83195

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