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

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 2 (2023) · pp. 195–206

DOI
10.22146/ijccs.83195
ISSN
2460-7258, 1978-1520
Published
2023-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Collaboration network of the authors

6 authors · 8 co-authorship links · 5 across different organisations

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  • Malikussaleh University
  • Universitas Gadjah Mada
  • Politeknik Negeri Lhokseumawe
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Azhari SnUniversitas Gadjah Mada1
Sri MulyanaUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara</dc:title>
	<dc:creator>Ula, Mutammimul</dc:creator>
	<dc:creator>Perdinanta, Gita</dc:creator>
	<dc:creator>Hidayat, Rahmad</dc:creator>
	<dc:creator>Sahputra, Ilham</dc:creator>
	<dc:subject xml:lang="en-US">Sistem Informasi, Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Clustering; K-means; C-Means; Prediction; Backpropagation</dc:subject>
	<dc:description xml:lang="en-US">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. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
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	<dc:date>2023-04-30</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/83195</dc:identifier>
	<dc:identifier>10.22146/ijccs.83195</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 2 (2023): April; 195-206</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/83195/36324</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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