Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh
The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to improve the performance of the K-Means algorithm by integrating the Purity method, specifically focusing on clustering regions renowned for oil palm production in North...
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Universitas Islam Negeri Sunan Kalijaga Yogyakarta
2025-01-01
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| Series: | JISKA (Jurnal Informatika Sunan Kalijaga) |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4817 |
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| author | Novia Hasdyna Rozzi Kesuma Dinata Balqis Yafis |
| author_facet | Novia Hasdyna Rozzi Kesuma Dinata Balqis Yafis |
| author_sort | Novia Hasdyna |
| collection | DOAJ |
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The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to improve the performance of the K-Means algorithm by integrating the Purity method, specifically focusing on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity+K-Means. The integration of the Purity method increases the efficiency of K-Means by decreasing the required iterations for convergence. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity+K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity+K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.
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| format | Article |
| id | doaj-art-b99f61f541b343e587ef54b8b516b64d |
| institution | Kabale University |
| issn | 2527-5836 2528-0074 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Islam Negeri Sunan Kalijaga Yogyakarta |
| record_format | Article |
| series | JISKA (Jurnal Informatika Sunan Kalijaga) |
| spelling | doaj-art-b99f61f541b343e587ef54b8b516b64d2025-02-02T00:37:10ZengUniversitas Islam Negeri Sunan Kalijaga YogyakartaJISKA (Jurnal Informatika Sunan Kalijaga)2527-58362528-00742025-01-01101Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North AcehNovia Hasdyna0Rozzi Kesuma Dinata1Balqis Yafis2Universitas Islam Kebangsaan IndonesiaUniversitas MalikussalehNational Yang Ming Chiao Tung University The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to improve the performance of the K-Means algorithm by integrating the Purity method, specifically focusing on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity+K-Means. The integration of the Purity method increases the efficiency of K-Means by decreasing the required iterations for convergence. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity+K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity+K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency. https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4817K-Means Algorithm, Purity Method, Data Clustering, Oil Palm Production, North Aceh, Davies-Bouldin Index (DBI) |
| spellingShingle | Novia Hasdyna Rozzi Kesuma Dinata Balqis Yafis Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh JISKA (Jurnal Informatika Sunan Kalijaga) K-Means Algorithm, Purity Method, Data Clustering, Oil Palm Production, North Aceh, Davies-Bouldin Index (DBI) |
| title | Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh |
| title_full | Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh |
| title_fullStr | Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh |
| title_full_unstemmed | Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh |
| title_short | Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh |
| title_sort | optimizing k means algorithm using the purity method for clustering oil palm producing regions in north aceh |
| topic | K-Means Algorithm, Purity Method, Data Clustering, Oil Palm Production, North Aceh, Davies-Bouldin Index (DBI) |
| url | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4817 |
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