K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region
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 speci...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4199 |
| _version_ | 1827361468053454848 |
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| author | Mawaddah Harahap Arief Wahyu Dwi Ramadhanu Zamili Muhammad Arie Arvansyah Erwin Fransiscus Saragih Selwa Rajen Amir Mahmud Husein |
| author_facet | Mawaddah Harahap Arief Wahyu Dwi Ramadhanu Zamili Muhammad Arie Arvansyah Erwin Fransiscus Saragih Selwa Rajen Amir Mahmud Husein |
| author_sort | Mawaddah Harahap |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T07:05:42Z |
| format | Article |
| id | doaj.art-5d3b5b8bf2524734a67ba87795d0370f |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:05:42Z |
| publishDate | 2022-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-5d3b5b8bf2524734a67ba87795d0370f2024-02-03T04:54:22ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-12-016690591010.29207/resti.v6i6.41994199K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra RegionMawaddah Harahap0Arief Wahyu Dwi Ramadhanu Zamili1Muhammad Arie Arvansyah2Erwin Fransiscus Saragih3Selwa Rajen4Amir Mahmud Husein5Universitas Prima IndonesiaUniversitas Prima IndonesiaUniversitas Prima IndonesiaUniversitas Prima IndonesiaUniversitas Prima IndonesiaUniversitas Prima IndonesiaCocoa 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%.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4199k- means clustering algorithmgaussian mixture modeldata mappingcocoa farm |
| spellingShingle | Mawaddah Harahap Arief Wahyu Dwi Ramadhanu Zamili Muhammad Arie Arvansyah Erwin Fransiscus Saragih Selwa Rajen Amir Mahmud Husein K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) k- means clustering algorithm gaussian mixture model data mapping cocoa farm |
| title | K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region |
| title_full | K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region |
| title_fullStr | K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region |
| title_full_unstemmed | K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region |
| title_short | K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region |
| title_sort | k means clustering algorithm approach in clustering data on cocoa production results in the sumatra region |
| topic | k- means clustering algorithm gaussian mixture model data mapping cocoa farm |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4199 |
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