MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS
The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies gre...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Bina Bangsa
2024-12-01
|
| Series: | Jurnal Lebesgue |
| Subjects: | |
| Online Access: | https://lebesgue.lppmbinabangsa.id/index.php/home/article/view/844 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846098799030697984 |
|---|---|
| author | Ika Nur Laily Fitriana Fonda Leviany Ria Faulina Nuramaliyah Nuramaliyah Emeylia Safitri |
| author_facet | Ika Nur Laily Fitriana Fonda Leviany Ria Faulina Nuramaliyah Nuramaliyah Emeylia Safitri |
| author_sort | Ika Nur Laily Fitriana |
| collection | DOAJ |
| description | The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables. |
| format | Article |
| id | doaj-art-a1e5016276e741dd9bfbb0e1df842b19 |
| institution | Kabale University |
| issn | 2721-8929 2721-8937 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Universitas Bina Bangsa |
| record_format | Article |
| series | Jurnal Lebesgue |
| spelling | doaj-art-a1e5016276e741dd9bfbb0e1df842b192025-01-01T12:27:16ZengUniversitas Bina BangsaJurnal Lebesgue2721-89292721-89372024-12-01532178219610.46306/lb.v5i3.844844MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPSIka Nur Laily Fitriana0Fonda Leviany1Ria Faulina2Nuramaliyah Nuramaliyah3Emeylia Safitri4Universitas TerbukaUniversitas TerbukaUniversitas TerbukaUniversitas TerbukaUniversitas TerbukaThe agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables.https://lebesgue.lppmbinabangsa.id/index.php/home/article/view/844clusteringprincipal component analysisfield sectorself-organizing maps |
| spellingShingle | Ika Nur Laily Fitriana Fonda Leviany Ria Faulina Nuramaliyah Nuramaliyah Emeylia Safitri MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS Jurnal Lebesgue clustering principal component analysis field sector self-organizing maps |
| title | MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS |
| title_full | MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS |
| title_fullStr | MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS |
| title_full_unstemmed | MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS |
| title_short | MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS |
| title_sort | mapping indonesia s agricultural diversity clustering provinces with self organizing maps |
| topic | clustering principal component analysis field sector self-organizing maps |
| url | https://lebesgue.lppmbinabangsa.id/index.php/home/article/view/844 |
| work_keys_str_mv | AT ikanurlailyfitriana mappingindonesiasagriculturaldiversityclusteringprovinceswithselforganizingmaps AT fondaleviany mappingindonesiasagriculturaldiversityclusteringprovinceswithselforganizingmaps AT riafaulina mappingindonesiasagriculturaldiversityclusteringprovinceswithselforganizingmaps AT nuramaliyahnuramaliyah mappingindonesiasagriculturaldiversityclusteringprovinceswithselforganizingmaps AT emeyliasafitri mappingindonesiasagriculturaldiversityclusteringprovinceswithselforganizingmaps |