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 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.