Analysis of the Spatial Distribution of Rice Prices Acrossprovinces in Indonesia Using the K-Means Algorithm
-
Parini
*
Universitas Royal,
Indonesia
- Yessica Siagian Universitas Royal, Indonesia
- Guntur Maha Putra Universitas Royal, Indonesia
- Julpan Hartono Suria Manja Manurung Universitas Royal, Indonesia
- Nuri Apriani Universitas Royal, Indonesia
- Putri Wulandari Universitas Royal, Indonesia
Abstract
Differences in rice prices across provinces in Indonesia reflect variations in distribution and market conditions that can be analyzed using a data clustering approach. This study aims to map the characteristics of interprovincial rice prices using the K-Means Clustering algorithm. The dataset consists of prices for Premium Rice, Medium Rice, Non-SPHP Medium Rice, and SPHP Rice obtained from official government data. The research stages included data preprocessing, transformation, normalization using StandardScaler, determining the number of clusters using the Elbow Method and Silhouette Score, and visualizing the results using Principal Component Analysis (PCA). The Elbow Method results indicated an efficient point at K = 5, while the highest Silhouette Score of 0.5658 was obtained at K = 7; thus, seven clusters were selected as the best model. PCA visualization reveals that provinces in Indonesia exhibit rice price characteristics that form seven groups with varying degrees of similarity. The research results indicate that the K-Means algorithm is capable of effectively identifying rice price clustering patterns and producing a map that can support the analysis of inter-regional price characteristics as a basis for formulating food distribution policies and stabilizing rice prices in Indonesia.
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