Klasterisasi Data Sosial Ekonomi Menggunakan Algoritma K-Means - Tiyo Wahyudi; Miftahul Jannah; Zurnan Alfian (PT Pustaka Cendekia Group, 2024)
The advancement of information technology has driven a significant increase in the volume of socio-economic data, encompassing various aspects such as education and essential community needs. This study aims to cluster socio-economic data using the K-Means algorithm, focusing on two types of data: the number of applicants to higher education institutions (public and private universities) and the average prices of basic commodities in Palembang City. The first dataset was obtained from the Indonesian Higher Education Statistics (Depdiknas 2006), covering five categories of institutions: universities, institutes, colleges, academies, and polytechnics. The second dataset was taken from Table 8.2 of the Consumer Price Statistics by BPS for the years 2004–2005, which includes prices of commodities such as beef, broiler chicken, rice, granulated sugar, chicken eggs, and bulk cooking oil. The clustering process was carried out by normalizing the data using the Min-Max Scaling method, followed by the application of the K-Means algorithm with k = 2 clusters for educational data and k = 3 for commodity price data. The results showed that universities fall into the highest applicant cluster, while other institutions are grouped into medium to low clusters. In the commodity dataset, three price clusters were formed: high, medium, and low. These findings are expected to serve as a foundation for policy formulation in the education sector and for price control of essential goods in a more targeted and data-driven manner.