Analisis Klaster Negara Berdasarkan Indikator Sosial-Ekonomi Menggunakan Fuzzy C-Means dan K-Means - Sulaiman, Davi; Afrian Al-Haritz, Rafi; Ahmad Saputra, Fahim; Irawan, Ade (Rekursif: Jurnal Informatika, 2024)
This paper analyzes country clustering based on socio-economic indicators using Fuzzy C-Means (FCM) and K-Means algorithms. Each country has unique socio-economic characteristics that include aspects such as child mortality, exports, imports, per capita income, inflation, life expectancy, and gross domestic product. The dataset used includes 167 countries with 10 key indicators. After pre-processing and normalizing the data, clustering is performed using FCM and K-Means, where effectiveness is evaluated based on Sum of Squared Errors (SSE) and Silhoutte Score. This research aims to find the best algorithm in terms of accuracy and time efficiency in clustering countries based on socio-economic indicators. Keywords: Clustering, Fuzzy C-Means, K-Means, Socio-economic indicators, Sum of Squared Errors (SSE), Silhouttte Score.