Implementasi Metode Fuzzy C-Means Dan Fuzzy Subtractive Clustering Dalam Pengklasteran Kabupaten/Kota Di Provinsi Sumatera Barat Berdasarkan Faktor Penyebab Stunting - Ferra Yanuar; Putri Aulia; Aidinil Zetra; Hazmira Yozza (Limits: Journal of Mathematics and Its Applications, 2026)
One of the problems of malnutrition that is still quite high in Indonesia is stunting. Stunting can occur as a result of malnutrition, especially during the 1000 HPK (First Day of Life). The government continues to make various efforts to emphasize the prevalence of stunting in toddlers, but these efforts are not effective enough. One way that is quite effective is to carry out cluster analysis. This research aims to group districts/cities in West Sumatra based on factors that cause stunting. The methods used in this research are fuzzy c-means and fuzzy subtractive clustering. The cluster validity tests used in this research are Modified Partition Coefficient (MPC), Partition Entropy (PE), and Xie-Beni index (XB). Based on the calculation of the three validity indices, it was found that the optimum number of clusters in clustering based on the FCM method was two clusters (c = 2). Meanwhile, in the fuzzy subtractive clustering method, the optimum number of clusters is found in clustering with radius (r) = 0.90 with the number of clusters formed being three clusters. In this research, the results showed that the fuzzy subtractive clustering method was better than the fuzzy c-means method because the resulting CE and XB validity index values were lower. Using the fuzzy subtractive clustering method, it was found that clusters one and two consisted of nine regions, while cluster three only consisted of one region