Klasifikasi Tingkat Masalah Gizi Balita 2024 Menggunakan Logika Fuzzy Mamdani di Jawa Tengah Klasifikasi Tingkat Masalah Gizi Balita 2024 Menggunakan Logika Fuzzy Mamdani di Jawa Tengah
Abstract
Child malnutrition remains a serious challenge in developing countries, including Indonesia, particularly in Central Java Province, which has recorded alarming prevalence rates of stunting, wasting, and overweight. This condition affects children's cognitive development, brain function, academic achievement, and future productivity. Variations in nutritional indicators across districts and municipalities render conventional analysis inadequate for accurately depicting local conditions. This study aims to develop a classification system for the level of under-five malnutrition using the Mamdani Fuzzy Inference System (FIS) method assisted by RStudio software. The model utilizes three main indicators as inputs—stunting, wasting, and overweight—derived from the 2024 SSGI (Indonesian Nutritional Status Survey) data, with 27 fuzzy rules (rule base), and produces an output classifying the level of under-five malnutrition into four categories: low, moderate, high, and very high. The analysis was conducted on 35 districts/municipalities in Central Java. The results show that 9 regions (25.71%) fall into the low category, 23 regions (65.71%) into the moderate category, 3 regions (8.57%) into the high category, and none into the very high category. The fuzzy approach proved more flexible and adaptive than traditional categorical methods and shows potential as a decision-support tool for regional nutrition intervention policy.
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