KETEPATAN KLASIFIKASI STATUS STUNTING PADA BALITA MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CLASSIFICATION AND REGRESSION TREE (CART) BERDASARKAN NILAI F1-SCORE (STUDI KASUS : KASUS STUNTING DI PAPUA TENGAH TAHUN 2023)

Noorkhaliza Maulidyani Safira, NIM.: 21106010072 (2025) KETEPATAN KLASIFIKASI STATUS STUNTING PADA BALITA MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CLASSIFICATION AND REGRESSION TREE (CART) BERDASARKAN NILAI F1-SCORE (STUDI KASUS : KASUS STUNTING DI PAPUA TENGAH TAHUN 2023). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Stunting is a chronic nutritional problem in toddlers that has long-term effects on physical growth, cognitive development, and future productivity. Central Papua is recorded as the province with the highest stunting prevalence in Indonesia in 2023, at 39.4%. This study aims to identify variables influencing stunting incidence and compare the accuracy of classification using Binary Logistic Regression and Classification and Regression Tree (CART) methods based on F1-score values. The data used were sourced from the 2023 Indonesian Health Survey obtained from the Center for Data and Information Technology of the Indonesian Ministry of Health, with a total sample of 401 toddlers from six districts in Central Papua. Data processing and analysis were conducted using RStudio software. Stunting status was determined based on the z-score of the height-for-age index (HAZ) calculated using the WHO Anthro software. The analysis results showed that out of 11 predictor variables, only age and height were significant predictors of stunting. The F1-score values for the Binary Logistic Regression method and the CART method are 95.73% and 90.38%, respectively. Thus, the Binary Logistic Regression method is more optimal in identifying factors influencing stunting in infants in Central Papua in 2023.

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