IDENTIFICATION OF DOMINANT FACTORS IN STUNTING CASE NARRATIVES USING PCA AND SVD APPROACHES

Debora Chrisinta; Justin Eduardo Simarmata; Milagros R. Baldemor; Miko Purnomo

doi:10.30598/parameterv4i1pp11-28

Debora Chrisinta Universitas Timor
Justin Eduardo Simarmata Universitas Timor
Milagros R. Baldemor Don Mariano Marcos Memorial State University
Miko Purnomo Universitas Timor

DOI: https://doi.org/10.30598/parameterv4i1pp11-28

Keywords: Stunting, Public Perception, TF-IDF, PCA, SVD, Text Analysis

Abstract

Stunting remains a significant public health issue in Indonesia, particularly in relation to public understanding of its causes and prevention. This study aims to analyze public perceptions of stunting based on reviews obtained through web scraping. The research methodology includes text processing with preprocessing techniques, Term Frequency-Inverse Document Frequency (TF-IDF) analysis, and dimensionality reduction using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). The research data consists of 21 reviews sourced from the 2023 Indonesian Health Survey (SKI), which were then converted into numerical data for further analysis. The findings indicate that PCA is more effective in simplifying the relationships among key terms related to stunting compared to SVD, as evidenced by a lower reconstruction error value (0.003861 < 0.004232). This suggests that PCA can preserve the essential information from the data with minimal distortion. The primary factors influencing public understanding include education, sanitation, and socio-economic conditions. The insights provided by this study reinforce that data-driven and visual-based educational strategies can enhance public awareness and contribute to more effective stunting prevention efforts.

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