Siti Rafiah Rangkuti, Nurul Fadhillah, Rita Novita Sari, Klause Roder

IMPLEMENTATION OF PRINCIPAL COMPONENT ANALYSIS (PCA) IN DIMENSION REDUCTION BASED ON INDONESIAN HEALTH DATA

Introduction

Implementation of principal component analysis (pca) in dimension reduction based on indonesian health data. PCA for dimension reduction & visualization of complex Indonesian health data. Simplifies analysis, reveals patterns, & supports data-driven health decisions.

Abstract

Indonesian health data for 2024 has multidimensional characteristics with a large number of interconnected variables, leading to high complexity in the analysis and visualization process. This complexity poses a challenge in generating information that is easy to understand and can support data-driven decision-making. This research aims to implement the Principal Component Analysis (PCA) method as a technique for dimension reduction and visualization of Indonesian health data. The research method used is a quantitative approach with descriptive-exploratory secondary data analysis. The research stages include data pre-processing, PCA implementation, principal component determination, variable contribution analysis, and data visualization using scatter plots and biplots. The research results show that PCA is able to significantly reduce the number of variables while still retaining most of the main information contained in the data. Principal component analysis-based visualization produces clearer and more easily interpretable patterns and structures in health data. Thus, PCA has proven effective in simplifying the complexity of national health data and supporting the presentation of more informative and actionable information for decision-making in the health sector.

Review

This paper addresses a highly pertinent issue concerning the inherent complexity of contemporary health datasets, specifically focusing on Indonesian health data from 2024. The abstract clearly articulates the challenge posed by multidimensional characteristics and numerous interconnected variables, which frequently impede effective analysis, visualization, and subsequent data-driven decision-making. By aiming to implement Principal Component Analysis (PCA) for dimension reduction and visualization, the research directly tackles a significant bottleneck in extracting actionable insights from large-scale health information, positioning itself as a valuable contribution to health informatics and public health strategy. The methodology adopted is a quantitative approach employing descriptive-exploratory secondary data analysis, which is appropriate for the stated objectives. The research design outlines a comprehensive series of steps, including data pre-processing, PCA implementation, principal component determination, variable contribution analysis, and visualization through scatter plots and biplots. These steps demonstrate a robust framework for applying PCA effectively. The reported findings indicate that PCA successfully reduced the number of variables while preserving the majority of crucial information, and crucially, led to clearer, more interpretable patterns and structures in the health data visualizations. In conclusion, the study convincingly demonstrates the efficacy of Principal Component Analysis in simplifying the complexity associated with national health data. The capability of PCA to transform high-dimensional data into more manageable and visually informative representations is a significant outcome, directly supporting the generation of more actionable information for decision-makers in the health sector. This research provides a strong argument for the broader application of PCA as a fundamental tool in processing and understanding complex health datasets, thereby enhancing strategic planning and resource allocation in public health.

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