Penerapan Principal Component Analysis (PCA) untuk Reduksi Dimensi dan Pemetaan Karakteristik Nutrisi pada Produk Makanan Kemasan di Indonesia - Rizky Saputra Tobing; Sigalingging, Ocha Hosea; Sinaga, Roberto Karlos; Lubis, Rhamanda Ardiansyah (Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa, 2026)
The increasing consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.