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All Journal Innovative: Journal Of Social Science Research Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Sinaga, Roberto Karlos
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Humanities Computer Science & IT Earth & Planetary Sciences Law, Crime, Criminology & Criminal Justice Mathematics Public Health Other

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Penerapan Metode Seleksi Maju dan Metode Penyisihan untuk Analisis Faktor Penentu Impor Beras BULOG di Sumatera Utara Purba, Mutia Agustin; Siregar, Usnul Marisa; Sinaga, Roberto Karlos; Hasibuan, Febriyanti; Fitriyani; Chairunisah
Innovative: Journal Of Social Science Research Vol. 5 No. 1 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i1.17024

Abstract

This research aims to examine the factors that influence BULOG’S rice imports in North Sumatra using the forward selection and backward elimination. This research focuses on the factor that influence the amount of rice imports considering how important rice is as a staple food in Indonesia. The data used in this research is secondary data on rice imports in 2010-2020 provided by the North Sumatra Province Central Statistics Agency. The analysis findings show that, with a combined contribution of 96.5%, BULOG’S rice output and stocks are the main element that have a large impact on the volume of rice imports. This research provides an important contribution in formulating the dynamics of rice imports to North Sumatra which can serve as a guide for developing food security polices.
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 Vol. 4 No. 1 (2026): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v4i1.891

Abstract

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.
Co-Authors Angelyca Chairunisah Fitriyani Hasibuan, Febriyanti Lubis, Rhamanda Ardiansyah Manulang, Sudianto Nainggolan, Dina Olivia Nasution, Alvi Sahrin Purba, Mutia Agustin Rizky Saputra Tobing Sigalingging, Ocha Hosea Siregar, Usnul Marisa