Sigalingging, Ocha Hosea
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Analisis Pengaruh Jumlah Penduduk dan UMP/UMK terhadap Jumlah Kemiskinan Menggunakan Metode Regresi Linear Berganda di Provinsi Sumatera Utara Hasibuan, Eka Sri Hartini; Napitu, Cindy Angelina Saragi; Pinem, Handre Gabriel; Ginting, Mikolis Etimanta; Sigalingging, Ocha Hosea; Chairunisah, Chairunisah
Jurnal Pendidikan Tambusai Vol. 8 No. 3 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Penelitian dilakukan untuk mengetahui perubahan tingkat kemiskinan di Sumatera Utara pada tahun 2022 dan 2023. Perubahan tersebut diamati dengan memperhatikan jumlah penduduk miskin di Kab/Kota Sumatera Utara dan beberapa faktor penyebabnya, yaitu jumlah penduduk Sumatera Utara dan besaran nilai UMP/UMK di seluruh daerah Sumatera Utara pada tahun 2022 dan 2023. Dengan variabel yang telah ditetapkan, penelitian dilakukan menggunakan analisis regresi linear berganda. Dari penelitian yang dilakukan, diketahui bahwa jumlah penduduk memiliki pengaruh besar dalam menghasilkan jumlah kemiskinan di Sumatera Utara dibandingkan nilai UMP/UMK daerah tersebut.
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.