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All Journal Building of Informatics, Technology and Science
Rachmatia, Baiq Wita
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Computer Science & IT

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Segmentasi Produk Minuman Tidak Termasuk Produk Susu Berdasarkan Informasi Nilai Gizi Menggunakan Metode DBSCAN Rachmatia, Baiq Wita; Primandari, Arum Handini
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7255

Abstract

Approximately 28.7% of Indonesians consume sugar, salt, and fat (SSF) in amounts that exceed the Ministry of Health's recommended limits. Over the past two decades, sweetened drink (MBDK: minuman berpemanis dalam kemasan) consumption has surged, making Indonesia the third highest in Southeast Asia for MBDK consumption. To mitigate this, consumers need clear information about GGL content, but nutritional labels are often complex and underutilized. Product segmentation can help consumers make healthier drink choices and support health interventions aimed at reducing risky consumption. Data on GGL values were collected from MBDK sold in three store types and analyzed using the DBSCAN method, which handles diversity and outliers without predefining cluster numbers. Descriptive statistics showed most products had low fat but higher sugar content, nearing 15 grams. After standardizing the data using z-scores, the DBSCAN clustering revealed two clusters and some noise. The evaluation indicated a silhouette coefficient of 0.396 and a Dunn index of 0.137, with t-tests showing significant differences between the clusters.
Co-Authors Primandari, Arum Handini