Segmentasi Produk Minuman Tidak Termasuk Produk Susu Berdasarkan Informasi Nilai Gizi Menggunakan Metode DBSCAN
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.
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References
World Health Organization, “Diabetes.” Accessed: Nov. 07, 2024. [Online]. Available: https://www.who.int/health-topics/diabetes#tab=tab_1
IDF Diabetes Atlas, “ IDF Diabetes Atlas 10th Edition.” Accessed: Nov. 05, 2024. [Online]. Available: https://diabetesatlas.org/
Administrator, “Cegah Dini Ancaman Diabetes.” Accessed: Nov. 05, 2024. [Online]. Available: https://indonesia.go.id/kategori/editorial/8401/cegah-dini-ancaman-diabetes?lang=1
Purwowidhu, “Menakar Pembatasan Minuman Berpemanis Dalam Kemasan.” Accessed: Nov. 05, 2024. [Online]. Available: https://mediakeuangan.kemenkeu.go.id/article/show/menakar-pembatasan-minuman-berpemanis-dalam-kemasan
Rokom, “Konsumsi Gula Berlebih, Waspadai Risikonya.” Accessed: Nov. 15, 2024. [Online]. Available: https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20220927/2841159/konsumsi-gula-berlebih-waspadai-risikonya/
L. Qadrini et al., “Metode K-Means dan DBSCAN pada Pengelompokan Data Dasar Kompetensi Laboratorium ITS Tahun 2017,” J Statistika, vol. 13, no. 2, pp. 5–11, 2020, doi: https://doi.org/10.36456/jstat.vol13.no2.a2886.
S. Mutiah, Y. Hasnataeni, A. Fitrianto, L. Risman Dwi Jumansyah, and S. dan Sains, “Perbandingan Metode Klastering K-Means dan DBSCAN dalam Identifikasi Kelompok Rumah Tangga Berdasarkan Fasilitas Sosial Ekonomi di Jawa Barat,” Teorema: Teori dan Riset Matematika, vol. 09, no. 02, pp. 247–260, Sep. 2024, doi: http://dx.doi.org/10.25157/teorema.v9i2.16290.
M. R. M. Hasan, “Analisis Dampak Konsumsi Makanan dan Minuman Berpemanis terhadap Tingkat Diabetes Pada Remaja Indonesia,” MagnaSalus: Jurnal Keunggulan, vol. 06, no. 4, Oct. 2024, [Online]. Available: https://journalpedia.com/1/index.php/jkk
A. Syafrianto and E. Riswanto, “Pengelompokkan Jumlah Kunjungan Mahasiswa ke Perpustakaan Kampus Menggunakan Algoritma DBSCAN,” G-Tech: Jurnal Teknologi Terapan, vol. 7, no. 1, pp. 75–81, Jan. 2023, doi: https://doi.org/10.33379/gtech.v7i1.1925.
N. Nasution and F. Rakhmawati, “Segmentasi Pengguna E-Wallet dengan Menggunakan Metode DBSCAN (Density Based Spatial Clustering Application with Noise) di Kota Medan,” Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 4, no. 2, Aug. 2023, doi: https://doi.org/10.46306/lb.v4i2.
Badan Pengawas Obat dan Makanan Republik Indonesia, Peraturan BPOM nomor 13 Tahun 2023. 2023.
M. Bahrudin, “Cara Membaca Informasi Nilai Gizi pada Kemasan Makanan.” Accessed: Nov. 07, 2024. [Online]. Available: https://perpustakaan.bsn.go.id/index.php?p=news&id=1486#:~:text=Informasi%20nilai%20gizi%20(nutrition%20facts,konsumen%20untuk%20membeli%20suatu%20barang.
H. Astuti, “Penerapan Data Mining Menggunakan Metode K-Means Clustering Untuk Pengelompokkan Data Pelanggan (Studi Kasus : PT. Pinus Merah Abadi),” Jurnal Web Informatika Teknologi, vol. 6, no. 1, pp. 1–8, Jun. 2021, [Online]. Available: https://ejurnal-wit.ac.id/index.php/J-WIT/article/view/51
T. D. Harjanto, A. Vatresia, and R. Faurina, “Analisis Penetapan Skala Prioritas Penanganan Balita Stunting Menggunakan Metode DBSCAN Clustering,” Jurnal Rekursif, vol. 9, no. 1, Mar. 2021, doi: https://doi.org/10.33369/rekursif.v9i1.14982.
D. Fitrianah, W. Gunawan, and R. Algian Kurniaputra, “Implementasi Algoritma DBScan dalam Pemngambilan Data Menggunakan Scatterplot,” echno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 6, no. 2, p. 91, Oct. 2021.
R. R. Muhima et al., Kupas Tuntas Algoritma Clustering: Konsep Perhitungan Manual dan Program Oleh: Rani Rotul Muhima. Yogyakarta: Penerbit Andi, 2021.
D. A. Puspitasari, Y. Cahyana, and S. A. P. Lestari, “Penerapan Algoritma Density Based Spastial Clustering Algorithm With Noise Untuk Pengelompokkan Penyakit Pasien,” Scientific Student Journal for Information, Technology and Science, vol. 4, no. 1, pp. 102–106, Jan. 2023.
T. Rahmawati, Y. Wilandari, and P. Kartikasari, “Analisis Perbandingan Silhouette Coefficient dan Metode Elbow Pada Pengelompokkan Provinsi di Indonesia Berdasarkan Indikator IPM dengan K-Medoids,” Jurnal Gaussian, vol. 13, no. 1, pp. 13–24, Aug. 2024, doi: https://doi.org/10.14710/j.gauss.13.1.13-24.
F. N. Dhewayani, D. Amelia, D. N. Alifah, B. N. Sari, and M. Jajuli, “Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM,” Jurnal Teknologi dan Informasi (JATI), vol. 12, no. 1, pp. 64–77, Mar. 2022, doi: https://doi.org/10.34010/jati.v12i1.6674.
H. Malikhatin, A. Rusgiyono, and D. A. I. Maruddani, “Penerapan k-Modes Clustering dengan Validasi Dunn Index Pada Pengelompokkan Karakteristik Calon TKI Menggunakan R-GUI,” Jurnal Gaussian, vol. 10, no. 3, pp. 359–366, 2021, doi: https://doi.org/10.14710/j.gauss.10.3.359-366.
A. Fitri et al., Dasar-Dasar Statistika Untuk Penelitian. Medan: Yayasan Kita Menulis, 2023.
M. Marisya, “Analisis Data Menggunakan Uji t : Menentukan Perbedaan yang Signifikan antara Dua Kelompok,” OSF Preprints, Jun. 2023, doi: https://doi.org/10.31219/osf.io/t458y.
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