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All Journal Jurnal Sains dan Teknologi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Information Systems and Informatics JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal AbdiMas Nusa Mandiri TIERS Information Technology Journal Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Journal of Artificial Intelligence and Digital Business Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS)
Irma Darmayanti
Universitas Amikom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Journal of Information Systems and Informatics

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Clustering Sugar Content in Children's Snacks for Diabetes Prevention Using Unsupervised Learning Darmayanti, Irma; Saputra, Dhanar Intan Surya; Saputri, Inka; Hidayati, Nurul; Hermanto, Nandang
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.932

Abstract

Diabetes is a chronic health problem with increasing prevalence, especially among children, due to the consumption of sugary foods/beverages. This study aims to cluster children's snack products based on sugar content using unsupervised learning by combining Hierarchical Clustering and K-Means algorithms optimized using Silhouette Score. This combined approach utilizes Hierarchical Clustering to determine the optimal value (????) of K-Means, ensuring the efficiency and accuracy of data clustering. A total of 157 sample data were effectively clustered with K-means. The test results with Silhouette Score yielded the highest value of 0.380 for 2 clusters, while 3 clusters scored 0.350 and 0.277 for 4 clusters. For this reason, 2 clusters better represent the homogeneity of the data in the cluster, although it has not reached the ideal condition. Further analysis showed that high sugar and calorie content in sugary drinks, including milk, could increase blood glucose levels. These findings can be the basis for the development of consumer-friendly nutrition labels. However, support is needed from the government to create a labelling policy to ensure the sustainability of implementation in the community as an educational effort to prevent the risk of diabetes in children.
K-Means Clustering with Elbow Method for Stunting Risk Detection in Toddlers Using Anthropometric and Nutritional Data Darmayanti, Irma; Saputra, Dhanar Intan Surya; Wijaya, Anugerah Bagus; Wijanarko, Andik; Fortuna, Dewi; Putranto, Aldrian Firmansyah
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1337

Abstract

Stunting remains a critical public health challenge in Indonesia, primarily due to inadequate nutrition and recurrent infections in early childhood. This study aimed to identify patterns of stunting risk by integrating anthropometric and dietary data, specifically sugar consumption, using an unsupervised machine learning approach. A total of 20 toddlers aged 12-59 months from Purwokerto Selatan participated. Anthropometric data (age, weight, height) and dietary intake (sugar consumption, snack frequency) were collected via a caregiver questionnaire. K-Means clustering was applied, with the optimal number of clusters determined using the Elbow Method (K=2). Two clusters were identified: Cluster 0, with a lower risk of stunting, and Cluster 1, with a higher proportion of toddlers at risk. Cross-tabulation with stunting status validated this, showing that Cluster 1 contained more children with "Potential" stunting. Internal validation using the Silhouette score (0.252) and PCA visualization confirmed the clustering's robustness. This study demonstrates the potential of combining anthropometric and dietary data for stunting risk profiling, suggesting a complementary approach for growth monitoring programs and targeted interventions.
Co-Authors Adam Prayogo Kuncoro Adhimah, Laily Farkhah Agus Pramono Akmali, Fajar Aldrian Firmansyah Putranto Ali Nur Ikhsan Anam, Syaiful Nurul Ananda Putra Herlambang Andi Dwi Riyanto Andik Wijanarko, Andik Anunggilarso, Luky Rafi Apitiadi, Satyo Dwi Asro Nasiri Banu Dwi Putranto Bunga Asriandhini Deuis Nur Astrida Dewi Fortuna Dewi Fortuna Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dias Ayu Budi Utami Dias Ayu Budi Utami, Dias Ayu Budi Dony Ariyus Faridatun Nida Harun Alrasyid Herlambang, Ananda Putra Hidayah, Debby Ummul Hidayatuloh, Aden Ikhsan, Ali Nur Jali Suhaman Kusrini Kusrini Kusuma, Velizha Sandy Luki Rafi Anuggilarso Mustofa, Dinar Nandang Hermanto Nandang Hermanto Nikmah Trinarsih Nugraha, Yoga Adi Nurhafiyah, Isti Nurul Hidayati Permadi, Ma’rifan Prastyadi Wibawa Rahayu Primandani Arsi Pungkas Subarkah Putranto, Aldrian Firmansyah Rayinda Maya Anjani Riyanto Riyanto Riyanto Rizki Sadewo Rosana Fadilla Sari Sadewo, Rizki Saputra, Dimas Saputri, Inka Siti Alvi Solikhatin Subarkah, Pungkas Sugandi, Zain Arif Wildan Suhaman, Jali Tarwoto Wanda Fitrianingsih Wijaya, Anugerah Bagus