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Ashari, Ayu
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Journal : Building of Informatics, Technology and Science

 1 
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Co-Authors A.Y. Soegeng Adelena Agiwahyuanto, Faik Agushybana , Farid Ahmad Hanafie Aina, Ganea Qorry Al Fitri, Muhammad Rizqi Al Mefa, Fadira Al zami, Farrikh Alzami, Farrikh Atari, Nabila Azkiya, Nur Anita Azzura, Aika cantika, clarisa martila Cerlyawati, Hugi Dzuha Hening Yanuarsari, Dzuha Hening Eti Rimawati Fajri, Muhammad Taufik Feny Marselina Fitri, Ichlasia Ainul Fitria Wulandari Hadi, Heru Pramono Haidar, Kadori Hakam, Mochamad Abdul Hallang Lewa, Andi Hamdan Arfandy Hanafiah, Akhmad Harahap, Ade Chita Putri Hastuti Agussalim Husna, Najwa Ida Farida Ilyas, Nita Magfirah Jaka Prasetya Juli Ratnawati Karis Widyatmoko Kris Setyaningsih, Kris Kusumawati, Nugraheni Kusumawati, Yupie Lewa, Andi Hallang Megantara, Rama Aria Moh. Yusuf, Moh. Mr Sjaeful Anwar Mudzanatun Muhammad Naufal, Muhammad Muhammad Yunus Munawwarah Munawwarah Nafira, Fathan Azfa Nakayaanni, Putri Nazella, Desvita Dian Nazira Dhani, Zahra Ninik Christiani Nur Hikmah Nuridzin, Dion Zein Omay Sumarna Padapi, Astrini Pirawati Prihandono, Adi prizkila, cici Puji Purwatiningsih, Aris R.B. D, Keukeu Amalia Rabial Kanada, Rabial Rahmayani, Hafsah Dahni Rambe, Afghan Bai Asy Ary Retno Astuti Setijaningsih Reza, Reza Rezky, Sri Rezky,  Sri Rohman, M. Hilma Minanur Safitri, Maria Santri, Ayu Sarqawi, Ahmad Sasono Wibowo Setiawan, Aries Siramaneerat, Issara Siti Zubaidah Sofiyanti, Ida Sugiarti, S. Suharnawi Suharnawi Sukamto, Titien Suhartini Sumiati Side SUTRISNO Tambunan, Rahma Sari Putri Tiara Dini Harlita Tuzahra, Sunia Umar, Bowasis wahyuni , kiki Wibowo, Syifa Sofia Widitya, Galuh Zaenal Arifin Zay, Fany Pebriani