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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Melva Regina Arpratika
STIMIK IKMI Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village Melva Regina Arpratika; Nana Suarna; Agus Bahtiar; Martanto; Odi Nurdiawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2248

Abstract

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy. The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality. The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors. This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs. Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning
Co-Authors Martanto . Nana Suarna Odi Nurdiawan