Analisis Data Mining dengan Metode K-Means Clustering Dalam Pengelompokan Penggunaan Alat Kontrasepsi
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.750Keywords:
Data Mining; K-Means Clustering; Contraceptives; Clustering; Family PlanningAbstract
Family Planning (KB) is a strategic government effort to suppress population growth and improve the quality of life. The availability of various types of contraceptives can delay unwanted pregnancies, including in women facing increased pregnancy risks. Based on this, this study aims to cluster contraceptive use. The K-Means Clustering method is an unsupervised learning algorithm used to group data into several clusters based on similar characteristics. This algorithm works by minimizing the distance between the data and the cluster center (centroid). The advantages of K-Means are its simplicity and speed in processing large data. This research variable uses data from the 2024 Family Data Collection of the BKKBN Representative Office of West Sumatra Province in West Pasaman Regency. Based on the application of the K-Means Clustering method to the contraceptive use data, the grouping is obtained into three clusters: low use of MKJP contraceptives, moderate use of MKJP contraceptives, and high use of MKJP contraceptives. This study contributes in the form of a data mining-based analysis model that is able to group contraceptive use patterns in a more structured and objective manner. By applying the K-Means Clustering method, this study produces information that can be used to identify the characteristics of each user group, so that relevant agencies can design more targeted contraceptive counseling and distribution strategies.
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