Analisis Kelompok Lansia Berdasarkan Kategori Usia Dengan Metode K-Means Clustering
DOI:
https://doi.org/10.55606/akuntansi.v2i2.234Keywords:
Data mining, Clustering, K-Means Clustering, elderly peopleAbstract
The application of k-means can be used to group the number of elderly people based on age category. By using this algorithm, groups of elderly residents who have the same age characteristics can be determined. Elderly (elderly) is the share of the population aged 60 years and over, the health department categorizes the elderly based on their age level, namely: early elderly, late elderly, and elderly). The increasing number of elderly people worldwide has created problems in managing the welfare of the elderly population. Inaccurate information regarding the distribution of the number of elderly people in each age category makes it difficult to make the right decisions to improve the welfare of the elderly population. In this case, the application of K-Means clustering can be used as a tool in grouping the number of elderly people based on age category. With this algorithm, data can be grouped quickly and efficiently, so that it can assist in making appropriate decisions to improve the welfare of the elderly population. However, the K-Means Clustering algorithm is only used as a tool, and must be strengthened with proper analysis and recommendations. In the village of Cimari, Cikoneng District, Ciamis Regency, in terms of grouping the elderly based on age categories, there are limitations in managing data on the elderly population, namely by manual method which requires quite a long time. The design method used is data collection: the number of elderly people and their age category. This data was obtained from data from Cimari villagers Based on the grouping results using K-Means Clustering on the grouping of the elderly population by age category, the Davies Bouldin results were 0.263, cluster 0 contained 119 items, cluster 1 contained 101 items, and cluster 2 contained 80 items.
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