Pengelompokan Remaja Berdasarkan Segmentasi Usia Menggunakan Metode K-Means Clustering (Studi Kasus : Desa Sindangsari)
DOI:
https://doi.org/10.55606/akuntansi.v2i2.236Keywords:
Data Mining, K-Means, Clustering, and Adolescent Phase.Abstract
Data mining is processing information from a database that can be used for various needs. One of the methods in data mining, namely Clustering which aims to find groupings from a series of patterns, points, objects and documents. The K-Means clustering algorithm is an algorithm that plays an important role in the field of data mining and is simple to implement and run. The K- Means Clustering method attempts to group existing data into several groups, where the data in one group have the same characteristics. By conducting clustering research youth based on age segmentation using the k-means clustering method is expected to be able to contribute especially to PIK R colleagues in dividing the segmentation of PIK R members easily and systematically without using manual methods. This age segmentation can be used to determine the level of development, needs, and preferences of adolescents in various aspects of life. This study aims to process the number of adolescents for members of the PIK-R organization, it is hoped that it will make it easier for secretaries in the Pik-R organization to manage new membership recruitment data based on age and knowing which hamlet has the most teenage population. In each cluster it is classified based on which criteria are prioritized. System testing was carried out 4 times with data consisting of 24 attributes 1789 records of new PIK-R members to get precision implementation results K-Means Clustering method.
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Diananda, A. (2018). PSIKOLOGI REMAJA DAN PERMASALAHANNYA. In ISTIGHNA (Vol. 1, Issue 1). www.depkes.go.id
Feng, G., Fan, M., & Chen, Y. (2022). Analysis and Prediction of Students’ Academic Performance Based on Educational Data Mining. IEEE Access, 10, 19558–19571. https://doi.org/10.1109/ACCESS.2022.3151652
Muttaqin, M. R., & Defriani, M. (2020). Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa. ILKOM Jurnal Ilmiah, 12(2), 121–129. https://doi.org/10.33096/ilkom.v12i2.542.121-129
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Rustam, S., Santoso, H. A., & Supriyanto, C. (2018). OPTIMASI K-MEANS CLUSTERING UNTUK IDENTIFIKASI DAERAH ENDEMIK PENYAKIT MENULAR DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DI KOTA SEMARANG. ILKOM Jurnal Ilmiah, 10(3), 251–259. https://doi.org/10.33096/ilkom.v10i3.342.251-259
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