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Journal : Building of Informatics, Technology and Science

Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam Ahmad Rifqi; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4278

Abstract

Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.
Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids Jhiro Faran; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4313

Abstract

Class assignments are carried out to focus students on the subjects that will be studied during Senior High School (SMA). Class majors are generally carried out in class of all the main values used in the class majoring process. This is a problem with the class majoring process, where mistakes often occur in the class majoring process. Mistakes regarding class majors made by students will have quite a fatal impact on the student, apart from not being able to change classes, it will also have a laziness effect on the student because it does not match the student's abilities. Solving this problem can be done using a technique called data mining. The solution to this problem is done using clustering. The K-Medoids algorithm is the algorithm used to solve the problems in this research. The process of grouping or forming clusters in the K-Medoids algorithm is based on calculating the closest distance to each object, calculating the closest distance is based on determining the centeroid value first. The K-Medoids algorithm can form 2 (two) clusters according to existing class majors. The results obtained show that there are 3 (three) alternatives included in cluster 1 and also 12 (twelve) alternatives included in cluster 2.