Implementasi Algoritma K-Medoids Clustering Untuk Mencari Keuntungan Sementara Dalam Laporan Keuangan - Annisa Oktavianti Hermadi; wowon Priatna; Allan D Alexander (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2023)
The current economy has developed rapidly, especially with advances in technology in obtaining an installation decision, especially in elementary school installations where in private elementary school installations, there is some financial report data information such as income, expenses, and the results that have been achieved in these installations such as in SD Plus Albina. However, at SD Plus Albina, Bekasi Regency, the role of technology has yet to be implemented, especially in the financial reporting system, by knowing the level of temporary profits because knowing the value of temporary profits can make it easier to make decisions. Using the K-Medoids Clustering Algorithm, you can find out the temporary profit level with manual calculations using Microsoft Excel 2020 and sample data, 2 clusters are obtained, namely high and low. The low cluster (C1) is 2 data, and the high cluster (C2) is 12. Compared to other clustering algorithms, the advantage of the K-Medoids algorithm is that it is more reliable when there is noise data because it is not too affected by other extreme data.