Lesmana, Agung (2022) PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS DALAM PENCLUSTERAN DATA PENJUALAN PT. UNITED TEKNOLOGI INTEGRASI. S1 thesis, Universitas Mercu Buana Jakarta-Menteng.
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Abstract
Recording sales transactions in a company is a very important thing to do. By recording sales transactions every day the company can find out the increase in the level of sales of goods. Sales transaction data contains data on goods that have been sold, the number of goods, the name of goods, the price of goods, and the name of customers who make purchases of goods. This sales data can be utilized to help the company in increasing the company's sales. This research utilizes data mining techniques that cluster data with K-Means and K-Medoids algorithms on sales data. This study conducted clustering sales data, data taken starting from 2018 - 2020 at PT United Teknologi Integrasi. Data were taken from 2018 because the recording system implemented in this company began to be used in 2018. The determination of clustering is determined using the elbow method that shows the most optimal cluster results formed are as many as three clusters, namely products that sell best-selling, best-selling, and less in-demand. These results are used to provide more promotion to existing products in the cluster less in demand to be able to increase sales. Then a comparison of the results of clustering algorithms with cluster quality tests using the Silhouette Index method which produces a maximum index value of 0.404 on the K-Means algorithm while 0.376 on the K-Medoids algorithm. The results of this study concluded that the K-Means Algorithm has better quality in forming clusters. This research is expected to be a consideration of which algorithm is most appropriate to be applied in the processing of sales data and the results of clustering data processing can be utilized to determine sales strategies. Key words: k-medoids algorithm, k-means algorithm, data mining, clustering, sales data
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41517120044 |
| Uncontrolled Keywords: | k-medoids algorithm, k-means algorithm, data mining, clustering, sales data |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Maulana Arif Hidayat |
| Date Deposited: | 21 Apr 2022 06:41 |
| Last Modified: | 21 Apr 2022 06:41 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/60339 |
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