K-MEANS CLUSTERING HWI PRODUCTS (Case Study: HWI Kisaran Distributor)

Risnawati Risnawati, Rohminatin Rohminatin

Abstract


HWI products are types of products in the form of food and health drinks and beauty products which are marketed and have been certified by DINKES, BPPOM, MUI and others, and provide excellent quality at relatively affordable prices and have re- leased 34 products. This HWI product itself can be obtained from an official HWI Distributor that has been verified. Of the many consumers and the many types of products registered with HWI distributors, the distributor makes a product supply without limit- ing the supply, but the large stock of products avail- able at the distributor does not guarantee that the consumer needs it, but it causes loss and income in- stability. In this study the method used is the K- Means Clustering method of 15 samples of product types. The data used and taken from the last 5 years namely 2015, 2016, 2017, 2018, and 2019 to do the grouping of data with the provisions of 3 clusters are Very in demand, in demand, and in demand. The results of the study and group-ing data by the K- Means Clustering method of 3 clusters determined there are 2 products with very best-selling clus-ters, 6 products with best-selling clusters, and 7 products with less-selling clusters.

Keywords: cluster; goods stock; HWI products; k-means clustering


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References


H. Agusvianto, “Sistem Informasi Inventori Gudang Untuk Mengontrol Persediaan Barang,” Journal Infrmation Engineering and Educational Technology, vol. 01, no. 012017. pp. 40–46, 2017.

E. Muningsih and S. Kiswati, “Penerapan Metode K-Means untuk Clustering Produk Online Shop dalam Penentuan Stok Barang,” J. Bianglala Inform., vol. 3, no. 1, pp. 10–17, 2015.

N. S. Represa, A. Fernández-Sarría, A. Porta, and J. Palomar-Vázquez, “Data Mining Paradigm in the Study of Air Quality,” Environ. Process., vol. 7, no. 1, 2020, doi: 10.1007/s40710-019-00407-5.

A. Rohman and M. Rochcham, “Komparasi Metode Klasifikasi Data Mining Untuk Prediksi Kelulusan Mahasiswa,” Neo Tek., vol. 5, no. 1, pp. 23–29, 2019, doi: 10.37760/neoteknika.v5i1.1379.

B. M. Metisen and H. L. Sari, “Analisis clustering menggunakan metode K-Means dalam pengelompokkan penjualan produk pada Swalayan Fadhila,” J. Media Infotama, vol. 11, no. 2, pp. 110–118, 2015.

A. Sani, “Penerapan Metode K-Means Clustering Pada Perusahaan,” J. Ilm. Teknol. Inf., no. 353, pp. 1–7, 2018.

S. Wijayanti, Azahari, and R. Andrea, “K-Means cluster analysis for students graduation (case study: STMIK widya cipta dharma),” ACM Int. Conf. Proceeding Ser., vol. Part F1296, pp. 20–23, 2017, doi: 10.1145/3108421.3108430.

N. Midha and V. Singh, “Classification of e-commerce products using reptree and k-means hybrid approach,” Adv. Intell. Syst. Comput., vol. 654, pp. 265–273, 2018, doi: 10.1007/978-981-10-6620-7_26.

V. Soundarya, “Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering,” J. Comput., vol. 12, no. 3, pp. 212–220, 2017, doi: 10.17706/jcp.12.3.212-220.

A. H. Putri, R. C. W, and M. A. Fauzi, “Implementasi Algoritma Improved K-Means Pada Portal Jurnal International,” https://www.researchgate.net/publication/324561707, no. April 2018, pp. 1–9, 2017.

N. Rahmadani and E. Kurniawan, “Implementasi Metode K-Means Clustering Tunggakan Rekening Listrik pada PT . PLN ( Persero ) Gardu Induk Kisaran,” J-SISKO TECH, vol. 3, no. 1, pp. 103–117, 2020.

yulya M. Anita, “Penentuan Tingkat Minat Belanja Online Melalui Media Sosial Menggunakan Metode Clustering K-Means,” Rang Tek. J. http//joernal.umsb.ac.id/index.php/RANGTEKNIKJOURNAL, vol. 2, no. 2, pp. 6–11, 2018.

L. R. Ananda, “Clustering Untuk Menentukan Calon Mahasiswa Berprestasi,” Jiti, vol. 1, no. 2, pp. 16–19, 2018.

C. Groß and D. Vriens, “The Role of the Distributor Network in the Persistence of Legal and Ethical Problems of Multi-level Marketing Companies,” J. Bus. Ethics, vol. 156, no. 2, pp. 333–355, 2019, doi: 10.1007/s10551-017-3556-9.




DOI: https://doi.org/10.33330/icossit.v1i1.824

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