Analisa Penjualan Produk Kosmetik Dengan Metode Algoritma K-Means Di Toko Erremy
Abstract
Applying data mining to analyze sales patterns of goods using the k-means algorithm method at the Erremy Shop. Availability of goods, stock of goods and completeness of goods in a shop is a very important element. So that the management process to regulate the availability of inventory is needed to avoid the accumulation of the same goods and is less desirable to customers. This research aims to determine buyer interest in a product so that we can ensure the supply and availability of products that are selling well or not selling well. The benefit of this research is to prevent product stockouts and accumulation of unsold products. The method used in product grouping uses the K-Means Clustering method so that the best-selling and less-selling products can be identified. Product data is grouped based on the similarity of the data so that data with the same value will be in one cluster. With the existence of product stock clusters with each level of stock movement owned, this allows it to be used as a reference in predicting the supply of products according to their needs. The tests carried out in this study were using black box testing.
References
M. Miftakhul and S. Prihandoko, 2017,“Penerapan Algoritma K-Means dan Cure Dalam Menganalisa Pola Perubahan Belanja Dari Retail ke E-Commerce,” vol. 7, no. 2, pp. 44–49.
D. Maharani, M. D. Sena, and I. Management, “International Conference on Social, Sciences and Information Technology,” vol. 4509, pp. 1–7, 2020.
A. Wibowo and A. R. Handoko, “Metode Data Mining Klasterisasi Dengan Analisis Recency Frequency Monetary ( Rfm ) Termodifikasi Segmentation of Customers of Drug Pharmaceutical Product Retail Using Clasterization Mining Data Method Using Modified Monetary Recency Frequency ( Rfm ) Anal,” vol. 7, no. 3, pp. 573–580, 2020, doi: 10.25126/jtiik.202072925.
A. Hariyanto, 2017, Computer BasedTest Dengan PHP MysQL Dan Bootsrap. Yogyakarta: Loko Media.
C. Ramadhana, Y. D. L. W, and K. D. K. W, “Data Mining dengan Algoritma Fuzzy C-Means Clustering Dalam Kasus Penjualan di PT Sepatu Bata,” Semant. 2013, vol. 2013, no. November, pp. 54–60, 2013.
Suyanto,2017, Data Mining. Yogyakarta: Informatika.
Retno Tri vulandari, 2017, Data Mining. Yogyakarta: Gava Media.
M. P. Syamala, 2013,“Analisis Prediksi Churn Dan Segmentasi Pelanggan Speedy Retail Daerah Operasional Bandung Menggunakan Algoritma Decision Tree Dan K-Means,” pp. 32–37.
S. Agustina, D. Yhudo, H. Santoso, N. Marnasusanto, A. Tirtana dan F. Khusnu, 2012, "Clustering Kualitas Beras Berdasarkan Ciri Fisik Menggunakan Metode K-Means," Universitas Brawijaya Malang, Malang.
M. . S. Rosa A.S, 2016, Rekayasa Perangkat Lunak. Bandung: Informatika.
B. Raharjo, 2016, Pemrograman Web. Bandung: Modula.
Rosa A.S M. Shalahuddin, 2013, Rekayasa Perangkat Lunak Terstruktur dan
Berorientasi Objek. Bandung: Informatika.
Simanjuntak, E.C., at al. 2010. “Blackbox Testing”. Kompasiana.com. http://www.kompasiana.com/elisa_grace_heriberty/blackboxtesting_550051c7a333115b735107d b
R. Mythily, A. Banu, and S. Raghunathan,2014, “Clustering models for data stream mining,” Procedia Comput. Sci., vol. 46, no. Icict , pp. 619–626, 2015.
T. H. Sardar and Z. Ansari,2018, “An analysis of Map Reduce efficiency in document clustering using parallel K-means algorithm,” Futur. Comput. Informatics J., pp. 1–10, 2018.
Sulastri H, Gufroni AI. 2017,Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia. Jurnal Teknologi dan Sistem Informasi. Sep 26;3(2):299-305.
Gurunescu, F.,2011, Data Mining Consepts, Models and Techniques (Volume 12 ed.). Verlag Berlin Heidelberg.
F. Gullo, 2015,“From patterns in data to knowledge discovery: What data mining can do,” Phys. Procedia, vol. 62, pp. 18–22.
https://sis.binus.ac.id/2022/01/31/clustering-algoritma-k-mean.
Copyright (c) 2023 Ismasari Nawangsih
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
.png)
.png)
