Clustering of goods in the lustrorezea online shop
DOI:
https://doi.org/10.35335/cit.Vol15.2023.594.pp199-205Keywords:
Clustering, K-Means, Rapid Miner, Dataset, Online ShopAbstract
Lustrorezea is an online shop that was founded in 2018 and has 150 regular customers. This Lustrorezea online shop sells goods in the form of women's, men's, children's clothing and other items, not all items sell well, there are also items that very popular, and not very popular, therefore we need technology in the form of data mining, data mining can help business people make decisions quickly and precisely. In this research, data grouping uses the Rapid Miner application. By using the Rapid Miner application you can group data more quickly and accurately. The results of this research are the formation of 3 clusters by determining the types of goods that are very popular, best-selling and less-selling. With this data, the owner of Lustrorezea can analyze stock needs so that sales can increase further and minimize losses.
Downloads
References
J. Cha, “Exploring the internet as a unique shopping channel to sell both real and virtual items: A comparison of factors affecting purchase intention and consumer characteristics,” J. Electron. Commer. Res., vol. 12, no. 2, p. 115, 2011.
F. Naufal, Y. H. Chrisnanto, and A. K. Ningsih, “Sistem Rekomendasi Penawaran Produk Pada Online Shop Menggunakan K-Means Clustering,” Informatics Digit. Expert, vol. 4, no. 1, pp. 10–17, 2022.
G. Antoniou and L. Batten, “E-commerce: protecting purchaser privacy to enforce trust,” Electron. Commer. Res., vol. 11, pp. 421–456, 2011.
M. L. Gargano and B. G. Raggad, “Data Mining?a powerful information creating tool,” OCLC Syst. Serv. Int. Digit. Libr. Perspect., vol. 15, no. 2, pp. 81–90, 1999.
M. Van Leeuwen, “Interactive data exploration using pattern mining,” Interact. Knowl. Discov. data Min. Biomed. informatics State-of-the-art Futur. challenges, pp. 169–182, 2014.
W. M. Hochachka et al., “Data?mining discovery of pattern and process in ecological systems,” J. Wildl. Manage., vol. 71, no. 7, pp. 2427–2437, 2007.
S. Dietrich and S. Urban, Fundamentals of object databases: Object-oriented and object-relational design. Morgan & Claypool Publishers, 2010.
R. J. Petrasch and R. R. Petrasch, “Data integration and interoperability: Towards a model-driven and pattern-oriented approach,” Modelling, vol. 3, no. 1, pp. 105–126, 2022.
R. M. Al-Khatib, N. K. T. El-Omari, and M. A. Al-Betar, “Innovative cloud computing object-oriented model to unify heterogeneous data,” Int. J. Oper. Res., vol. 46, no. 3, pp. 289–322, 2023.
I. H. Sarker, “Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective,” SN Comput. Sci., vol. 2, no. 5, p. 377, 2021.
N. Normah, S. Nurajizah, and A. Salbinda, “Penerapan data mining metode k-means clustering untuk analisa penjualan pada toko fashion hijab Banten,” J. Khatulistiwa Inform., vol. 7, no. 2, pp. 158–163, 2021.
M. J. H. Mughal, “Data mining: Web data mining techniques, tools and algorithms: An overview,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 6, 2018.
M. V. Sitorus, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Konsumsi Produk Kosmetik milik PT Cedefindo,” J. Ilm. MIKA AMIK Al Muslim, vol. 5, no. 2, pp. 63–68, 2021.
A. L. Hananto, P. Assiroj, B. Priyatna, A. Fauzi, A. Y. Rahman, and S. S. Hilabi, “Analysis of drug data mining with clustering technique using K-Means algorithm,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 12024.
Sudirman, A. P. Windarto, and A. Wanto, “Data mining tools| rapidminer: K-means method on clustering of rice crops by province as efforts to stabilize food crops in Indonesia,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2018, p. 12089.
T. M. Ghazal, “Performances of K-means clustering algorithm with different distance metrics,” Intell. Autom. Soft Comput., vol. 30, no. 2, pp. 735–742, 2021.
U. Hasanah and B. Humaidi, “Analysis of data mining using k-means clustering algorithm for product grouping,” Int. J. Informatics Inf. Syst., vol. 3, no. 1, pp. 12–22, 2020.
R.-Q. Liu, Y.-C. Lee, and H.-L. Mu, “Customer classification and market basket analysis using K-means clustering and association rules: evidence from distribution big data of korean retailing company,” Knowl. Manag. Res., vol. 19, no. 4, pp. 59–76, 2018.
M. DeJonckheere and L. M. Vaughn, “Semistructured interviewing in primary care research: a balance of relationship and rigour,” Fam. Med. community Heal., vol. 7, no. 2, 2019.
M. Ciesielska, K. W. Boström, and M. Öhlander, “Observation methods,” Qual. Methodol. Organ. Stud. Vol. II Methods Possibilities, pp. 33–52, 2018.
C. Yuan and H. Yang, “Research on K-value selection method of K-means clustering algorithm,” J, vol. 2, no. 2, pp. 226–235, 2019.
K. P. Sinaga and M.-S. Yang, “Unsupervised K-means clustering algorithm,” IEEE access, vol. 8, pp. 80716–80727, 2020.
H. Ismkhan, “Ik-means?+: An iterative clustering algorithm based on an enhanced version of the k-means,” Pattern Recognit., vol. 79, pp. 402–413, 2018.
M. Hossain and S. Abufardeh, “A New Method of Calculating Squared Euclidean Distance (SED) Using pTree Technology and Its Performance Analysis.,” in CATA, 2019, pp. 45–54.
T. Behrens, K. Schmidt, R. A. Viscarra Rossel, P. Gries, T. Scholten, and R. A. MacMillan, “Spatial modelling with Euclidean distance fields and machine learning,” Eur. J. Soil Sci., vol. 69, no. 5, pp. 757–770, 2018.
G. F. Nama, H. Lukmanul, and J. Junaidi, “Implementation of K-Means Technique in Data Mining to Cluster Researchers Google Scholar Profile,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, 2019.
V. Pynam, R. R. Spanadna, and K. Srikanth, “An extensive study of data analysis tools (rapid miner, weka, r tool, knime, orange),” Int. J. Comput. Sci. Eng, vol. 5, no. 9, pp. 4–11, 2018.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Erlinda, Febri Haswan
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.