Clustering Data Penjualan Toko XYZ Menggunakan Metode K-Means
Abstract
Sales, A Process Involving Sellers Offering Goods Or Services To Buyers With The Objective Of Profiting From The Transaction, Is A Pivotal Activity In Business. Xyz Store, A Micro, Small, And Medium Enterprise (Msme) Specializing In Children’s Clothing Sales, Has Been Operational Since 2009. Transactions Can Be Conducted Online, Enabling Buyers To Shop Without The Need To Visit The Physical Store. However, Xyz Store Faces A Challenge In Stock Management, Specifically The Mismatch Between Demand And Product Availability, Leading To An Accumulation Of Less Popular Items. By Understanding Sales Trends, Xyz Store Can Optimize Their Stock Management, Either By Curtailing The Purchase Of Stocks For Less Sold Items Or By Substituting Less Popular Items With New Ones That May Be More Appealing To Potential Buyers. Through The Evaluation Stage In The Kdd Method, It Was Determined That The Optimal Number Of Clusters In This Study Is Three, With An Evaluation Result Of 0.5063460425226173. These Three Clusters Were Identified As Less Popular Items, Moderately Popular Items, And Very Popular Items. Cluster 1, Deemed Less Popular, Comprises 84 Items. Cluster 2, Which Is Moderately Popular, Includes 12 Items. Meanwhile, Cluster 3, Identified As Very Popular, Contains Only 4 Items. This Study Provides Valuable Insights Into Sales Strategies And Stock Management At Xyz Store For Enhancing Efficiency And Sales.
References
Arhami, M., & Nasir, M. (2020). Data Mining: Algoritma Dan Implementasi. In R. I. Utami (Ed.), Data Mining Algoritma Dan Implementasi (1st Ed.). Penerbit Andi.
Indra, I., Pratiwi, W. A. A., & Putra, Y. D. (2022). Pengaruh Biaya Promosi Terhadap Penjualan. Jurnal Ekonomi, Manajemen Dan Akuntansi, 24(4), Hal 711-716. Https://Doi.Org/10.30872/Jfor.V24i4.11704
Indriyani, F., & Irfiani, E. (2019). Clustering Data Penjualan Pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means. Juita : Jurnal Informatika, 7(2). Https://Doi.Org/10.30595/Juita.V7i2.5529
Nagari, S. S., & Inayati, L. (2020). Implementation Of Clustering Using K-Means Method To Determine Nutritional Status. Jurnal Biometrika Dan Kependudukan, 9(1). Https://Doi.Org/10.20473/Jbk.V9i1.2020.62-68
Paembonan, S., & Abduh, H. (2021). Penerapan Metode Silhouette Coefficient Untuk Evaluasi Clustering Obat. Pena Teknik: Jurnal Ilmiah Ilmu-Ilmu Teknik, 6(2). Https://Doi.Org/10.51557/Pt_Jiit.V6i2.659
Suyanto, D., & Others. (2017). Data Mining Untuk Klasifikasi Dan Clusterisasi Data. In Bandung: Informatika Bandung (Pertama). Informatika Bandung.
Yahya, S., & Sugiyanto, C. (2020). Indonesian Demand For Online Shopping: Revisited. Journal Of Indonesian Economy And Business, 35(3), 188–203. Https://Doi.Org/10.22146/Jieb.55358
Yunistya, D. I., Goejantoro, R., Deny, F., & Amijaya, T. (2022). The Application Of K-Harmonic Means Method In District/City Grouping (Case Study: Poverty In Kalimantan Island In 2020) Penerapan Metode K-Harmonic Means Dalam Pengelompokan Kabupaten/Kota (Studi Kasus: Kemiskinan Di Pulau Kalimantan Tahun 2020). 19(1), 51–64. Https://Doi.Org/10.20956/J.V19i1.21116
Zhao, H. (2022). Design And Implementation Of An Improved K-Means Clustering Algorithm. Mobile Information Systems, 2022, 6041484. Https://Doi.Org/10.1155/2022/6041484
Zubair, M., Iqbal, M. A., Shil, A., Chowdhury, M. J. M., Moni, M. A., & Sarker, I. H. (2022). An Improved K-Means Clustering Algorithm Towards An Efficient Data-Driven Modeling. Annals Of Data Science. Https://Doi.Org/10.1007/S40745-022-00428-2
