K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores
DOI:
https://doi.org/10.46799/ijssr.v3i12.652Keywords:
K-Means, Algorithm Method, Clustering Sales, Product Data, Grocery StoresAbstract
This study aims to utilize the K-means clustering algorithm in data mining to categorize sales data at XYZ Grocery store. The research is essential for understanding sales patterns and enhancing inventory management strategies. The research methodology involves implementing the K-means clustering algorithm to generate centroid values for each cluster, thereby creating groups of products based on their sales performance. The findings of this study are expected to provide insights into sales trends at the store. While the abstract provides a general overview, specific results and contributions of this research are not detailed. Further studies could offer a more in-depth understanding of the practical applications of these findings in improving store management and inventory control.
References
Baihaqi, W. M., Indartono, K., & Banat, S. (2019). Penerapan Teknik Clustering Sebagai Strategi Pemasaran pada Penjualan Buku Di Tokopedia dan Shopee. Paradigma - Jurnal Komputer Dan Informatika, 21(2), 243–248. https://doi.org/10.31294/p.v21i2.6149
Chen, L., Shan, W., & Liu, P. (2021). Identification of concrete aggregates using K-means clustering and level set method. Structures, 34, 2069–2076. https://doi.org/10.1016/j.istruc.2021.08.048
Fauzi, A. (2017). Data Mining dengan Teknik Clustering Menggunakan Algoritma K-Means pada Data Transaksi Superstore.
Griffin, E. C., Keskin, B. B., & Allaway, A. W. (2023). Clustering retail stores for inventory transshipment. European Journal of Operational Research, 311(2), 690–707. https://doi.org/10.1016/J.EJOR.2023.06.008
Hadi, F., & Diana, Y. (2020). PENGKLUSTERAN PENJUALAN BAHAN BANGUNAN MENGGUNAKAN ALGORITMA K-MEANS. JOISIE (Journal Of Information Systems And Informatics Engineering), 4(1), 22. https://doi.org/10.35145/joisie.v4i1.629
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/J.INS.2022.11.139
Jabat, J. T., & Murdani, M. (2019). Penerapan Data Mining Pada Penjualan Produk Retail Menggunakan Metode Clustering. Pelita Informatika: Informasi Dan Informatika, 8(1), 26–32.
Kashef, R., & Pun, H. (2022). Predicting l-CrossSold products using connected components: A clustering-based recommendation system. Electronic Commerce Research and Applications, 53. https://doi.org/10.1016/J.ELERAP.2022.101148
Khedmati, M., & Azin, P. (2020). An online portfolio selection algorithm using clustering approaches and considering transaction costs. Expert Systems with Applications, 159. https://doi.org/10.1016/J.ESWA.2020.113546
Kuo, R. J., Rakhmat Setiawan, M., & Nguyen, T. P. Q. (2022). Sequential clustering and classification using deep learning technique and multi-objective sine-cosine algorithm. Computers and Industrial Engineering, 173. https://doi.org/10.1016/J.CIE.2022.108695
Luu, E., Xu, F., & Zheng, L. (2023). Short-selling activities in the time of COVID-19. British Accounting Review, 55(4). https://doi.org/10.1016/J.BAR.2023.101216
Meng, Q., Huang, H., Li, X., & Wang, S. (2023). Short-selling and corporate default risk: Evidence from China. International Review of Economics and Finance, 87, 398–417. https://doi.org/10.1016/J.IREF.2023.04.026
Miao, Y., Li, S., Wang, L., Li, H., Qiu, R., & Zhang, M. (2023). A single plant segmentation method of maize point cloud based on Euclidean clustering and K-means clustering. Computers and Electronics in Agriculture, 210. https://doi.org/10.1016/j.compag.2023.107951
Mudzakkir, B. D. (2018). Pengelompokan Data Penjualan Produk Pada Pt Advanta Seeds Indonesia Menggunakan Metode K-Means. JATI (Jurnal Mahasiswa Teknik Informatika), 2(2), 34–40.
Nasution, Y. R., & Eka, M. (2018). Penerapan Algoritma K-Means Clustering Pada Aplikasi Menentukan Berat Badan Ideal. ALGORITMA: Jurnal Ilmu Komputer Dan Informatika, 2(1).
Niu, G., Ji, Y., Zhang, Z., Wang, W., Chen, J., & Yu, P. (2021). Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method. Energy Reports, 7, 250–256. https://doi.org/10.1016/j.egyr.2021.08.049
Pelekis, S., Pipergias, A., Karakolis, E., Mouzakitis, S., Santori, F., Ghoreishi, M., & Askounis, D. (2023). Targeted demand response for flexible energy communities using clustering techniques. Sustainable Energy, Grids and Networks, 36. https://doi.org/10.1016/J.SEGAN.2023.101134
Purba, W., Tamba, S., & Saragih, J. (2018). The effect of mining data k-means clustering toward students profile model drop out potential. Journal of Physics: Conference Series, 1007, 012049. https://doi.org/10.1088/1742-6596/1007/1/012049
Sinan, M., Leng, J., Shah, K., & Abdeljawad, T. (2023). Advances in numerical simulation with a clustering method based on K–means algorithm and Adams Bashforth scheme for fractional order laser chaotic system. Alexandria Engineering Journal, 75, 165–179. https://doi.org/10.1016/j.aej.2023.05.080
van der Borgh, M., Nijssen, E. J., & Schepers, J. J. L. (2023). Unleash the power of the installed base: Identifying cross-selling opportunities from solution offerings. Industrial Marketing Management, 108, 122–133. https://doi.org/10.1016/J.INDMARMAN.2022.11.010
Vásquez Sáenz, J., Quiroga, F. M., & Bariviera, A. F. (2023). Data vs. information: Using clustering techniques to enhance stock returns forecasting. International Review of Financial Analysis, 88. https://doi.org/10.1016/J.IRFA.2023.102657
Wang, X., Shao, Z., Shen, Y., & He, Y. (2023). Research on fast marking method for indicator diagram of pumping well based on K-means clustering. Heliyon, 9(10). https://doi.org/10.1016/j.heliyon.2023.e20468
Windarto, A. P. (2017). Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method. International Journal of Artificial Intelligence Research, 1(2), 26. https://doi.org/10.29099/ijair.v1i2.17
Zhou, Q., & Sun, B. (2023). Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem. Data and Information Management, 100064. https://doi.org/10.1016/j.dim.2023.100064
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