PENGELOMPOKAN PENJUALAN PRODUK DENGAN MENGGUNAKAN K-MEANS CLUSTERING : STUDI KASUS ANALISIS PENJUALAN COFFEE SHOP OLEH KAGGLE.COM
Abstract
This study aims to group sales products in a coffee shop based on transaction data using the K-Means Clustering algorithm. The dataset from Kaggle.com includes the attributes product_id, transaction_qty, and unit_price. This method was chosen because of its ability to identify sales patterns in grouping products into three main clusters including high, medium, and low sales. The research process includes data collection, pre-processing, normalization, determining the optimal number of clusters, to evaluating the results using a Silhouette Score of 0.65. These results indicate that the K-Means method is effective in providing product segmentation that can be used as a basis for making business decisions, in optimizing stock and data-based marketing strategies.
References
Awalina, E. F. L., & Rahayu, W. I. (2023). Optimalisasi Strategi Pemasaran dengan Segmentasi Pelanggan Menggunakan Penerapan K-Means Clustering pada Transaksi Online Retail. Jurnal Teknologi Dan Informasi, 13(2), 122–137. https://doi.org/10.34010/jati.v13i2.10090
Azzahra, L., & Amru Yasir. (2024). Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food. Jurnal Ilmu Komputer Dan Sistem Informasi, 3(1), 1–10. https://doi.org/10.70340/jirsi.v3i1.88
Deden, 1✉, S., Shofia Hilabi, S., Huda, B., & Hananto, A. L. (2024). Website: https://j-innovative.org/index.php/Innovative Implementasi Algoritma K-Means Untuk Klasterisasi Data Stunting. INNOVATIVE: Journal Of Social Science Research, 4, 363–373. https://j-innovative.org/index.php/Innovative
Hananto, A., Pramono, E., & Huda, B. (2022). Application Of Recapitulation And Staff Performance Assessment Using Standard Working Method. Buana Information Technology and Computer Sciences (BIT and CS), 3(1), 5–10. https://doi.org/10.36805/bit-cs.v3i1.2047
Hidayat, R., & Kusniyati, H. (2022). Analisis Clustering Dalam Pengelompokan Penjualan Menggunakan Algoritma K-Means Pada Cafe 47°Coffee Clustering Analysis in Sales Grouping Using The K-Means Algorithm at Cafe 47°Coffee. 7(2), 420–434. www.jurnal.unimed.ac.id
Hilabi, S. S. (2017). Rancang Bangun Situs Responsif Di Universitas Buana Perjuangan Karawang Dengan Menggunakan Metode Perpaduan Grid System Dan Css Media Query. Techno Xplore : Jurnal Ilmu Komputer Dan Teknologi Informasi, 2(1). https://doi.org/10.36805/technoxplore.v2i1.220
Kamalludin, A., & Budianto, et al. (2023). Penerapan Algoritme Klasterisasi K-Means Kinerja Produk Dengan Analisis Recency Frequency Monetary Pada Cafe Xyz. Prosiding Seminar …, 2(April), 258–266. http://senafti.budiluhur.ac.id/index.php/senafti/article/view/631%0A
Lia Hananto, A., Assiroj, P., Priyatna, B., Nurhayati, Fauzi, A., Yuniar Rahman, A., & Shofiah Hilabi, S. (2021). Analysis of Drug Data Mining with Clustering Technique Using K-Means Algorithm. Journal of Physics: Conference Series, 1908(1). https://doi.org/10.1088/1742-6596/1908/1/012024
Nur Afidah, N. (2023). Penerapan Metode Clustering dengan Algoritma K-means untuk Pengelompokkan Data Migrasi Penduduk Tiap Kecamatan di Kabupaten Rembang. PRISMA, Prosiding Seminar Nasional Matematika, 6, 729–738. https://journal.unnes.ac.id/sju/index.php/prisma/
Prasetiani, S. D., & Rochmawati, N. (2022). Penerapan Data Mining Untuk Clustering Menu Favorit Menggunakan Algoritma K-Means (Studi Kasus Kedai Expo). Journal of Informatics and Computer Science (JINACS), 3(03), 278–286. https://doi.org/10.26740/jinacs.v3n03.p278-286
Prasetiyo, D., Lestati, W., Atina, V., Bangsa, U. D., Surakarta, K., Informasi, J. S., Ilmu, F., Universitas, K., & Bangsa, D. (2024). Penerapan Clustering Dengan K-Means Untuk Pemilihan Menu Favorit Di Tetra Coffeeshop. 11(3).
Priyatman, H., Sajid, F., & Haldivany, D. (2019). Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(1), 62. https://doi.org/10.26418/jp.v5i1.29611
Priyatna, B. (2019). Accounting Information System. 17–30.
Siahaan, A. S., Saragih, R., Simanjuntak, M., & Binjai, S. K. (2024). Penerapan Metode K-Means Clustering untuk Pengelompokan Minat Konsumen terhadap Pengguna Jasa Layanan pada Kantor Pos Binjai. 2(5).
Tri Cahaya, D., Puspita, D., & Syahri, R. (2024). Penerapan Metode K-Means Clustering Untuk Pengelompokan Potensi Padi Di Kota Pagar Alam. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 2187–2193. https://doi.org/10.36040/jati.v8i2.9432
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