Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor
-
Sri Puspita Dewi
*
STMIK Royal Kisaran, Kisaran,
Indonesia
- Nurwati Nurwati STMIK Royal Kisaran, Kisaran, Indonesia
- Elly Rahayu STMIK Royal Kisaran, Kisaran, Indonesia (*) Corresponding Author
Abstract
The implementation of Data Mining is very much needed by UD Andar because this trading business sells various types of products. This trading business sells powdered herbal medicine, plastic bags, food & beverage ingredients, and frozen foods that are in demand by consumers. Judging from the large number of consumer requests, it turns out that there are several best-selling and not-selling products, so based on the last 1 year data, a prediction of the best-selling product sales is needed, in order to make it easier for trading businesses in planning stock providers. Because the current system is still manual, the data obtained is less accurate and efficient. So to overcome this, we need a sales prediction system for best-selling products with data mining techniques using the k nearest neighbor method. This research produces a system of k nearest neighbor algorithms in data mining techniques that help to predict the sales of the best-selling products at UD Andar
Downloads
References
A. Alfani W.P.R., F. Rozi, and F. Sukmana, “Prediksi Penjualan Produk Unilever Menggunakan Metode K-Nearest Neighbor,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 1, pp. 155–160, 2021, doi: 10.29100/jipi.v6i1.1910.
I. Yolanda and H. Fahmi, “Penerapan Data Mining Untuk Prediksi Penjualan Produk Roti Terlaris Pada PT . Nippon Indosari Corpindo Tbk Menggunakan Metode K-Nearest Neighbor,” vol. 3, no. 3, pp. 9–15, 2021.
S. Rahmatullah, S. Wahyuni, M. F. Chaining, and F. C. Method, “Penerapan Data Mining Untuk Prediksi Penjualan Produk Furniture Terlaris Menggunakan Metode Knearest Neighbor,” no. 2, pp. 75–86, 2020.
Hasmawati, J. Nangi, and M. Muchtar, “Aplikasi prediksi penjualan barang menggunakan metode k- nearest neighbor (knn) (studi kasus tumaka mart),” semanTIK, vol. 3, no. Jul-Des 2017, pp. 151–160, 2019.
Y. R. Amalia, “Penerapan data mining untuk prediksi penjualan produk elektronik terlaris menggunakan metode k-nearest neighbor,” 2018.
N. Noviyanto, “Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 183–188, 2020, doi: 10.31294/p.v22i2.8808.
W. M. S. Karsito, “PREDIKSI POTENSI PENJUALAN PRODUK DELIFRANCE DENGAN METODE NAIVE BAYES DI PT. PANGAN LESTARI,” vol. 9, no. September, pp. 67–78, 2018.
Andi Saputra, Ashari Imamuddin, and Pria Sukamto, “Rancang Bangun Aplikasi Sistem Penjualan Case Study: Pt. X,” INFOTECH J. Inform. Teknol., vol. 1, no. 2, pp. 78–86, 2020, doi: 10.37373/infotech.v1i2.67.
I. Firnando, “Implementasi Algoritma Apriori Dan Forecasting,” Pros. SENTIA, vol. 7, no. 3, pp. 2085–2347, 2019.
J. Informasi and D. Komputer, “PENERAPAN DATA MINING UNTUK PREDIKSI PENJUALAN PRODUK TRIPLEK PADA PT PUNCAK MENARA HIJAU MAS,” J. Inf. dan Komput., vol. 8, 2020.
Y. Yahya and W. Puspita Hidayanti, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Efektivitas Penjualan Vape (Rokok Elektrik) pada ‘Lombok Vape On,’” Infotek J. Inform. dan Teknol., vol. 3, no. 2, pp. 104–114, 2020, doi: 10.29408/jit.v3i2.2279.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor
Pages: 639-648
Copyright (c) 2022 Sri Puspita Dewi, Nurwati Nurwati, Elly Rahayu
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).
