Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali)
Keywords:
Data Mining, Product Sales Level, Knowledge Discovery in Databases, K-Nearest Neighbor, Naïve Bayes.
Abstract
PT Kotamas Bali is a company that operates in tableware and kitchenware, where every sale is sold at various counters. Product sales are permanently printed and entered in sales reports, and there are problems such as the fact that the product is sold very much and it is difficult to see the rate of sale of most products, lots, and not lots. Then, to see the level of product sales, it is necessary to use data mining techniques with the method of Knowledge Discovery in Databases to predict the purchase rate of products using the two algorithms K-Nearest Neighbor and Naïve Bayes. The purpose of this research is so that PT Kotamas Bali can see the sales rate of each product sold so that there is no accumulation of goods and more focus on the most marketed products. These two algorithms result in different accuracy on the 90:10 data split, where the K-Nearest Neighbor algorithm successfully predicted the sales rate of the product with a 99% accuracy rate and was categorized as an excellent classification. The Naïve Bayes algorithm failed to make predictions with an accuracy of only 54% and was classified as a failure classification. ROC performance results on the K-Nearest Neighbor algorithm with an AUC value of 99% and the Naïve Bayes algorithm with an AUC of 74%. K-Nearest Neighbor managed to obtain the highest accuracy, while the Naïve Bayes algorithm failed to conduct classification.
Downloads
Download data is not yet available.
References
Alfani W.P.R., A., Rozi, F., & Sukmana, F. (2021). Prediksi Penjualan Produk Unilever Menggunakan Metode K-Nearest Neighbor. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 6(1), 155–160. https://doi.org/10.29100/jipi.v6i1.1910
Arhami, M., & Muhammad, N. (2020). Data Mining Algoritma Dan Implementasi. Penerbit Andi, 2020. https://books.google.co.id/books?id=AtcCEAAAQBAJ&lpg=PP1&hl=id&pg=PP1#v=onepage&q&f=false
Astuti, N. K. M., Utami, N. W., & Juliharta, I. G. P. K. (2022). Classification of Blood Donor Data Using C4.5 and K-Nearest Neighbor Methods (Case Study: Utd Pmi Bali Province). Jurnal Pilar Nusa Mandiri, 18(1), 9–16. https://doi.org/10.33480/pilar.v18i1.2790
Budiyanto, A., & Dwiasnati, S. (2018). The Prediction of Best-Selling Product Using Naïve Bayes Algorithm ( A Case Study at PT Putradabo Perkasa ). Ijctjournal.Org, 5(6), 68–74. http://www.ijctjournal.org/Volume5/Issue6/IJCT-V5I6P10.pdf
Fayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in. 17(3), 37–54. http://www.aaai.org/
Kotamas Makmur - Houseware & Lifestyle Products. (2022). Diakses dari https://ptkotamas.com/
Muqorobin, M., Kusrini, K., Rokhmah, S., & Muslihah, I. (2020). Estimation System for Late Payment of School Tuition Fees. International Journal of Computer and Information System (IJCIS), 1(1), 1–6. https://doi.org/10.29040/ijcis.v1i1.5
Rahmad, F. Suryanto, Y. Ramli, K. (2020). Performance Comparison of Anti-Spam Technology Using Confusion Matrix Classification Performance Comparison of Anti-Spam Technology Using Confusion Matrix Classification. IOP Conference Series: Materials Science and Engineering, 1–11. https://doi.org/10.1088/1757-899X/879/1/012076
Romadhon, M. R., & Kurniawan, F. (2021). A Comparison of Naive Bayes Methods, Logistic Regression, and KNN for Predicting Healing of Covid-19 Patients in Indonesia. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, 41–44. https://doi.org/10.1109/EIConCIT50028.2021.9431845
Saputra, I., & Ajeng Kristiyanti, D. (2022). Machine Learning Untuk Pemula. Informatika, Bandung, Indonesia.
Soepriyanto, B. (2021). Comparative Analysis of K-NN and Naïve Bayes Methods to Predict Stock Prices. International Journal of Computer and Information System (IJCIS) Peer Reviewed-International Journal, 02(02), 2745–9659. https://ijcis.net/index.php/ijcis/index
Suyanto. (2017). Data Mining Untuk Klasifikasi Dan Klasterisasi Data. Informatika, Bandung, Indonesia. https://suyanto.staff.telkomuniversity.ac.id/textbook-data-mining/
Swamynathan, M. (2019). Mastering Machine Learning With Python In Six Steps. Bangalore, Karnataka, India. https://books.google.co.id/books?id=dEqzDwAAQBAJ&lpg=PR3&dq=python programming concept and implementation&lr&hl=id&pg=PA3#v=onepage&q=python programming concept and implementation&f=false
Tarmizi. (2021). Marketing Strategy to Increase Sales Volume. 2(2), 322–328. https://doi.org/10.51612/teunuleh.v2i2.49
Yulianto, T. (2019). Prediksi Penjualan Produk Menggunakan Algoritma Naive Bayes. Journal Teknologi Yogyakarta, 1(2), 3–10. http://eprints.uty.ac.id
Published
2023-09-01
How to Cite
Miha Djami, A., Utami, N., & Paramitha, A. A. I. (2023). The Prediction Of Product Sales Level Using K-Nearest Neighbor and Naive Bayes Algorithms (Case Study : PT Kotamas Bali). Jurnal Pilar Nusa Mandiri, 19(2), 77-84. https://doi.org/10.33480/pilar.v19i2.4420
Section
Articles
Copyright (c) 2023 Aris Setiawan Miha Djami, Nengah Widya Utami, A. A. Istri Ita Paramitha
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is 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 the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her 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 the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
