Penerapan Metode Naive Bayes untuk Klasifikasi Produk Kurang Diminati Berdasarkan Data Penjualan di Toko Laris Eksis
Article Sidebar
Main Article Content
Abstract
Article Summary
This study aims to apply the Naïve Bayes algorithm to classify in-demand and less in-demand products at Toko Laris Eksis based on sales data, including attributes such as the number of product page views (view), the number of products added to the cart (cart), and the number of products sold (sales). The dataset consists of 245 products from 516 sales transactions after data cleaning. The results show that, despite the class imbalance, the Naïve Bayes algorithm achieved an accuracy of 97.26%, with 100% precision and 96.8% recall for the Less In-Demand class, and 84.6% precision and 100% recall for the In-Demand class. This model outperforms the majority baseline accuracy of 89%. These findings indicate that the Naïve Bayes method is highly effective in detecting in-demand products, even with imbalanced data. Practically, this model can support decisions related to promotions, bundling, and stock clearance in retail. Future research is recommended to use k-fold stratification for evaluation, test adaptive thresholds, and integrate the model into an interactive visual dashboard.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 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 a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement 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 acknowledgement 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.
License: All articles are licensed under 
Alam, S., & Sunardi, S. (2023, September). Analisis Prediksi Penjualan Kue menggunakan Metode Naive Bayes. In SISITI: Seminar Ilmiah Sistem Informasi dan Teknologi Informasi (Vol. 12, No. 2, pp. 54-67). https://doi.org/10.36774/sisiti.v12i2.1327.
Gaho, I., & Maslan, A. (2024). Implementasi Data Mining untuk Memprediksi Penjualan Produk Terlaris pada Petshop Menggunakan Algoritma Naive Bayes. Computer and Science Industrial Engineering (COMASIE), 11(2), 31-40. https://doi.org/10.33884/comasiejournal.v11i2.9041.
Harahap, F., Fahrozi, W., Adawiyah, R., Siregar, E. T., & Harahap, A. Y. N. (2023). Implementasi Data Mining dalam Memprediksi Produk AC Terlaris untuk Meningkatkan Penjualan Menggunakan Metode Naive Bayes. Jurnal Unitek, 16(1), 41-51.
Hasyim, A., Fatchan, M., & Hadikristanto, W. (2022). Penerapan Algoritma Naïve Bayes Dalam Memprediksi Tingkat Penjualan Mobil Tahun 2022. Jurnal Ilmiah Intech: Information Technology Journal of UMUS, 4(02), 207-215. https://doi.org/10.46772/intech.v4i02.872.
Honestya, G., Defit, S., & Nurcahyo, G. W. (2024). Penerapan Naive Bayes untuk Memilih Produk Berdasarkan Jenis Kulit di Toko Kosmetik. Jurnal KomtekInfo, 274-280. https://doi.org/10.35134/komtekinfo.v11i4.559.
Husaini, A. P., & Lisdiyanto, A. (2024). Sistem Prediksi Penjualan Produk APD Terlaris di PT A3 Karunia Sidoarjo menggunakan Metode Naive Bayes. Jurnal Teknologi Dan Sistem Informasi Bisnis, 6(2), 431-437. https://doi.org/10.47233/jteksis.v6i2.1266.
Indriyani, I., & Bahtiar, A. (2023). Implementasi data mining untuk mengklasifikasikan data penjualan pada supermarket menggunakan algoritma naïve bayes. Jurnal Manajemen Dan Bisnis Ekonomi, 1(1), 207-220.
Julianto, A., & Andayani, S. (2024). Penerapan Data Mining Untuk Klasifikasi Produk Terlaris Menggunakan Algoritma Naive Bayes Pada Bengkel Motor. Jusitik: Jurnal Sistem Dan Teknologi Informasi Komunikasi, 7(2), 50-58. https://doi.org/10.32524/jusitik.v7i2.1148.
Musfita, N., Fitriyani, N., & Baskara, Z. W. (2023). Klasifikasi Penjualan Provider Pulsa di Kecamatan Masbagik Lombok Timur Menggunakan Metode Naïve Bayes. ESTIMASI: Journal of Statistics and Its Application, 261-272.
Purnama, P. A. W., & Putra, T. A. (2024). Klasifikasi Penjualan Produk Menggunakan Algoritma Naive Bayes pada Konter HP Bayu Cell. REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, 8(1), 286-292. https://doi.org/10.33395/remik.v8i1.13207.
Purwasih, I., Setiawan, K., Sarimole, F. M., & Tundo, T. (2024). Klasifikasi Penjualan Produk Terlaris Pada Kedai Ira Dengan Menggunakan Algoritma Naï ve Bayes Dan Algoritma K-Nearest Neighbor. TEKNIKA, 18(2), 695-â. https://doi.org/10.5281/zenodo.13236656.
Vibrianti, V., Wahyudin, E., Kaslani, K., Pratama, D., & Dwilestari, G. (2024). Klasifikasi Barang Produksi Pada Tnt. Guitar Workshop Dengan Metode Naive Bayes Menggunakan Rapid Miner. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 1432-1438.
Wijaya, A. A. (2024). Klasifikasi Data Mining Dalam Menentukan Produk Vapor-Juice Terlaris Menggunakan Naïve Bayes. Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM), 4(2), 1225-1235.
Wijaya, K., Rahmanti, N., Kurnia, R., Ulyani, R., & Mufti, E. P. (2023). Implementasi Algoritma Naïve Bayes untuk Memprediksi Penjualan Lampu Pada Toko Satria. Innovative: Journal Of Social Science Research, 3(2), 9373-9387.