Vol. 5 No. 1 (2024) Articles
Open Access

Implementasi Algoritma Naïve Bayes Terhadap Data Penjualan untuk Mengetahui Pola Pembelian Konsumen pada Kantin

Raihan Putra Mohammad Rosidi
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Kiki Setiawan
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
Published: January 10, 2024 Pages: 120-126
Original Full-Text Article
Download published version for reading and archiving
Abstract

Implementation of the Naïve Bayes algorithm on sales data to determine consumer buying patterns in canteens is an approach that seeks to overcome the limited understanding of consumer behavior in canteens. The goal of this approach is to analyze sales data and identify purchasing patterns to gain insight into consumer behavior, optimize sales strategies, and improve customer satisfaction. By applying the Naïve Bayes algorithm to sales data, this research seeks to identify factors that influence consumer behavior, such as price, convenience, and menu offerings, and provide insights that can be used to optimize menu offerings, pricing strategies and resource allocation. Additionally, the approach seeks to increase customer satisfaction and differentiate canteens from competitors by personalizing menu offerings and enhancing the overall customer experience. This research also aims to contribute to the field of consumer behavior research by applying the Naïve Bayes algorithm to canteen sales data, potentially providing insights that can be applied in other contexts. Overall, the application of the Naïve Bayes algorithm to canteen sales data can provide a data-driven approach to understanding consumer behavior and improving sales strategy and customer satisfaction.

Article Metrics & Downloads Graph
Monthly Download Trends:
Author Biographies
Raihan Putra Mohammad Rosidi Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia

Kiki Setiawan Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia

How to Cite
Rosidi, R. P. M., & Setiawan, K. (2024). Implementasi Algoritma Naïve Bayes Terhadap Data Penjualan untuk Mengetahui Pola Pembelian Konsumen pada Kantin. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(1), 120-126. https://doi.org/10.35870/jimik.v5i1.407
License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .

  • Share: You are free to copy, distribute, and transmit the work in any medium or format.
  • Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
  • Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Copyright & Retention: Authors retain copyright without restrictions and grant this journal the right of first publication under an open-access model. The journal retains non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

References
Total: 11 References
  1. Aksoy, G., Ataş, P., & Karabatak, M. (2019, November). Investigation of shopping habits using data mining classification algorithms. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/UBMYK48245.2019.8965647.
  2. Nurdiawan, O., & Salim, N. (2018). Penerapan Data Mining Pada Penjualan Barang Menggunakan Metode Metode Naive Bayes Classifier Untuk Optimasi Strategi Pemasaran. Jurnal Teknologi Informasi dan Komunikasi, 13(1), 84-95.
  3. Jiaxian, Y., & Gengming, Z. (2019, March). Algorithm based on improved naive Bayesian for predicting microblog behavior. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence (pp. 182-187). DOI: https://doi.org/10.1145/3319921.3319956.
  4. ROZAQ, A. J. (2021). IMPLEMENTASI METODE KLASIFIKASI ALGORITMA NAIVE BAYES DALAM MENGANALISIS TRANSAKSI PENJUALAN PADA 212 MART KUTO PALEMBANG.
  5. Syamsudin, D., Halundaka, Y. C. D., & Nugroho, A. (2020). Prediksi Status Konsumen Produk Celana Menggunakan Naïve Bayes. JOINTECS (Journal of Information Technology and Computer Science), 5(3), 177-184. DOI: https://doi.org/10.31328/jointecs.v5i3.1435.
  1. Yulianto T. (2019). Prediksi Penjualan Produk Menggunakan Algoritma Naïve Bayes (Studi kasus Couple Store Yogyakarta). Skripsi. Fakultas Teknologi Informasi dan Elektro Universitas Teknologi Yogyakarta.
  2. Sari, R M. (2022). Implementasi Data Mining Untuk Memprediksi Penjualan menggunakan Metode Naïve Bayes. Skripsi. Fakultas Sains dan Teknologi Universitas Pembangunan Panca Budi.
  3. Nawangsih, I., & Setyaningsih, A. (2020). THE APPLICATION OF THE NAÏVE BAYES ALGORITHM TO DETERMINE THE CLASSICATION OF BEST SELLING PRODUCTS ON PULSES SALES. Incomtech, 9(1), 39-45.
  4. Ariska, P., Hasibuan, N. A., & Purba, B. (2020). Penerapan Algoritma Naïve Bayes Untuk Perhitungan Nilai Point Of Sales (Pos) Dari Penjualan Produk Fashion (Studi Kasus: CV. Sumber Makmur). KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 4(1). DOI: http://dx.doi.org/10.30865/komik.v4i1.2717
  5. Juwita, J., Safii, M., & Damanik, B. E. (2022). Naïve Bayes Algorithm For Predicting Sales at the Pematang Siantar VJCakes Store. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(4), 337-346.
  6. Pransiska, N., Mirza, A. H., & Andri, A. (2019). PENERAPAN DATA MINING PREDIKSI PENJUALAN BARANG ELEKTRONIK TERLARIS MENGGUNAKAN ALGORITMA NAÏVE BAYES (Study Kasus: Planet Cash And Credit Cabang Muara Enim). In Bina Darma Conference on Computer Science (BDCCS). 1(6), 2157-2169).
Most read articles by the same author(s)

Other papers published by author(s) in this journal:

Penerapan IoT dengan Algoritma Fuzzy dan Mikrokontroler ESP32 dalam Monitoring Penyiraman
Implementasi Backup Koneksi Jaringan Menggunakan Metode Failover MikroTik pada PT Tiga Kawan Sertifikasi
Implementasi Data Mining Prediksi Penjualan Produk Semen Menggunakan Metode Linear Regression (Studi Kasus PT. Toyo Mortar Indonesia)
Analisis Sentimen Komentar TikTok terhadap Kebijakan Larangan Wisuda Sekolah oleh Gubernur Jawa Barat Menggunakan Algoritma Naive Bayes
Analisis Konfigurasi Tunnel IPv6, Auto Tunnel, dan ISATAP dalam Pembangunan Infrastruktur Jaringan