Analisis Prediksi Tingkat Penjualan Brownies Tape Menggunakan Algoritma Naïve Bayes
Keywords:
Tape Cirebon, Algoritma Naïve Bayes, Oleh-Oleh CirebonAbstract
The problem of this covid pandemic has hit all parties, sales in this pandemic era must be observant of changes, entrepreneurs are obliged to manage finances properly so as not to experience colaps or bankruptcy. solutions made by entrepreneurs ranging from reducing the amount of production, reducing employees and or promoting massively. The criteria for this study were obtained from the journals used, namely the criteria, namely date, month, year, code, product name, price and quantity. Then this study uses primary data, which means that the data is used with brownie purchase data from tape products with sales records from 2021 in September. The method used is the naïve bayes algorithm with retrive operators, cross validation, naïve bayes, apply model and performance. The accuracy result in this study is 83.24% Prediction of Less Selling with true Less Selling as much as 2004 data. Prediction of Less Selling with true Selling as much as 350 data. In-demand predictions with less in-demand as much as 202 data. Laris prediction with true Laris as much as 737 data.
References
Eka, M., Adi, B., & Niswatin, R. (2021). Sistem Pendukung Keputusan Lokasi Penjualan Burger yang Strategis Menggunakan Metode Naive Bayes. 193–199.
Frank, Eibe, et al. (2004). The WEKA Data Mining Software: An Update. Department of Computer Science. New Zealand. University of WaikatoHamilton.
Kristanto, A. (2019). Tinggi Berdasarkan Prestasi Dengan Menggunakan Metode Iterative Dichotomizer 3 ( Id3 ) ( Studi Kasus : Fti Uksw ). Simetris, 10(2), 433–444.
Larose, D. T. (2005). Discovering Knowledge In Data: An Introduction to Data mining. JohnWilley& Sons. Inc.
Lisdiyanto, A., Winarti, & Lega Wibisono, C. (2022). Klasifikasi Tingkat Penjualan Air Minum Isi Ulang Harian Menggunakan Naive Bayes. Informatics, Electrical and Electronics Engineering (Infotron), 2(1), 33–38. http://riset.unisma.ac.id/index.php/infotron
Pambudi, H. K., Kusuma, P. G. A., Yulianti, F., & Julian, K. A. (2020). Prediksi Status Pengiriman Barang Menggunakan Metode Machine Learning. Jurnal Ilmiah Teknologi Infomasi Terapan, 6(2), 100–109. https://doi.org/10.33197/jitter.vol6.iss2.2020.396
Putri, Ucha, Sanni Irawan, E., & Rizky, F. (2021). Implementasi Data Mining Untuk Prediksi Penyakit Diabetes. KESATRIA( Jurnal Penerapan Sistem Informasi Dan Manajemen, 2(1), 39–46.
Reisandi, I., Daryana, Sri Mulyati, F., & Fauzi, M. (2021). Implementasi Clustering K-Means Terhadap Penilaian Kinerja Karyawan PT XYZ. Jurnal Sosial Dan Teknologi (SOSTECH), 1(8), 757–767.
Reza, M., & Suprayogi, -. (2017). Prediksi Jangka Waktu Pengiriman Barang Pada PT. Pos Indonesia menggunakan Backpropagation. CogITo Smart Journal, 3(1), 111. https://doi.org/10.31154/cogito.v3i1.50.111-122
Sigit Riyadi. (2017). Penerapan Metode Naive Bayes dalam Pengklasifikasi Trafik Jaringan. Smatika Jurnal, 07(1), 20–23.
Wahyuni, F. S., & Zahro, H. Z. (2022). Penerapan Teknik Data Mining untuk Menentukan Rencana Strategi Penjualan. Jupiter (Jurnal Pendidikan Teknik Elektro), 7(1), 47–54.
