Duck Meat Sales Prediction System at Cv. Berkah Farm Mandiri Based on Monthly Sales Data Using Web-Based Multiple Linear Regression Method

M Rif'an Khariri; Retno Wardhani; Ayu ismi Hanifah

doi:10.59934/jaiea.v5i1.1729

Authors

M Rif'an Khariri Universitas Islam Lamongan
Retno Wardhani Universitas Islam Lamongan
Ayu ismi Hanifah Universitas Islam Lamongan

DOI:

https://doi.org/10.59934/jaiea.v5i1.1729

Keywords:

Sales Prediction, Multiple Linear Regression, Duck Meat, Web Application

Abstract

CV. Berkah Farm Mandiri was a livestock business focused on the production and sale of duck meat. In dealing with monthly sales fluctuations, a system capable of accurately predicting sales was needed to support stock and production planning. This study aimed to develop a web-based sales prediction system using the Multiple Linear Regression method, utilizing historical sales data as the basis for analysis. The variables used in the model included price, day type (weekday/weekend), and season (dry/rainy). The data used in this research consisted of 100 sales records obtained through direct observation. The test results showed that the system could predict sales with an accuracy rate of 94.53%, based on the calculation of the Mean Absolute Percentage Error (MAPE), indicating that the method used was highly accurate. The system was implemented as a web application to allow flexible access for management. With this system, CV. Berkah Farm Mandiri could enhance decision-making efficiency regarding stock and product distribution. This research was also expected to serve as a reference for other livestock businesses in implementing data-driven predictive approaches

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References

N. A. Nugroho, A. Sanjaya, and D. W. Widodo, “Pemilihan Bibit Bebek Pedaging Menggunakan Sistem Komputerisasi Menggunakan Metode SAW,” Semin. Nas. Inov. Teknol. UN PGRI Kediri, pp. 27–32, 2022.

S. Mujilahwati and L. D. Windasari, “Implementasi Metode k-Nearest Neighbor (k-NN) untuk Memprediksi Penjualan Buah di Indonesia berbasis Website,” Semin. Nas. Teknol. Sains, vol. 3, no. 1, pp. 7–14, 2024, doi: 10.29407/stains.v3i1.4077.

H. N. Anthony Anggrawan, “Prediksi Penjualan Produk Unilever Menggunakan Metode Regresi Linear,” J. Bumigora Inf. Technol., vol. BITe, pp. 123–132, 2022.

D. F. Ghebyla Najla Ayuni, “Penerapan Metode Regresi Linear Untuk Prediksi Penjualan Properti pada PT XYZ,” J. Telemat., vol. 14, no. 2, pp. 79–86, 2020.

A. I. Akbar Muharram, Prediksi Jumlah Produksi Daging Unggas Tahun 2023-2027. JATI, 2023.

R. M. Davila Erdianita, “Sistem Prediksi Penjualan Menggunakan Metode Weighted Moving Average Dan Economic Order Quantity Pada Toko Mariah,” JIP (Jurnal Inform. Polinema, vol. 9, no. 4, pp. 363–394, 2023.

L. D. Melany Mustika Dewi, “Regresi Linier Untuk Prediksi Konsumsi Dan Produksi Daging Unggas,” J. Inf. Syst. Manag. (JOISM, vol. 4, no. 2, pp. 81–85, 2023.

R. L. Berganda, “Menggunakan Metode Regresi Linier Berganda,” pp. 1–12.

N. H. Pajriati, E. Kurniati, and D. Suhaedi, “Penerapan Metode Average Based Fuzzy Time Series Lee Untuk Peramalan Harga Emas Di PT. X,” J. Ris. Mat., vol. 1, no. 1, pp. 73–81, 2021, doi: 10.29313/jrm.v1i1.221.

P. S. I. Pratiwi, Mg. Rohman, and M. Sholihin, “Sistem Pakar Penyakit Telinga Menggunakan Metode Naïve Bayes,” Gener. J., vol. 7, no. 2, pp. 70–82, 2023, doi: 10.29407/gj.v7i2.19991.