Implementasi Data Mining Dalam Memprediksi Penjualan Parfum Terlaris Menggunakan Metode K-Nearest Neighbor

 (*)Mhd Angga Sabda Mail (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)
 Suhardi Suhardi (Universitas Islam Negeri Sumatera Utara, Medan, Indonesia)

(*) Corresponding Author

Submitted: December 19, 2023; Published: December 29, 2023

Abstract

Ahlinyaparfum is a perfume shop that provides various kinds of fragrance oils. To run his business, Ahlinyaparfum must send perfume variants to his shop from the place where the perfume is made, which requires shipping costs. Often, the perfume variants offered by Ahlinyaparfum do not match the wishes of customers, which has a negative impact on the number of store sales. The best-selling prediction process is needed based on previous sales data to help stores know which perfumes are most popular with customers and the level of best-selling in the future. By applying data mining using the K-Nearest Neighbor method, this research aims to overcome this problem. This method was tested using perfume sales data from January to June 2023 with a total of 215 data. To test and ensure its performance with the help of the Jupyter Notebook application with Python. The process of predicting perfume sales for the next month uses the parameter k = 3 with Euclidean distance calculations. The best-selling result predicted for the 7th month is Aigner Blue Emotion with total sales of 153 ml. Based on the evaluation algorithm, the overall average value of MSE is 0.52, which shows that the results are very good in determining next month's perfume sales. This is due to the fact that the calculation results are closer to the actual value the lower the MSE value.

Keywords


Sales Predictions; Perfume; Data Mining; K-Nearest Neighbor; MSE

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