Penerapan Teknik Data Mining dengan Algoritma Regresi Linier Berganda Untuk Estimasi Tingkat Penjualan Cafe
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Rama Prameswara Ritonga
*
Politeknik Negeri Medan, Medan,
Indonesia
(*) Corresponding Author
Abstract
This study aims to apply data mining techniques using multiple linear regression methods to estimate sales levels. Efficient sales are a key factor in the success of a cafe business; therefore, this approach is expected to provide accurate predictions to assist management in strategic decision-making. The main problem faced is uncertainty in forecasting sales levels, which can lead to excess or shortages of raw material stocks, operational disruptions, and decreased profits. Therefore, this study focuses on developing a multiple linear regression model that can utilize historical sales data, environmental variables, and other related factors to produce more accurate estimates. This research method involves collecting sales data from previous periods, analyzing statistics, and applying multiple linear regression as the main tool for building a prediction model. In addition, the selection and adjustment of variables that most influence sales levels are also focused in this study. The results show that the multiple linear regression model can provide more accurate sales level predictions compared to conventional methods. This can assist in inventory planning, operational management, and marketing strategy development to improve business performance. The implementation of data mining techniques with this method makes a significant contribution to supporting the sustainability and growth of cafe businesses in an era of increasingly fierce business competition.
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