Analisis Penjualan di Cabang Toko Serba Ada dengan Algoritma Machine Learning

Abstract

This research aims to analyze the factors influencing sales performance in a convenience store. The main focus of this study is to identify the factors affecting sales based on location zones and demographic characteristics. The research methodology involves collecting sales data from the convenience store over a period of time, as well as gathering data on store locations and zone demographics. Based on the collected data, various machine learning models such as multiple linear regression, Ridge Regression, Lasso, Elastic-Net, Bayesian Regression, Support Vector Machines (SVM), Gradient Boosting, and Random Forest are employed to analyze the relationship between the factors influencing sales. The scientific findings of this research provide a better understanding of the contributing factors to sales performance in the convenience store. These analysis results can guide store management in improving sales by optimizing the location and marketing strategies tailored to the local demographic characteristics.