Analysis of Batrsiyia Product Sales Prediction Using Linear Regression Method

Firzi Cahya Priana; Muhtajuddin Danny; Edora Edora

doi:10.47709/cnahpc.v7i2.5776

Authors

Firzi Cahya Priana Informatics Engineering Study Program, Faculty of Engineering, Pelita Bangsa University, Indonesia
Muhtajuddin Danny Informatics Engineering Study Program, Faculty of Engineering, Pelita Bangsa University, Indonesia
Edora Informatics Engineering Study Program, Faculty of Engineering, Pelita Bangsa University, Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v7i2.5776

Keywords:

Products, Batrsiyia Products, Product Sales, Product Sales Prediction, Data Mining, Linear Regression, Rapid Miner

Abstract

The rapidly growing herbal and health industry encourages the need for accurate sales planning to avoid the risk of shortages or excess stock. This research aims to predict sales of Batrsiyia products using the Linear Regression algorithm with RapidMiner tools, through analyzing historical data such as sales time, number of products sold, and unit prices to identify patterns and trends to produce accurate predictions. The results show that the Linear Regression algorithm is able to predict sales with an RMSE value of 96687030.354 +/- 0.000, and a Squared Error of 9348381838748252.000 +/- 25081062946532056.000. This approach helps companies understand sales patterns, predict future trends, and optimize stock and marketing strategies. By utilizing data mining-based prediction methods, companies can make more informed decisions in meeting customer needs, maintaining business stability, and improving operational efficiency.

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