OPTIMIZING RAW MATERIAL INVENTORY MANAGEMENT OF MSME PRODUCT USING EXTREME GRADIENT BOOSTING (XGBOOST) REGRESSOR ALGORITHM: A SALES PREDICTION APPROACH

Muhammad Khusni Fikri; Farrikh Al Zami; Ika Novita Dewi; Abu Salam; Ifan Rizqa; Mila Sartika; Diana Aqmala

doi:10.52436/1.jutif.2024.5.2.1487

Muhammad Khusni Fikri Information System, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Farrikh Al Zami Information System, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Ika Novita Dewi Information System, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Abu Salam Information Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Ifan Rizqa Information Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Mila Sartika Faculty of Economics and Business, Universitas Dian Nuswantoro, Indonesia
Diana Aqmala Faculty of Economics and Business, Universitas Dian Nuswantoro, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.2.1487

Keywords: data science, MSMEs, predictive analysis, sales prediction, xgboost regressor

Abstract

Micro, Small and Medium Enterprises or MSMEs have a very important role for the survival of the economic sector in Indonesia. However, as the development of MSMEs, followed by a series of problems that arise. One of them is the problem of sales, business people have difficulty in determining the number of product sales in the future so that there is often an accumulation of raw materials or unsold products. This study aims to help MSMEs optimize raw material management by predicting product sales using the XGBoost Regressor Algorithm. Recently, the algorithm is very famous in the competition because of its reliability and no one has applied it to predict MSME product sales. Based on several other studies, this algorithm is accurate in predicting a value, such as predicting stock prices and the number of accidents in Bali, Indonesia. This research uses historical product sales data and weather data consisting of air temperature and relative humidity in Semarang Indonesia to train and evaluate the performance of the model. The prediction model was performed with predetermined variables and resulted in MAE 3.0752730568649156, MSE 38.25842541629838, and RMSE 6.185339555456788. In the end, it is concluded that the model built with XGBoost Regressor has a low error rate so that it can accurately predict the sales of an MSME product and optimize the management of raw materials for related products.

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