Prediksi Jumlah Penjualan melalui Live Stream dan Affiliate di TikTok Shop dengan Machine Learning - Putri, Nabila Agustina Cahyani; Patrisiane, Viena (Jurnal Informatika dan Rekayasa Perangkat Lunak, 2024)
Sales techniques have evolved rapidly from conventional methods to online methods often referred to as e-commerce. One of the types of e-commerce that exists in Indonesia is TikTok Shop, on TikToc Shop there are many sales supporting factors. The two sales supporting factors available on TikTok Shop are live stream and affiliate but there is no research to discuss the two factors simultaneously to the volume of sales. The aim of this study is to create a model to predict sales with live stream and affiliate factors on TikTok Shop. The method used in this research is by comparing the results of RMSE (Root Mean Squared Error) formed from the application of machine learning models multiple linear regression and random forest regression. The results of the study show that the best model based on RMSE for predicting sales with the live stream factor and the affiliate on TikTok shop is a multiple linear regression algorithm with RMSE(Root mean squared error) of 39.306882.