Prediction model of revenue restaurant's business using random forest

Yakin, Erfan Ainul, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219 and Pagalay, Usman (2023) Prediction model of revenue restaurant's business using random forest. Indonesian Journal of Artificial Intelligence and Data Mining, 6 (2). pp. 252-261. ISSN 2614-6150

Preview

Text 16599.pdf - Published Version Download (683kB) | Preview

Abstract

This research was conducted to predict the level of revenue from the Soto Kwali Pak Wasis restaurant business using Machine Learning. The Random Forest method was chosen because it can predict optimal and fast results with low hardware requirements. Prediction Model results using the Random Forest method resulted in an average accuracy value of 75.4% from a combination of 4 experiments. Thus, the Random Forest method is one of the flexible algorithms and is very suitable for predicting revenue in the Soto Kwali Pak Wasis restaurant business because of its good speed, high accuracy, and requires lower costs.

Item Type:	Journal Article
Keywords:	machine learning; prediction; random forest; revenue
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080309 Software Engineering 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Ririen Kusumawati
Date Deposited:	17 Nov 2023 05:20

Downloads

Origin of downloads

Actions (login required)

View Item