Prediction Model of Revenue Restaurants Business Using Random Forest

Erfan Ainul Yakin, Ririen Kusumawati, Usman Pagalay


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.


Machine Learning;Prediction;Random Forest;Revenue

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