Smart Kitchen Monitoring System Based on Human Health Index Using Internet of Things and Decision Tree

The health of employees is the main factor that needs to be considered by the company. Especially, employees work in the kitchen because they have a high risk of health problems. Kitchen condition is influenced by many factors, including temperature, humidity, and air quality. It has an impact on the human health index at work. Health efforts can monitor the kitchen by regulating air circulation using ventilation and air conditioning is a solution to control temperature, humidity, and air quality in the kitchen. Therefore, in the current research, We propose monitoring kitchen conditions in real-time using the internet of things and decision tree algorithms. The decision tree used to model the problem consists of decisions that lead to solutions. Each node is in a decision state while the leaf is a solution for making decisions accurately based on real-time conditions. Decision tree algorithms ID3, C4.5, and C5.0 compared to classification algorithms can predict kitchen conditions. The results of the evaluation of the decision tree algorithm will be implemented into the system in the form of rule sets. The testing process is carried out to determine the performance of the algorithm by using parameters accuracy, precision, and recall. The test results show that the algorithm C4.5 can predict condition classification using gain ratio and split info with the highest accuracy of 99.35% precision of 96.55%, and recall of 96.55%.