Abstract

The abundance of plastic waste from liquid products such as dish soap, shampoo, mineral water, etc., has been a big problem so far. Therefore, a liquid refill station for liquid products has been designed to reduce plastic waste, and in this research, we used dish soap as liquid product. The tool works equipped with two pumps and a water flow sensor. So that customers can choose the volume and type of dish soap they want to buy from the two available brands. All purchase data in the form of customer ID, the brand of dish soap purchased, the volume of dish soap bought, and the date of the purchase were saved in the database. The residual volume in the tank is monitored and displayed on the Grafana dashboard. The timeout of dish soap volume in the tank is predicted using linear regression with the independent variable in the form of time (days) of purchase and the dependent variable in the form of the remaining dish soap in the tank. The prediction uses training data taken from the remaining volume data in the tank when it is first filled until it is first used up. The training data found that the coefficient of determination R2= 0.99 for the two brands.

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