Predicting Waste Production Trends in Palu City Using Linear Regression Analysis
DOI:
https://doi.org/10.26877/asset.v6i3.523Keywords:
Linear Regression, Linear Regression Analysis, Waste Volume Prediction, Mean Absolute Error (MSE), Mean Absolute Percentage Error (MAPE)Abstract
The aim of this research is to obtain predicted results for the volume of waste in Palu City. In Helping the Environmental Agency One of the complex aspects of the waste problem in Palu City is the lack of waste facilities, there is no basic reference for predicting the movement of waste carried out by the Environmental Agency. If this is left unchecked then the waste in Palu City will never be completely resolved. The algorithm chosen is a linear regression algorithm which can help make good predictions, one of which can create waste volume traffic which is useful for knowing the rise and fall of waste volume in each area. in Palu City, so it is a concern. For the community, it is important to protect the environment from waste. Therefore, the Linear Regression Method is used to predict the value of the dependent variable if the independent variable has a value that is known to describe the level of waste pollution. Waste production in the Palu City area to provide information to the Palu City Environmental Service regarding waste production which continues to increase every year. Trash Trends that occur in Palu City using the previous dataset, based on Trend results showing the accumulated volume of waste in Palu City. The highest waste volume occurred in 2017 to 2021, around 350,000 (kg/person/month), the lowest volume occurred in 2021, around 200,000 (kg/person/month). shows that the analysis carried out is as good as possible. The system creation process begins with creating a flowchart, collecting waste volume data, determining an algorithm that can manage waste data, and determining an algorithm that can predict data that shows trends.
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