DEVELOPMENT OF MULTIPLE LINEAR REGRESSION MODEL TO PREDICT COD CONCENTRATION BASED ON WEST TARUM CANAL SURFACE WATER QUALITY DATA

Abstract

COD level indicates the organic matter pollution in water. A predictive analysis, such as Multiple Linear Regression, could be an option to make the COD measurement more effective. Objectives:This research aims to determine the parameter that can predict COD concentration using correlation analysis and develop a Multiple Linear Regression Model for predictive analysis on COD level in the West Tarum Canal surface water. Method and results: The correlation analysis is done in Microsoft Excel using the Pearson Product Moment Correlation Analysis. The water quality dataset is inputted to the R Studio and made the MLR model. The model is validated using t-Test. The result showed that all models in all intake points are not showing good prediction results, and the predictors showed no effect on the COD level. Conclusion: The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water. Abstract[rh1] . COD level indicates the organic matter pollution in water. A predictive analysis, such as Multiple Linear Regression, could be an option to make the COD measurement more effective. Objectives[rh2] :This research aims to determine the parameter that can predict COD concentration using correlation analysis and develop a Multiple Linear Regression Model for predictive analysis on COD level in the West Tarum Canal surface water. Method and results[rh3] : The correlation analysis is done in Microsoft Excel using the Pearson Product Moment Correlation Analysis. The water quality dataset is inputted to the R Studio and made the MLR model. The model is validated using t-Test. The result showed that all models in all intake points are not showing good prediction results, and the predictors showed no effect on the COD level. Conclusion[rh4] : The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water.