Natural Disaster Earthquake Prediction using Linear Regression Algorithm Comparing with K-Nearest Neighbors Algorithm

For more precise results, of earthquake predictions given by the KNN algorithm, this research employs the Linear Regression Algorithm. This research makes use of both the KNN algorithm and the linear regression method. The sample size varies for each category. 10 in each group, which is iterated and calculated using Clinical software with an alpha value of 0.05, G-power pre-test score of 80% and a CI of 95%. The earthquake prediction dataset with a size of 10112 was taken from Kaggle.com The It would seem that the new linear regression the accuracy of the KNN technique is surpassed by the method. (89.50%). which is 79.20% and it shows that the significant difference between Linear regression and KNN algorithm with the value of p=0.001 (Independent sample t-test p<0.05) in SPSS statistical analysis prediction of earthquake. The results determined that earthquake prediction the Linear Regression Algorithm has better when compared to the K-Nearest Neighbors Algorithm in prediction of natural disaster earthquake.