Prediksi Penyakit Getah Bening dengan Algoritma Linear Regresi Berganda - Christofael Natalio Dondan; Evangs Mailoa (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
This study aims to evaluate the performance of the multiple linear regression algorithm in predicting lymph node diseases by utilizing a multivariate dataset. This algorithm was chosen for its ability to analyze complex relationships between independent and dependent variables, which is expected to provide accurate prediction results. The model evaluation was conducted using three key metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE), to measure prediction error levels and model reliability. The study results indicate that the multiple linear regression algorithm achieved MAE of 0.3, MSE of 0.3, and RMSE of 0.5. These values demonstrate low prediction error and acceptable accuracy, suggesting the algorithm's potential for application in assisting the diagnosis of lymph node diseases.