Seismic event classification can be challenging in the regions where different types of seismicity overlap in space, time, and magnitude. In this paper, I evaluate the performance of a supervised machine learning technique called Random Forest for the discrimination of icequakes and earthquakes in southeast Alaska at 15 stations surrounding the region. I train the Random Forest on about 3000 icequakes and earthquakes that occurred in the region over the last 17 years. For each event, absolute frequency spectrum values are considered as input features. The accuracies at different stations range from 75 to 95% with an average of about 90%. I conducted tests for selecting the optimum number of decision trees in the RF model and compared the results obtained by applying bandpass filters of different frequency bands on input waveforms. I further experiment by reducing the dimensions of input features by applying Principal Component Analysis (PCA), and conducted test for selecting the minimum number of components and the frequency band that gives the best results. The application of PCA resulted in slightly better results and a final model that gave the best results among all the tests was chosen. The accuracy results of the final model were further analyzed with respect to the amount of available dataset, the average distance of a station from all the glaciers, and the local geology.