KINERJA ALGORITMA BACKPROPAGATION DAN RNN DALAM PREDIKSI BEBAN JARINGAN - Annisah, Wulan Nur; Harani, Nisa Hanum; Habibi, Roni (Jurnal Teknik Informasi dan Komputer (Tekinkom), 2024)
Bandwidth allocation optimization is crucial to ensure optimal network performance and user satisfaction. This research aims to identify the best machine learning algorithm between backpropagation and recurrent neural network (RNN) in predicting network load, using two different datasets. The main issue addressed is how to choose the right algorithm for network load prediction to optimize bandwidth allocation. The CRISP-DM methodology was used as the research framework, with four evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results showed that the backpropagation algorithm provided the best performance on the data with the lowest evaluation matrix: MAE 0.0203, MSE 0.0007, RMSE 0.0281, and MAPE 20%. In conclusion, the backpropagation algorithm is more suitable for predicting bandwidth requirements compared to RNN based on the evaluation metrics used, making it reliable for bandwidth allocation optimization.