Sistem Prediksi Kebutuhan Bandwidth dengan Machine Learning Metode Long Short Term Memory (LSTM)

Abstract

Internet has become an essential need in various aspects of life, especially in public environments such as Malang Creative Centre (MCC). However, bandwidth management that is still done manually at MCC causes inefficient allocation and has an impact on service quality. Therefore, this research aims to design a bandwidth usage prediction system using the Long Short Term Memory (LSTM) method to optimise bandwidth management at MCC. The LSTM method was chosen because of its ability to learn past data patterns and provide accurate predictions. This research uses 80% training data and 20% test data, with the optimal LSTM model configuration consisting of 80 layers and 100 epochs, and optimisation using Adam. The evaluation results show a prediction accuracy rate of 82.39%, which indicates that this model is reliable in predicting bandwidth usage on various floors and times that have been determined. In conclusion, this LSTM-based prediction system offers a solution for more precise and efficient bandwidth management at MCC. This not only saves network operational costs but also improves the quality of internet services for users, supporting creative economic activities at MCC by providing adequate and efficient internet connections.