PENERAPAN MODEL LONG SHORT TERM MEMORY PADA PERAMALAN PENJUALAN LAYANAN INTERNET (Studi Kasus: PT. HIPERNET INDODATA) - Pradista Aprilia Winarno, (2021)
Competition in providing internet services in Indonesia is getting tougher. Market demand that is increasing complicated and difficult to predict makes companies have to work more to satisfy customers. The application of forecasting methods for client needs can be a solution. Machine Learning-based forecasting with the Long Short Term Memory (LSTM) method can be one way to make forecasts. The output of this research is the forecasting of the price of the service product which is expected to make the company take policies to take actions that can minimize losses for the client and the company. In this study, the author will use the Long Short Term Memory (LSTM) method to predict the price of internet services at the Indodata Hypernet company using time series data. The data used is internet service sales in 2016-2018 obtained from PT. Indodata Hypernet. The results obtained in this study, in a Root Mean Square Error value of 8.7463 and Mean Absolute Percentage Error of 4.167% indicating that the LSTM model already has the right configuration and is successful in predicting service prices quite well.