Prediction System on Electricity Consumption using Web-Based LSTM Algorithm - jati, Fathoni waseso; Bhaskara, Komang Jaya; Hasibuan, Faisal Candrasyah; Irawan, Budhi (CEPAT Journal of Computer Engineering: Progress, Application and Technology, 2022)
The technology development from year to year is increasing rapidly, especially in the electronics devices such as notebooks and smartphones. With the rapid development of technology, lifestyle habits have also changed. This can lead to an increase in the use of electrical energy. In addition, the negligence of electricity users in monitoring electricity usage at the place of the electricity meter also causes an increase in electrical energy. Monitoring the electricity meter in real time can limit the user from manage their electricity efficiently. This study aims to create a web-based electrical energy usage prediction system. This system can make it easier for users to manage and reduce waste of electrical energy. In the development of this system, it begins by collecting image data of remaining electricity which are processed manually into electrical energy consumption data. Then the data is pre-processed so that the data is clean and ready to use. The clean data is carried out by the process of making a Long-Short Term Memory (LSTM) model which was chosen because it can overcome Time Series and Non-Linear data types. LSTM model is designed to be able to predict the use of electrical energy. Then do the web application design as an interface on the predictive data. Based on the results of the test, the LSTM model can predict the use of electrical energy with a Loss Mean Square Error (MSE) value of 0.0071. While the results of website testing carried out with the alpha test get an accuracy of 100% and a beta test of 82.64%.