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Journal : Journal of Information Technology and Computer Engineering

Otomatisasi Pengoperasian Alat Elektronik Berdasarkan Hasil Prediksi Algoritma Long Short Term Memory Afriansyah Afriansyah; Ade Irawan
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.83-89.2020

Abstract

Excessive use of household electricity is one of the causes of the largest amount of national electricity consumption coming from households. One way to reduce the amount of household electricity consumption is to automate the operation of electronic devices. This research proposes utilizing Long Short Term Memory (LSTM) algorithm to predict the habit of operating an electronic device. The prediction is then applied to automate the operation of that by exploiting the time series data from the usage. A series of experiments are conducted to capture the data of operating a manual lamp. Then, an LSTM model is built by training the data. The experiment results show the prediction accuracy of 99,28% and Root Mean Square Error of 0,091. Furthermore, the LSTM model is used to automatically operate a lamp in a month. The electricity cost from the automation is 36,38% lower than the manual.
Prediksi Energi Listrik Kincir Angin Berdasarkan Data Kecepatan Angin Menggunakan LSTM Muhammad Qubaisy Andiyantama; Iffah Zahira; Ade Irawan
JITCE (Journal of Information Technology and Computer Engineering) Vol 5 No 01 (2021): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.1-7.2021

Abstract

Fossil energy is well known as the most energy resource consumed by humans. However, the exploitation leads to damage both in the process of taking raw materials and in the use of those. Furthermore, the amount has become decreasing nowadays. Renewable energy could solve the energy crisis. One kind of renewable energy that has been successfully used by a human is by utilizing wind turbines. However, there are still many problems in its implementation and usage. One of the problems is the unstable generated electricity that is caused by instability of the wind speed. Inappropriate plans for utilizing wind turbines in such areas with varying wind speed could harm renewable energy investment. Therefore, forecasting the wind speed is necessary to anticipate the stability and embrace optimal produced energy. This study proposes the Long Short Term Memory (LSTM) algorithm to predict the generated energy by using the wind speed dataset. Thus, wind turbines can be utilized effectively and efficiently in the right area with sufficient average wind speed.