@thesis{thesis,
		author={Hanifa  Yaasmin and Haris  Abdul and Siregar  Riki Ruli A.},
		title ={PENGEMBANGAN TEKNOLOGI SMART IRIGASI DENGAN INTERNET OF THINGS UNTUK MENDUKUNG PERTANIAN 4.0 UNTUK LAHAN TERTUTUP},
		year={2022},
		url={http://156.67.221.169/5897/},
		abstract={With the development of technology today the level of soil moisture can be controlled by watering which is carried out automatically or often called the Internet of Things (IoT). Deep learning is known as the latest learning algorithm that has reliable performance and is able to build optimal models in carrying out data processing. The LSTM method was chosen to perform predictions on soil temperature and moisture data. In this study, what will be tested is the prediction of results from temperature and humidity data by looking at the rmse accuracy level with a ratio of 70% of train data and 30% of test data by conducting trials including hidden neurons, epochs, and batch sizes. The best parameters were hidden neurons of 5, epochs of 50 and 75, and batch size of 1. The results obtained from the hidden neuron parameters were valued at 0.23 for the RMSE data train score results and 0.09 for the RMSE data test score results, the parameter results from epochs worth 0.23 train score data and 0.09 test score data, the results of the parameters from the batch size worth 0.22 train score data and 0.09 test score data. Because it only has 100 data with a small value, predictions using LSTM have good accuracy.}
		}