Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
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