COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method.

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An extreme climate change results in a long dry season and an extreme rainfall results the losses in various areas of life. Rainfall prediction becomes an important thing for planning in many life sectors. Many prediction methods have been proposed, such as Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). ANN has some advantages compared with the ARIMA model. Backpropagation algorithm is one of the ANN which has been successfully used in various fields. However, the performance of the backpropagation algorithm depends on the architecture and the optimization method used. The standard backpropagation algorithm optimized by gradient descent method works slowly to get a small error. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm works faster than gradient descent method. For this reason, this paper proposes the rainfall prediction using the backpropagation algorithm optimized by the BFGS algorithm. From the experiment results, it can be shown that the backpropagation algorithm optimized by the BFGS algorithm gives better result compared with the standard backpropagation algorithm for rainfall prediction. The big number of neuron hidden causes overfitting and the small number of neuron hidden make the worst accuracy. Choosing the right learning rate will produce better accuracy.

AbstrakPrakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211. Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseries Abstract Rainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is