Weather Forecasting Using Neural Networks with Backpropagation and ADAM Optimizer for city of Lhokseumawe

Arhami, Muhammad
Aulia, Annisa Rizka
Salahuddin, Salahuddin
Desiani, Anita
Yassir, Yassir

Proceeding Applied Business and Engineering Conference
Vol. 12 (2024): 12th Applied Business and Engineering Conference

Weather forecasting in Lhokseumawe is crucial due to its diverse climate and impact on community activities.It serves as an operational responsibility of the Meteorology, Climatology and Geophysics Agency (BMKG) worldwide.The method of forecasting currently employed by the BMKG involves meteorological teams observing and analyzingstatistics based on principles of mechanics and physics. Artificial Neural Networks (ANN) can be utilized to forecastlong-term weather conditions, with the backpropagation algorithm being an ANN algorithm employed for short-termweather prediction. This involves training the backpropagation architecture data, which includes an input layer with asize of 6 using Relu activation, one hidden layer with a size of 64 using Relu activation, and an output layer with a size of3 using softmax activation. We also apply the ADAM optimizer, loss sparse categorical crossentropy, and accuracymetrics. However, the backpropagation algorithm displays weaknesses, including slow convergence, overfitting, andsusceptibility to local minima, which can be addressed by utilizing the ADAM optimization algorithm. The researchutilizes Artificial Neural Network (ANN) with the backpropagation algorithm and ADAM optimization to predictweather conditions in Lhokseumawe City with high accuracy. The research methods comprise of data collection,preprocessing, division, model building, and evaluation. The study outcomes present the weather conditions as sunny,cloudy, or rainy with an algorithm accuracy of 72%.