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All Journal International Journal of Advances in Intelligent Informatics
Erica Reva Akilah
Universitas Budi Luhur
Computer Science & IT

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Multi-sensor weather data prediction-based artificial neural network algorithm Indra Riyanto; Erica Reva Akilah; Akhmad Musafa; Eka Purwa Laksana; Nazori Agani
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.1841

Abstract

Accurate weather prediction is essential for supporting various human activities and mitigating the impacts of changing atmospheric conditions. Recent advances in artificial intelligence have enabled the development of data-driven forecasting models capable of capturing complex relationships among meteorological variables. This study proposes an Artificial Neural Network (ANN)-based weather prediction model using multi-sensor weather data, including temperature, humidity, precipitation, solar irradiance, and wind velocity. The proposed ANN architecture consists of an input layer, two hidden layers, and an output layer. Model performance was evaluated using three training–testing data splits (90/10, 80/20, and 70/30) with 100 and 150 training epochs. Prediction performance was assessed using accuracy and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed model achieves the best performance with a 70/30 training–testing split and 150 training epochs, providing the highest prediction accuracy and the lowest RMSE among the evaluated configurations. These findings indicate that a relatively simple ANN architecture can effectively model multi-sensor weather data and provide reliable weather predictions.
Co-Authors Indra Riyanto Laksana, Eka Purwa Musafa, Akhmad Nazori AZ