<![CDATA[Extreme weather in coastal areas such as Kota Padang is influenced by complex topography and local atmospheric dynamics, making daily rainfall prediction a critical challenge for disaster mitigation and sectoral planning. This study aims to develop a daily rainfall prediction model using a Deep Neural Network (DNN) and evaluate its performance. The dataset consists of daily temperature, humidity, and air pressure data collected from the Automatic Weather Station (AWS) of BMKG Maritime Teluk Bayur from January 2020 to December 2024, comprising 1,823 samples after preprocessing. The research methodology follows the CRISP-DM framework, which includes six main phases. The proposed DNN architecture contains three hidden layers (128, 64, and 32 neurons) with ReLU activation and dropout regularization, enhanced by lag and rolling average features to capture temporal patterns. The model achieved an MAE of 1.79, RMSE of 17.31, and R² of 0.8972, indicating strong predictive performance. The model was deployed through a Streamlit-based interactive dashboard]]>
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