Forecasting the Population of Balikpapan City Using the Artificial Neural Network Method - Vira Oktavia; Andri Azmul Fauzi; Yuki Novia Nasution (COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi, 2026)
The continuous growth of the population requires prediction methods capable of generating accurate population estimates as a basis for development planning. This study aims to predict the population of Balikpapan City for the period 2025–2029 using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The dataset consists of annual population data of Balikpapan City from 2010 to 2024. The data were processed using the sliding window technique, followed by normalization, model training, and testing. The proposed ANN model employed a 3–4–1 architecture with a learning rate of 0,5, a sigmoid activation function in the hidden layer, a linear activation function in the output layer, and 150 training epochs. The model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The experimental results show that the proposed model produces predictions that closely match the actual population data, achieving a MAPE value of 0,57%, which indicates a high level of prediction accuracy. The trained model was subsequently used to forecast the population of Balikpapan City for the period 2025–2029. The prediction results are expected to provide useful references for local governments in formulating policies and development planning that are aligned with future population growth.