Prediksi Kenaikan Penduduk Jawa Timur Menggunakan Metode Long Short Term Memory - Irsyadi, Muhammad Rohman; Rozi, Atiqur; Nicholas, Sandy; Sari, Anggraini Puspita (Jurnal Nasional Teknologi Informasi dan Aplikasnya; Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024, 2024)
This research aims to develop a prediction model for population increase in East Java using the Long Short Term Memory (LSTM) method. Historical population data from the previous period will be used as input to train the LSTM model. This approach is expected to produce accurate predictions about population growth in the East Java region. The LSTM method was chosen due to its ability to handle sequential data and long-term memory, which is in line with the characteristics of demographic data. This research will involve data pre-processing, LSTM model building, and model performance evaluation using relevant metrics. The results of this research are expected to contribute to a better understanding of population growth trends in East Java and provide a basis for more informed decision-making in future regional development planning and social policy.