Forecasting Air Quality Indeks Using Long Short Term Memory - Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar (Politeknik Negeri Batam, 2024)
Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values