Sandy Hardian Susanto Herho , Iwan Pramesti Anwar, Faruq Khadami, Mutiara Rachmat Putri

This study presents a comprehensive statistical analysis of chlorophyll-a dynamics in Balikpapan Bay, Indonesia, combining time series analysis, extreme value modeling, and machine learning techniques to understand phytoplankton variability near Indonesia's planned new capital city. Analysis of daily chlorophyll-a concentrations (2019-2021) revealed a non-Gaussian distribution (skewness = 2.212, kurtosis = 10.160) ranging from 0.350 to 11.270 mg/m\textsuperscript{3}, with distinct seasonal patterns showing May maxima (2.110 mg/m\textsuperscript{3}) and July minima (1.340 mg/m\textsuperscript{3}). Block Maxima extreme value analysis identified 79 extreme events and fitted a Gumbel distribution (location $\mu$ = 2.924, scale $\sigma$ = 1.231, log-likelihood = -141.644), though notably failed to capture two major HAB events (11.270 and 10.430 mg/m\textsuperscript{3}). A WeightedEnsemble\_L2 model combining ExtraTreesMSE (0.462), CatBoost (0.346), and LightGBMXT (0.192) identified temperature (importance: 0.072, p $<$ 0.001), solar radiation (0.061, p = 0.002), and phosphate (0.047, p $<$ 0.001) as key drivers, achieving moderate performance (RMSE = 0.868 mg/m\textsuperscript{3}, R\textsuperscript{2} = 0.204). The trained model was serialized for potential operational deployment, providing crucial baseline data for HAB monitoring systems in this rapidly developing coastal region.