The development of COVID-19 cases in Aceh for each region based on spatio-temporal is vital information to know. Spatio-temporal mapping is carried out to knowthe distribution of cases in diversity based on regional and time conditions. The timeseries design study was used as the research design in this study. This study aims toobtain factors that influence the incidence of COVID-19 cases in Aceh using paneldata regression analysis and the GTWR model for more accurate results. There arenine variables from 23 districts/cities in Aceh Province in 2020 and 2021. Based onpartial panel data regression analysis, of the eight independent variables that arefactors for analysis, it shows that only the variable number of doctors (p < 0.000),number of Tuberculosis Cases (p < 0.000), Number of Villages with Puskesmas (p< 0.026), and Number of Poor population (p < 0.035) have a significant effect onthe increase in COVID-19 cases in Aceh. The number of Tuberculosis Cases is avery dominant variable. Then, the results of the GTWR analysis using the AdaptiveKernel Exponential weighting function show that regional and time diversity affectthe factors that cause an increase in COVID-19 cases in Aceh. These factors need tobe a concern in controlling COVID-19 cases in Aceh in the future. 

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.