Kasindir Village is one of the villages in Jorlang Hataran District, Kabupaten Simalungun, Sumatera Utara. Kasindir Village is a village that carries out agricultural, livestock, and trade activities. Kasindir Village has great potential for developing MSMEs in the village. The development of MSMEs is considered capable of realizing a sustainable economy and supporting local governments in the Sustainable Development Goals (SDG's) program, especially in poverty and hunger eradication programs, sustainable economic growth, and employment. Community service implementers in Kasindir Village observed that MSME actors still require understanding and knowledge on utilizing information technology to advance their businesses. This includes packaging attractive products, providing the latest innovations, and promoting on social media to expand their market. MSME strategy development is able to realize the spirit of the SDGs. In this activity, the methods used to carry out community service activities are socialization and observation methods. Some of the problems faced by creative MSMEs in Kasindir Village include limited capital, raw materials, the use of very simple production equipment, and limited marketing media. Therefore, various parties, both government and private, need to provide support through sustainable business development programs.

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.