Natural resource management involves complex decision-making processes that often result in non-linear optimization problems. This study explores the application of genetic algorithms (GA) and particle swarm optimization (PSO) to manage resources like water and forest reserves more efficiently. We compare the effectiveness of these algorithms in achieving sustainable utilization while minimizing environmental impact. The results show that GA outperforms PSO in forest management scenarios, while PSO is more suitable for water resource distribution.

This research aims to explore the impact of implementing shalawat on the level of student self-confidence at SDN 16 Bansa. Shalawat is prayer and praise to the Prophet Muhammad SAW which is often practiced in the daily lives of Muslims. The research method used was an experiment with a pretest-posttest control group design, involving two groups: an experimental group that followed the application of shalawat and a control group that did not. The instrument used was a questionnaire to measure students' level of self-confidence before and after the intervention. Data were analyzed using descriptive and inferential statistical techniques. The results of the research show that the application of shalawat has a significant positive impact on increasing the level of student self-confidence at SDN 16 Bansa.