ANALISIS IMPLEMENTASI KURIKULUM 2013 DI SMA NEGERI 3 REJANG LEBONG, BENGKULU

This study aimed at analyzing qualitatively the implementation of K13 curriculum which has long been applied in the state senior high school number 3 of Rejang Lebong. The data were garnered using observations and interviews for the sake of eliciting information to answer the proposed problem. The other technique considered important was documentation to see the curriculum-based depictions and loads in the existing documents. After the data were gathered, the selection of data was undertaken in order to be displayed in findings; the data were analyzed and discussed; and they were concluded as the ultimate answer to the formulated problem. The present study drew a conclusion that the implementation of k13 curriculum ran well although some obstacles were faced such as learning facilities. The entire school’s community seemed to be committed to carry out the mandate of this national educational system’s law such as training for teachers’ competence, doing the duties of making lesson plans in the form of RPP and syllabi, providing a monthly report concerning the implementation of learning by teachers, and carrying out evaluations alongside giving guidance for teachers’ performances.

Combining Pso Algorithm and Honey Bee Food Foraging Behavior for Solving Multimodal and Dynamic Optimization Problems

stract Swarm intelligence algorithms are taking the spotlight in the field of function optimization. In this research our attention centers on combining the Particle Swarm Optimization (PSO) algorithm with food foraging behavior of honey bees. The resulting algorithm (called HBF-PSO) and its variants are suitable for solving multimodal and dynamic optimization problems. We focus on the niching and speciation capabilities of these algorithms which allow them to locate and track multiple peaks in environments which are multimodal and dynamic in nature. The HBF-PSO algorithm performs a collective foraging for fitness in promising neighborhoods in combination with individual scouting searches in other areas. The strength of the algorithm lies in its continuous monitoring of the whole scouting and foraging process with dynamic relocation of the bees (solution/particles) if more promising regions are found. We also propose variants of the algorithm in which each bee has a different position update equation and we utilize genetic programming (GP) for continuous evolution of these position update equations. This process ensures adaptability and diversity in the swarm which leads to faster convergence and helps to avoid premature convergence. We also explore the use of opposite numbers in our algorithm and incorporate opposition based initialization, opposition based generation jumping and opposition based velocity calculation. The proposed algorithm and its variants are tested on a suite of benchmark optimization problems. In the final portion of our work we report our experiments on the training of feedforward neural networks utilizing our proposed algorithms.