No abstract

Group work is an essential part of the learning process, especially for students at school. Group work is one part of the learning method that helps teachers involve students and provides opportunities for students to learn cooperatively. This study aims to determine students' perceptions of the implementation of group work in English class. This study used an instrument in the form of a survey involving 125 participants consisting of five classes from class xi of SMAN 2 Malang. The author uses five variables as a reference to what problems often occur when grouping and the leak of students’ motivation when studying. The author uses a descriptive technique by describing what problems usually happen to analyze the data obtained. The findings from this study indicate that students working in groups can improve their responsibility and share their ideas, time, and task, resulting in an increased chance of completing tasks quickly and achieving better scores.

Abstract. cc This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward backpropagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn't identical pattern and different from weekday's pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constanta value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.