EFFECTIVENESS OF WEB BASED E-LEARNING MEDIA ON INFORMATION AND COMMUNICATION TECHNOLOGY SUBJECT FOR IMPROVE ACHIEVEMENT STUDENTS OF GRADE X SMA NEGERI 1 KALASAN

This research purposed to know the comparative effectiveness of the use of web based E-Learning media in Information Technology and Communication subjects to improve learning achievement students of grade X SMA Negeri 1 Kalasan when compared with conventional learning in place at the school. The research method is quasi-experimental research method with two class sample XA and XB in SMA Negeri 1 Kalasan. The research method is used in this research was quasi-experimental research methods with a sample of research is the class XA and XB SMA Negeri 1 Kalasan. The research design was pretest-posttest control group design. The sampling technique is used was random sampling. Different test done to measure the difference in the effectiveness of web based E-Learning media and conventional learning media based on learning achievement of using Independent Sample t-test. Based on these results it can be concluded that the effectiveness of web based E-Learning media have an index of normalized gain is 0,54; effectiveness of conventional learning media have an index of normalized gain is 0,30 and improved learning achievement with web based E-Learning media is better than the improved learning achievement of conventional learning media while they in the same criteria. The criteria is in sufficient criteria. But if based on calculation of the gain, improvement learning achievement with based on the t test of the average value of learning achievement acquired thitung > ttabel (2,870> 1,672), as well as the significant value of P (0006) <α (0.05), thus Ho rejected and Ha accepted. It is proved that web based E-Learning media effectively to improve student learning achievement in the material Presentation Maker Software Class X SMA Negeri 1 Kalasan. Keywords: media, E-Learning, conventional learning, learning achievemen

Statistical Mechanics of Time Domain Ensemble Learning

Conventional ensemble learning combines students in the space domain. On the other hand, in this paper we combine students in the time domain and call it time domain ensemble learning. In this paper, we analyze the generalization performance of time domain ensemble learning in the framework of online learning using a statistical mechanical method. We treat a model in which both the teacher and the student are linear perceptrons with noises. Time domain ensemble learning is twice as effective as conventional space domain ensemble learning.Comment: 10 pages, 10 figure

Enhancement Of Mathematical Reasoning Ability At Senior High School By The Application Of Learning With Open Ended Approach

The objective of this research is to investigate the differences of students’ enhancement of mathematical reasoning ability as the result of the application of learning with open ended approach and conventional learning. The population in this research was the entire students in high schools and Aliyah in Bandung. The sample is students on grade X. Two classes are randomly selected from each school, one class as an experiment class (open-ended approach) and another class as a control class (conventional learning). The instruments used include mathematical prior knowledge test, mathematical reasoning test, and guidelines for observation. The results of data analysis show that if it is viewed as a whole, students’ enhancement of mathematical reasoning who had treated with instruction using open-ended approach was better than students who had treated with regular instruction. There is interaction between learning approach and school levels towards students’ enhancement of mathematical reasoning. There is no interaction between learning approach and the initial of mathematical ability towards students’ enhancement of mathematical reasoning. Keywords: Open Ended Approach, Conventional, and Mathematical Reasonin

The Enhancement of Mathematical Communication and Self Regulated Learning of Senior High School Students Through PQ4R Strategy Accompanied by Refutation Text Reading

This study is experiment research with control group pretest-posttest design and aimed to examine the influence of PQ4R strategy and Refutation Text, school level, and student’s mathematical early knowledge toward achievement and enhancement of student’s mathematical communication ability and Self Regulated Learning. Subject of study as much as 241 students of class X from three Public Senior High School from high, medium, and low school level. Research instrument consist of one set of student’s mathematical communication, and one set of student’s Self Regulated Learning scale. Data analysis use Kosmogorov-Smirnov Test (Test-Z), Level Test, Test-t, one-way and two-way ANOVA, Post Hoc Test (Scheffe) and also Chi-Square Test. Study found that learning with PR4R strategy accompanied by Refutation Text Reading give consistent influence compared with conventional learning as viewed as a whole, based on school level and also mathematical early knowledge. In addition, study also found: (1) there is no interaction between learning (PQ4R) accompanied by Refutation Text reading and conventional and school level toward (a) student’s mathematical communication and (b) student’s Self Regulated Learning; (2) there is no significant interaction between learning and student’s mathematical early knowledge toward (a) student’s mathematical communication ability and (b) student’s Self Regulated Learning; and (3) there is association between student’s mathematical communication ability and student’s Self Regulated Learning. Keywords: PQ4R, Refutation Text, Mathematical Communication, and Self Regulated Learning

Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data

Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Such architecture is well known to represent higher learning capability compared with some conventional models if the best set of parameters in the optimal network structure is found. We have been developing the adaptive learning method that can discover the optimal network structure in Deep Belief Network (DBN). The learning method can construct the network structure with the optimal number of hidden neurons in each Restricted Boltzmann Machine and with the optimal number of layers in the DBN during learning phase. The network structure of the learning method can be self-organized according to given input patterns of big data set. In this paper, we embed the adaptive learning method into the recurrent temporal RBM and the self-generated layer into DBN. In order to verify the effectiveness of our proposed method, the experimental results are higher classification capability than the conventional methods in this paper.Comment: 8 pages, 9 figures. arXiv admin note: text overlap with arXiv:1807.03487, arXiv:1807.0348