Dea Annisa Utami - Academia.edu (original) (raw)
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UERJ - Universidade do Estado do Rio de Janeiro / Rio de Janeiro State University
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Papers by Dea Annisa Utami
Jurnal Penelitian dan Pembelajaran IPA, 2016
The aims of this study is to determine the increase in critical thinking skills and mastery of th... more The aims of this study is to determine the increase in critical thinking skills and mastery of the concept after the application of abductive inquiry learning model at high school students. This research are motivated by the lack of mastery of concepts and critical thinking skills of the preliminary study in the same school. This study used a preexperimental research design and one-group pretest-posttest design involving 33 students of class X in one of the high school in Bandung. Aspects of critical thinking skills measured are the interpretation, analysis, evaluation, inference, and explanatory, while the measured aspects of mastery of concepts is remembering, understanding and analysis. The results of analysis of pretest-posttest scores showed that abductive inquiry learning model can improve critical thinking skills with an average value of 0.47 with a normalized gain medium category. If considered each aspect, all aspects of critical thinking skills with moderate category, explanatory aspects showed the greatest increase is 0.68. Abductive inquiry learning model can also improve the mastery of concepts with an average value of 0.62 normalized gain. All aspects of the concept of mastery increases the medium category, the aspect of the analysis showed the greatest increase in the amount of 0.65
Bioinformatics, 2006
Motivation: Gene selection algorithms for cancer classification, based on the expression of a sma... more Motivation: Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffrey's prior. The improved algorithm (BLogReg) is then typically two or three orders of...
Jurnal Penelitian dan Pembelajaran IPA, 2016
The aims of this study is to determine the increase in critical thinking skills and mastery of th... more The aims of this study is to determine the increase in critical thinking skills and mastery of the concept after the application of abductive inquiry learning model at high school students. This research are motivated by the lack of mastery of concepts and critical thinking skills of the preliminary study in the same school. This study used a preexperimental research design and one-group pretest-posttest design involving 33 students of class X in one of the high school in Bandung. Aspects of critical thinking skills measured are the interpretation, analysis, evaluation, inference, and explanatory, while the measured aspects of mastery of concepts is remembering, understanding and analysis. The results of analysis of pretest-posttest scores showed that abductive inquiry learning model can improve critical thinking skills with an average value of 0.47 with a normalized gain medium category. If considered each aspect, all aspects of critical thinking skills with moderate category, explanatory aspects showed the greatest increase is 0.68. Abductive inquiry learning model can also improve the mastery of concepts with an average value of 0.62 normalized gain. All aspects of the concept of mastery increases the medium category, the aspect of the analysis showed the greatest increase in the amount of 0.65
Bioinformatics, 2006
Motivation: Gene selection algorithms for cancer classification, based on the expression of a sma... more Motivation: Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffrey's prior. The improved algorithm (BLogReg) is then typically two or three orders of...