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Your search keyword '"M. Hery Purnomo"' showing total 3 results
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3 results on '"M. Hery Purnomo"'

1. Modified Background Subtraction Statistic Models for Improvement Detection and Counting of Active Spermatozoa Motility

Author

I Gede Susrama Masdiyasa, I D. G. Hari Wisana, I K. Eddy Purnama, and M. Hery Purnomo

Subjects
Electronic computers. Computer science, QA75.5-76.95
Abstract

An important early stage in the research of sperm analysis is the phase of sperm detection or separating sperm objects from images/video obtained from observations on semen. The success rate in separating sperm objects from semen fluids has an important role for further analysis of sperm objects. Algorithm or Background subtraction method is a process that can be used to separate moving objects (foreground) and background on sperm video data that tend to uni-modal. In this research, some of the subproject model statistics of substrata model are Gaussian single, Gaussian Mixture Model (GMM), Kernel Density Estimation and compared with some basic subtraction model background algorithm in detecting and counting the number of active spermatozoa. From the results of the tests, the Grimson GMM method has an f-measure value of 0.8265 and succeeded in extracting the sperm form near its original form compared to other methods

Published
2018
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2. A NEW APPROACH OF LEARNERS' ASSESSMENT USING BLOOM'S TAXONOMY-BASED SERIOUS GAME

Author

I.N Sukajaya, S. M Susiki Nugroho, I. K.E Purnama, and M. Hery Purnomo

Subjects
Learning Assessment, Bloom's Taxonomy, Serious Game, Electronic computers. Computer science, QA75.5-76.95
Abstract

This paper describes: a scenario, agent specification, mapping of knowledge domain, an implementation of Bloom's taxonomy-based serious game (BoTySeGa), and players' response against the game. The development of BoTySeGa is pursued to the availability of an alternative assessment tool for learning in elementary school. It considers aspects: game knowledge, subject matter of parallelogram for 5th grade elementary school learners, and cognitive domain of Bloom's taxonomy. BoTySeGa's level of challenge is structured accommodates cognitive domain of Bloom for elementary school learners (knowledge, comprehension, application). To make sure that all functions and features work well; we conducted user acceptance test against the game prototype. We also took players' response to BoTySeGa utilizing five-points Likert-type of questionnaire. The questions are distributed in 15 items. User acceptance testing involving 85 learners of 5th grade elementary school shows that BoTySeGa has fulfilled the learning assessment requirement. With the response score ranged from 5 to 75; it is found that the average score of players' response to the implementation of BoTySeGa in learning is 59.93. This response value falls within "Positive" category.

Published
2015
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3. Prediction of DNA Hepatitis C Virus based on Recurrent Neural Network-Back Propagation Through Time (RNN-BPTT)

Author

M. Hery Purnomo, Tri Arief Sardjono, and Berlian Al Kindhi

Subjects
Normalization (statistics), Decision support system, Network architecture, Mutation rate, Recurrent neural network, Mean squared error, Artificial neural network, business.industry, Computer science, Technical analysis, Artificial intelligence, business
Abstract

Hepatitis C Virus (HCV) is one virus that has a high mutation rate in the world. To predict the mutation can be used fundamental analysis and technical analysis. Fundamental analysis relies on external factors such as attributes attached to the primer and the isolate. While technical analysis learns the movement of the mutation itself by relying on graphs and mathematical formulas. This study combines fundamental analysis and technical analysis in predicting HCV mutations. Application of Recurrent Neural Network (RNN) method as a form of technical analysis and fundamental analysis is applied in the form of including some fundamental factor data as training datasets. RNN is a neural network that has a feedback connection to the neuron itself, or a previous neuron. RNN is able to reactivate actual data values in the past to be re-entered with actual data values at the moment. This study used Elman network architecture with Back Propagation Through Time learning algorithm (BPTT) and used Linear normalization. The problem when predicting HCV mutations is how to determine the best learning rate value. Therefore the fundamental approach will also be incorporated into the neural network. On the backward process, to calculate the value of weight correction is to multiply the value of learning rate with hidden neuron value. We propose that each neuron has an adaptive learning rate according to the condition of the neuron. Where each input neuron from this study is the result of the HCV primer normalized in preliminary research. Each primer has attributes that can be developed as decision support. Test results show the smallest error of the prediction process by RMSE are 0.0163000, with accuracy prediction value are 99%.

Published
2019
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