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Achmad Basuki
Electronic Engineering Polytechnic Institute of Surabaya
Computer Science & IT

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Remo Dance Motion Estimation with Markerless Motion Capture Using The Optical Flow Method Neny Kurniati; Achmad Basuki; Dadet Pramadihanto
EMITTER International Journal of Engineering Technology Vol 3 No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v3i1.33

Abstract

Motion capture has been developed and applied in various fields, one of them is dancing. Remo dance is a dance from East Java that tells the struggle of a prince who fought on the battlefield. Remo dancer does not use body-tight costume. He wears a few costume pieces and accessories, so required a motion detection method that can detect limb motion which does not damage the beauty of the costumes and does not interfere motion of the dancer. The method is Markerless Motion Capture. Limbs motions are partial behavior. This means that all limbs do not move simultaneously, but alternately. It required motion tracking to detect parts of the body moving and where the direction of motion. Optical flow is a method that is suitable for the above conditions. Moving body parts will be detected by the bounding box. A bounding box differential value between frames can determine the direction of the motion and how far the object is moving. The optical flow method is simple and does not require a monochrome background. This method does not use complex feature extraction process so it can be applied to real-time motion capture. Performance of motion detection with optical flow method is determined by the value of the ratio between the area of the blob and the area of the bounding box. Estimate coordinates are not necessarily like original coordinates, but if the chart of estimate motion similar to the chart of the original motion, it means motion estimation it can be said to have the same motion with the original.Keywords: Motion Capture, Markerless, Remo Dance, Optical Flow
Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes Andri Permana Wicaksono; Tessy Badriyah; Achmad Basuki
EMITTER International Journal of Engineering Technology Vol 4 No 1 (2016)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (813.592 KB) | DOI: 10.24003/emitter.v4i1.119

Abstract

Mellitus Diabetes is an illness that happened in consequence of the too high glucose level in blood because the body could not release or use insulin normally. The purpose of this research is to compare the two methods in The data-mining, those are a Regression Logistic method and a Bayesian method, to predict the risk level of diabetes by web-based application and nine attributes of patients data. The data which is used in this research are 1450 patients that are taken from RSD BALUNG JEMBER, by collecting data from 26 September 2014 until 30 April 2015. This research uses performance measuring from two methods by using discrimination score with ROC curve (Receiver Operating Characteristic).  On the experiment result, it showed that two methods, Regression Logistic method and Bayesian method, have different performance excess score and are good at both. From the highest accuracy measurement and ROC using the same dataset, where the excess of Bayesian has the highest accuracy with 0,91 in the score while Regression Logistic method has the highest ROC score with 0.988, meanwhile on Bayesian, the ROC is 0.964. In this research, the plus of using Bayesian is not only can use categorical but also numerical.
Co-Authors Andri Permana Wicaksono Dadet Pramadihanto Neny Kurniati Tessy Badriyah, Tessy