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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) IPTEK Journal of Proceedings Series Perfecting a Video Game with Game Metrics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec REKAYASA Jurnal Ilmiah Pangabdhi Proceeding of the Electrical Engineering Computer Science and Informatics Network Engineering Research Operation [NERO] Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Layanan Masyarakat (Journal of Public Service)
Rima Tri Wahyuningrum
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Physics Social Sciences Other

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Journal : Perfecting a Video Game with Game Metrics

 1 
Multi-Criteria in Discriminant Analysis to Find the Dominant Features Arif Muntasa; Indah Agustien Siradjuddin; Rima Tri Wahyuningrum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3472

Abstract

A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra.  Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP). 
Double Difference Motion Detection and Its Application for Madura Batik Virtual Fitting Room Rima Triwahyuningrum; Indah Agustien Siradjuddin; Yonathan Fery Hendrawan; Arik Kurniawati; Ari Kusumaningsih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2236

Abstract

Madura Batik Virtual Fitting Room using double difference algorithms motion detection is proposed in this research. This virtual fitting room consists of three main stages, i.e. motion detection, determination of region of interest of the detected motion, superimposed the virtual clothes into the region of interest. The double difference algorithm is used for the motion detection stage, since in this algorithm, the empty frame as the reference frame is not required. The double difference algorithm uses the previous and next frame to detect the motion in the current frame. Perception Test Images Sequences Dataset are used as the data of the experiment to measure the performance accuracy of this algorithm before the algorithm is used for the Madura batik virtual fitting room. The accuracy is 57.31%, 99.71%, and 78.52% for the sensitivity, specificity, and balanced accuracy, respectively. The build Madura batik virtual fitting room in this research can be used as the added feature of the Madura batik online stores, hence the consumer is able to see whether the clothes is fitted to them or not, and this virtual fitting room is also can be used as the promotion of Madura batik broadly.
Efficient Kernel-based Two-Dimensional Principal Component Analysis for Smile Stages Recognition Rima Tri Wahyuningrum; Fitri Damayanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 1: March 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i1.766

Abstract

 Recently, an approach called two-dimensional principal component analysis (2DPCA) has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Co-Authors Ahmad Irsyad Amru Rosyidi Angkoso, Cucun Very Ari Kusumaningsih Arif Muntasa Arik Kurniawati Bagus Fajariyanto Bahrur Rosyid Bain Khusnul Khotimah Budi Dwi Satoto Fitri Damayanti Hendrawan, Yonathan Ferry I Ketut Eddy Purnama Irhamni, Firli Khozaimi, Ach. Kurniawan Eka Permana Mauridhi Hery Purnomo Mochammad Kautsar Sophan Rochman, Eka Mala Sari Siradjuddin, Indah Agustien Syafi?i, Slamet Imam Yonathan Ferry Hendrawan Yonathan Fery Hendrawan