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All Journal Jurnal Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Pangabdhi Register: Jurnal Ilmiah Teknologi Sistem Informasi Panrita Abdi - Jurnal Pengabdian pada Masyarakat Network Engineering Research Operation [NERO] Jurnal Teknoinfo JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Indonesian Journal of Electrical Engineering and Computer Science Journal of Data Science and Its Applications Jurnal Layanan Masyarakat (Journal of Public Service) Makara Journal of Technology
Siradjuddin, Indah Agustien
Universitas Trunojoyo Madura
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : telkomnika telecommunication computing electronics and control

 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.
Contradictory of the Laplacian Smoothing Transform and Linear Discriminant Analysis Modeling to Extract the Face Image Features Arif Muntasa; Indah Agustien Siradjuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6576

Abstract

Laplacian smoothing transform uses the negative diagonal element to generate the new space. The negative diagonal elements will deliver the negative new spaces. The negative new spaces will cause decreasing of the dominant characteristics. Laplacian smoothing transform usually singular matrix, such that the matrix cannot be solved to obtain the ordered-eigenvalues and corresponding eigenvectors. In this research, we propose a modeling to generate the positive diagonal elements to obtain the positive new spaces. The secondly, we propose approach to overcome singularity matrix to found eigenvalues and eigenvectors. Firstly, the method is started to calculate contradictory of the laplacian smoothing matrix. Secondly, we calculate the new space modeling on the contradictory of the laplacian smoothing. Moreover, we calculate eigenvectors of the discriminant analysis. Fourth, we calculate the new space modeling on the discriminant analysis, select and merge features. The proposed method has been tested by using four databases, i.e. ORL, YALE, UoB, and local database (CAI-UTM). Overall, the results indicate that the proposed method can overcome two problems and deliver higher accuracy than similar methods. 
Particle Filter with Gaussian Weighting for Human Tracking Indah Agustien Siradjuddin; M. Rahmat Widyanto; T. Basaruddin T. Basaruddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 4: December 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i4.869

Abstract

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weight. Particle filter with Gaussian weighting is proposed to accomplish the computational problem. There are two main stages in this method, i.e. prediction and update. The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage. In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle’s weight is calculated, and the remain particle’s weight is calculated using the Gaussian weighting. Experiment is done using artificial dataset. The average accuracy is 80,862%. The high accuracy that is achieved by this method could use for the real-time system tracking
Co-Authors Ari Kusumaningsih Ariesta Kartika Sari Ariesta Kartika Sari Arif Muntasa Arik Kurniawati Bauravindah, Achmad Fifin Ayu Mufarroha Hamed Ayani, Irham Irmalia Suryani Faradisa Kurniawan Eka Permana Kurniawan Kurniawan M. Rahmat Widyanto Mochammad Kautsar Sophan Muhammad Rahmat Widyanto Muhammad Rahmat Widyanto, Muhammad Rahmat Muhammad Yusqi Alfan Thoriq Puji Rahayu Ningsih Puji Rahayu Ningsih Rima Tri Wahyuningrum Sukmawati Endah Sukmawati Endah, Sukmawati Sukmawati Nur Endah T. Basaruddin T. Basaruddin T. Basaruddin Tarzan Basaruddin Tarzan Basaruddin, Tarzan Tri Wahyuningrum, Dr. Rima Wanda Ramansyah Wrida Adi Wardana Yonathan Fery Hendrawan