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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Informatika Komputer Prosiding KOMMIT EXPLORE Dinasti International Journal of Management Science Cakrawala Repositori Imwi EXPLORE Explore Prosiding SeNTIK STI&K

Asep Juarna

Universitas Gunadarma

Author-ID : 293106

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Background Estimation Using Principal Component Analysis Based on Limited Memory Block Krylov Subspace Optimization Ilmiyati Sari; Asep Juarna; Suryadi Harmanto; Djati Kerami
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Given a video of ???? frames of size ℎ × ????. Background components of a video are the elements matrix which relative constant over ???? frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × ???? × ????) into 2 dimensions one (???? × ????), where ???? is a linear array of size ℎ × ????. The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).

A Preference Model on Adaptive Affinity Propagation Rina Refianti; Achmad Benny Mutiara; Asep Juarna; Adang Suhendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.

Co-Authors A Benny Mutiara Achmad Benny Mutiara Adang Suhendra Bagus Setiadi Gurmilang Dharmayanti Dharmayanti Djati Kerami Erni Rihyanti Ilmiyati Sari Jahfal Uno Surya Lazuardi Maria Sri Wulandari Prihandoko . Rezky Puteri Sariaji Rina Refianti Santi Dwi Agustin Santi Dwi Agustin Sulaiman, Agus Suryadi Harmanto Tri Juhari Yudha Destianto Dwi Prakoso Zulkarnaen, Banu Harsa

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Journal : International Journal of Electrical and Computer Engineering