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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Knowledge Engineering and Data Science BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JCES (Journal of Character Education Society) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Signal and Image Processing Letters International Journal of Robotics and Control Systems
Azhari, Ahmad
Universitas Ahmad Dahlan
Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Neuroscience Social Sciences Other

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Journal : International Journal of Advances in Intelligent Informatics

 1 
Vehicle pose estimation for vehicle detection and tracking based on road direction Adhi Prahara; Ahmad Azhari; Murinto Murinto
International Journal of Advances in Intelligent Informatics Vol 3, No 1 (2017): March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i1.88

Abstract

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).
Brainwaves feature classification by applying K-Means clustering using single-sensor EEG Ahmad Azhari; Leonel Hernandez
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.86

Abstract

The use of brainwave signal is a step in the introduction of the individual identity using biometric technology based on characteristics of the body. Brainwave signal has unique characteristics and different on each individual because the brainwave cannot be read or copied by people so it is not possible to have a similarity of one person with another person. To be able to process the identification of individual characteristics, which obtained from the signal brainwave, required a pattern of brain activity that is prominent and constant. Cognitive activity testing using a single-sensor EEG (Electroencephalogram) divided into two categories, called the activity of cognitive involving the ability of the right brain (creativity, imagination, holistic thinking, intuition, arts, rhythms, nonverbal, feelings, visualization, tune of songs, daydreaming) and the left brain (logic, analysis, sequences, linear, mathematics, language, facts, think in words, word of songs, computation) give a different cluster based on two times the test on mathematical activities (no cluster slices of experiment 1 and experiment 2). The result showed that cognitive activity based on math activity can provide a signal characteristic that can be used as the basis for a brain-computer interface applications development by utilizing EEG single-sensor.
Principal component analysis implementation for brainwave signal reduction based on cognitive activity Ahmad Azhari; Murein Miksa Mardhia
International Journal of Advances in Intelligent Informatics Vol 3, No 3 (2017): November 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i3.118

Abstract

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form.  To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect.  Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.
Co-Authors Adhi Prahara Adhi Prahara Adhi Susanto Andri Pranolo Ardiansyah, Ardiansyah Arif Wirawan Muhammad Beni Purnomo Dewi Pramudi Ismi DWI SURYANTO Fathia Irbati Ammatulloh Fathurrahman, Haris Imam Karim Garnis Ajeng Pamiela Gita Indah Budiarti Gralo Yopa Rahmat Pratama Isro Dwian Yunandha Leonel Hernandez Li-yi Chin Murein Mardhia Murein Miksa Mardhia Murinto Murinto Prahara, Adhi Prasetya Murdaka Putra Tole Sutikno Ummi Athiyah Yingchi Mao