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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Informatika Mulawarman International Journal of Artificial Intelligence Research 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

Author-ID : 2470095

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

Published : 19 Documents Claim Missing Document

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Information Visualization of Lesson Study Activity Mardhia, Murein; Azhari, Ahmad; Ardiansyah, Ardiansyah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 3, No 2 (2017)
Publisher : Universitas Ahmad Dahlan

This research explores how to design an interface dashboard to visualize information about evaluation activities occurred during lesson study. Data were collected in two stages using interview and questionnaire techniques whom the respondents were experts and team teaching who had been experienced the whole activities of Lesson Study. We conducted a case study in Mathematics Education Program of Universitas Ahmad Dahlan, where the teaching staffs have participated and conducted the Lesson Study activities for more than two semesters. The interface dashboard prototype was tested by conducting User Experience method and Software Usability Testing assessment. Results obtained show a fairly good acceptance level qualitatively and so does from SUS scored 75 from the average value of all participants

Analisis Fitur Warna dan Tekstur untuk Metode Deteksi Jalan Prahara, Adhi; Azhari, Ahmad
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 2, No 2 (2016)
Publisher : Universitas Ahmad Dahlan

Deteksi jalan digunakan untuk mengidentifikasi area jalan pada citra atau frame video. Tantangan dalam mendeteksi jalan diantaranya warna dan tekstur jalan yang beragam serta masalah pencahayaan. Oleh karena itu diperlukan fitur yang sesuai untuk menghadapi permasalahan tersebut. Pada penelitian ini dilakukan analisis fitur warna dan tekstur untuk mendeteksi jalan. Kumpulan 50 sampel jalan diambil untuk diekstrak fitur warna di tiga ruang warna yang berbeda yaitu RGB (Red-Green-Blue), HSV (Hue-Saturation-Value), dan CIE L*a*b* serta diekstrak fitur teksturnya dengan GLCM (Gray Level Co-occurrence Matrix). Fitur-fitur tersebut kemudian dianalisis untuk didapatkan fitur dengan variasi yang rendah dari semua sampel jalan yang digunakan untuk menentukan threshold warna maupun tekstur. Hasil pengujian metode deteksi jalan dari 150 citra uji jalan menggunakan batasan fitur hasil analisis menunjukkan akurasi 90,54%.

Deep Learning on EEG Study Concentration in Pendemic Garnis Ajeng Pamiela; Ahmad Azhari
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 16, No 2 (2021): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v16i2.6474

Brainwaves are one of the biometric properties that can be used to identify individuals based on their physical and behavioural characteristics. An electroencephalogram (EEG) can be used to measure and capture brain wave activity. The activities required are in the form of giving complex tasks to get thinking and concentration processes called Cognitive Tests, in the form of a Culture Fair Intelligence Test (CFIT) stimulus and Competency Test (UK). This study aims to obtain a pattern of the relationship between concentration and learning outcomes for late adolescent students during the pandemic. The object of research involved in this research is the 10th grade students of TKJ SMK. Data acquisition was carried out twice on the Beta signal by doing cognitive test questions which were done twice at school and at home. Then the data obtained from the test results will be extracted using Fast Fourier Transform (FFT). Furthermore, after the data extraction results are obtained, the classification process will be carried out using the CNN algorithm. The results of the FFT obtained the average value of the signal peak. The results of the CNN classification show that the pandemic does not affect student concentration. The average signal concentration in schools when testing using CFIT is 0.2445 and at the time of testing using UK Mathematics is 0.1330 with an average CFIT score of 77.05 and for UK average is 53.33 with an accuracy value of 83.33 %. While the average signal concentration at home when testing using CFIT is 0.2252 and at the time of testing using UK Mathematics is 0.1301 with an average CFIT score of 77.13 and for UK average is 57.50 with an accuracy value of 83, 33%.

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection Arif Wirawan Muhammad; Cik Feresa Mohd Foozy; Ahmad Azhari
International Journal of Artificial Intelligence Research Vol 4, No 1 (2020): June
Publisher : STMIK Dharma Wacana

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity. DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development. According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.

Texton Based Segmentation for Road Defect Detection from Aerial Imagery Adhi Prahara; Son Ali Akbar; Ahmad Azhari
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : STMIK Dharma Wacana

Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier Ummi Athiyah; Arif Wirawan Muhammad; Ahmad Azhari
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p19-27

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure.

Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU Adhi Prahara; Dewi Pramudi Ismi; Ahmad Azhari
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p40-49

K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when dealing with outliers and arbitrary distance metric also in the situation when the mean or median does not exist within data. However, k-medoids suffers a high computational complexity. Partitioning Around Medoids (PAM) has been developed to improve k-medoids clustering, consists of build and swap steps and uses the entire dataset to find the best potential medoids. Thus, PAM produces better medoids than other algorithms. This research proposes the parallelization of PAM in k-medoids clustering on GPU to reduce computational time at the swap step of PAM. The parallelization scheme utilizes shared memory, reduction algorithm, and optimization of the thread block configuration to maximize the occupancy. Based on the experiment result, the proposed parallelized PAM k-medoids is faster than CPU and Matlab implementation and efficient for large dataset.

Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities Ahmad Azhari; Adhi Susanto; Andri Pranolo; Yingchi Mao
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.

Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor Ahmad Azhari; Fathia Irbati Ammatulloh
Signal and Image Processing Letters Vol. 1 No. 1: March 2019
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.170

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.

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

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).

Co-Authors Adhi Prahara Adhi Prahara Adhi Susanto Andri Pranolo Ardiansyah, Ardiansyah Arif Wirawan Muhammad Arif Wirawan Muhammad Beni Purnomo Cik Feresa Mohd Foozy 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 Son Ali Akbar Tole Sutikno Ummi Athiyah Yingchi Mao

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