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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 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
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 : Knowledge Engineering and Data Science

 1 
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

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (666.504 KB) | DOI: 10.17977/um018v2i22019p47-57

Abstract

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.
Co-Authors Adhi Prahara Adhi Prahara Adhi Susanto Andri Pranolo Ardiansyah, Ardiansyah 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