Article Per Year (5 Year)
p-Index From 2021 - 2026
2.064
P-Index
This Author published in this journals
All Journal Jurnal Sarjana Teknik Informatika International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science KACANEGARA Jurnal Pengabdian pada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Signal and Image Processing Letters Science in Information Technology Letters Signal and Image Processing Letters
Adhi Prahara, Adhi
Universitas Ahmad Dahlan
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Neuroscience

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : knowledge engineering and data science

 1 
Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU Prahara, Adhi; Ismi, Dewi Pramudi; Azhari, Ahmad
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Parallel Approach of Adaptive Image Thresholding Algorithm on GPU Prahara, Adhi; Pranolo, Andri; Anwar, Nuril; Mao, Yingchi
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques to optimize the calculation. The proposed approach was tested on various sizes of grayscale images. The result shows that the parallel implementation of three adaptive image thresholding methods on GPU achieves 4-6 speeds up compared to the CPU implementation, reducing the computational time significantly and effectively dealing with high resolution images.
Co-Authors A.A. Ketut Agung Cahyawan W Abdillah, Aldi Khoirul Ahmad Azhari Alqorni, Wais ALYA MASITHA Andri Pranolo Anggisa, Ahmad Bramdimas Anwar, Kaspul Arif Budi Setiawan Banjaransari, Muhammad Dewi Pramudi Ismi, Dewi Pramudi Fatkhannudin, Muhammad Noor Fatkhannudin, Muhammad Noor Habibillah, Ahmad Yasin Herman Yuliansyah, Herman Indikawati, Fitri Indra Jefree Fahana Laelatul Azizah, Laelatul Mao, Yingchi Munif, Rihwan Murinto Murinto Nuril Anwar, Nuril Pramesti, Mara Indar Ramadayanti, Susanti Aulia Rani, Siti Sofia Romadhoni, Ilham Fauzi Son Ali Akbar