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All Journal Elektron Jurnal Ilmiah Adi Widya : Jurnal Pengabdian Masyarakat Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler JUTEKIN (Jurnal Manajemen Informatika) AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT JMAI (Jurnal Multimedia & Artificial Intelligence) JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal ADAM : Jurnal Pengabdian Masyarakat Journal Of Information System And Artificial Intelligence Jikom: Jurnal Informatika dan Komputer Jurnal Pengabdian Masyarakat Bangsa Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Kepada Masyarakat Radisi Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif Jurnal Pengabdian Masyarakat Mentari Exhibition and Seminar on Science and Creative Technology – Al Azhar Proceeding International Journal of Informatics Engineering and Computing
Indah Susilawati
Universitas Mercu Buana Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Compiler

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GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation Akbar, Mutaqin; Witanti, Arita; Susilawati, Indah
Compiler Vol 8, No 2 (2019): November
Publisher : Sekolah Tinggi Teknologi Adisutjipto Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (477.913 KB) | DOI: 10.28989/compiler.v8i2.455

Abstract

In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.
GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation Mutaqin Akbar; Arita Witanti; Indah Susilawati
Compiler Vol 8, No 2 (2019): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (477.913 KB) | DOI: 10.28989/compiler.v8i2.455

Abstract

In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.
Co-Authors Aji, Dennis Feliawan Arif Wijayanto Arita Witanti Benedictus Fredika Apriawan Santoso Budi Sulistiyo Jati Dennis Feliawan Aji Dwi Pramudya, Alvian Ferry Pradana Putra Gideon Rensenbrink Ayal, Justin Heru Wahyudi Imam Edwin Hasym Muhammad Fandi Nurdiansyah Mutaqin Akbar Nugraeni Nugraeni Nur Alamsyah Paramitalaksmi, Ratri Pebiani, Misa Prasetyaningrum, Putri puput zahiroh Purwanto, Hendri Putry Wahyu Setyaningsih Rahmatuloh, Muhamad Rendi Saputra Risqi, Yoga Alfa Rizkyansyah, M Daffa Rogawati, Nesya Safitri, Della Nur Setyaningsih, Putry Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus Sipata, Daud Imanuel Siti Istiqomah Suharjo, Imam Supatman Supatman Supatman Supatman, Supatman Wibowo, Ibnu Surya Wirabowo, Imam Yoga Purna Irawan Zakaria Habib Al-Ra’zie