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All Journal Bulletin of Electrical Engineering and Informatics PROSIDING CSGTEIS 2013 EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika J-Dinamika: Jurnal Pengabdian Kepada Masyarakat GUYUB: Journal of Community Engagement The Indonesian Journal of Computer Science
Idris Winarno
Politeknik Elektronika Negeri Surabaya
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease Fajrianti, Evianita Dewi; Pratama, Afis Asryullah; Nasyir, Jamal Abdul; Rasyid, Alfandino; Winarno, Idris; Sukaridhoto, Sritrusta
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.793

Abstract

In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.
Co-Authors Abd. Rasyid Syamsuri Afifah, Izza Nur Ali Ridho Barakbah Amang Sudarsono, Amang Arissabarno, Cahyo Arna Fariza Ata Amrullah Aulia, Fira Ayub, Waqas Bambang Sumantri Bima Sena Bayu Dewantara Budiarti, Rizqi Putri Nourma Dadet Pramadihanto Dewi, Ni Made Lintang Asvini Entin Martiana Kusumaningtyas Fajrianti, Evianita Dewi Gunawan, Agus Indra Hamida, Silfiana Nur Hary Oktavianto Huda, Achmad Thorikul iwan Syarif Jamal Abdul Nasyir Jamal Abdul Nasyir M Udin Harun Al Rasyid, M Udin Harun Marlisa Sigita Marlisa Sigita, Marlisa Muhammad Alfian Muzaki Nurus Sani Muzaki Nurus Sani, Muzaki Nurus Nasyir, Jamal Abdul Nurazmi, Talita Iza Pratama, Afis Asryullah Primajaya, Grezio Arifiyan Rachmawati, Oktavia Citra Resmi Ramadhan, Alif Wicaksana Rasyid, Alfandino Rudi Kurniawan Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Suasono, Zaikhul Sulthon Sudarsono , Amang Susanti, Puspasari Tessy Badriyah, Tessy Tri Harsono Udin Harun Al-Rasyid Udin Harun Al-Rasyid Utomo, Agus Priyo Wahjoe Tjatur Sesulihatien Wiratmoko Yuwono Yunia Ikawati