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Bib Paruhum Silalahi
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, IPB University, Bogor
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation

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Journal : Journal of ICT Research and Applications

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Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern Ngakan Nyoman Kutha Krisnawijaya; Yeni Herdiyeni; Bib Paruhum Silalahi
Journal of ICT Research and Applications Vol. 11 No. 1 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.1.5

Abstract

As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.
Co-Authors Agung Prajuhana Putra Agus Buono Agus Buono Agus Buono Agus Buono Amril Aman Danny Dimas Sulistio Elisabeth Sri Hendrastuti Fadhilah Syafria Fadhilah Syafria Fahren Bukhari Faisal Muhammad Fajar Setyawan Farahdila Sahara Farida Hanum Farida Hanum Franki Yusuf Bisilisin Haliza Suci Rachmadini Hidayatul Mayyani Hidayatul Mayyani Hidayatul Mayyani I Gede Arta Wibawa I Wayan Mangku I Wayan Mangku Inggih Permana Julio Adisantoso Karlisa Priandana Khoerul Fatihin Marisa Nurbaiti Mayyani, Hidayatul Mohamad Novanto Mu'min, Ulil Albab Muhammad Wiranadi Utama Mutia Atika Nabilah Diar Brigitha Ngakan Nyoman Kutha Krisnawijaya Nur Ahzan, Zulkaidah Nur Aliatiningtyas Nurul Fathiah Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Prapto Tri Supriyo Rafika Husnia Munfa'ati Silviana Eka Pertiwi Sri Nurdiati Sugi Guritman Sumanto, Sumanto Syeiva Nurul Desylvia T Murdani Saputra Ulil Albab Mu'min Yasin Yusuf