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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Alfanico, Febrian
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Computer Science & IT Decision Sciences, Operations Research & Management

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Deep Learning Untuk Klasifikasi Kematangan Buah Mangrove Berdasarkan Warna Mukhtar, Harun; Alfanico, Febrian; Fu’adah Amran, Hasanatul; Handayani, Fitri; Medikawati Taufiq, Reny
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6292

Abstract

Plants that live between land and sea, such as mangroves, are influenced by the tides and tides. Indonesia has the largest mangrove forest in the world and a variety of biodiversity and structure. People currently detect mangrove maturity by looking directly at the fruit. This study proposes to classify the maturity of mangrove fruit using artificial intelligence techniques, making it easier for farmers to determine the ripeness of the fruit. This proposal uses data from 200 images for mangroves taken directly from Lukit Village, Merbau District, Meranti Islands Regency. This research improves the Convolutional Neural Network (CNN) method to classify mangrove fruit maturity. The results obtained from this research were by classifying ripe and unripe fruit. Based on this research, accuracy reaches a maximum of 96%.
Co-Authors Fitri Handayani Fu’adah Amran, Hasanatul Harun Mukhtar Taufiq, Reny Medikawati