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All Journal TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal FATEKSA : Jurnal Teknologi dan Rekayasa Jurnal Teknologi Terpadu Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Tematik : Jurnal Teknologi Informasi Komunikasi JNANALOKA Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Scientific Research, Education, and Technology Jurnal Honai
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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Analisa Performa Convolutional Neural Network dalam Klasifikasi Citra Apel dengan Data Augmentasi Dzalfa Tsalsabila Rhamadiyanti; Kusrini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2023

Abstract

Augmentation is creating new samples from an original dataset by applying small random transformations to the original dataset but retaining its labels. This research applies Data Augmentation to the Convolutional Neural Network model for apple image classification. The apple images used are Braeburn apples which have orange to red skin with a yellow background, Crimson Snow apples which have red skin, and Pink Lady apples with bright pink skin and yellow and green hues. There are 675 apple images used, divided into three classes, each with 225 photos. Four augmentation techniques are applied, namely flipping, cropping, rotation, and noise injection. This research carried out six scenarios, namely without augmentation, using each augmentation technique separately and combining two augmentation techniques, which produced the highest accuracy values. From the six scenarios, it was found that the augmentation technique that produced the best accuracy value was noise injection, namely 98.82%, followed by flipping with an accuracy of 72.78%, then rotation with an accuracy value of 68.64% and an augmentation technique that produced an accuracy value. The lowest is cropping, namely 67.46%. The two best augmentation techniques, noise injection, and flipping, were combined and produced an accuracy value of 84.02%. The accuracy value obtained by this combination could be more optimal due to the effect of noise injection, which can erase consistent changes in orientation from flipping. This needs to be improved so that the model can learn consistent features. It is hoped that future research can maximize the effectiveness of augmentation techniques by choosing augmentation techniques that complement each other and suit the characteristics of the data being processed
Co-Authors Agustina Rahmawati Agustina, Nova Ahmad Almas'ud ZD Ahsan, Muhammad Salimy Ali Asgar Zainal Abidin Anggit Dwi Hartanto Anggit Hartanto Arief Setyanto Arli Aditya Parikesit Arnap, Adam DHANI ARIATMANTO Dzalfa Tsalsabila Rhamadiyanti Ema Utami Ferry Wahyu Wibowo Hanafi Handoko Adji Pangestu Kurni, Samuel Everth Andrias Kusnawi Kusnawi M. Rudyanto Arief Maula Hidayat, Fajar Mawene, Brenstein Mohamad Firdaus, Mohamad Rosadi, Ilham Aziz Sari, Yunita Sartika Sri Wahyuningsih Tamaulina Br Sembiring Tolla, Yefta TONNY HIDAYAT Widyanto, Agung Yanuargi, Bayu Zakaria Zakaria Zulkarnain, Jefri Zulpan Hadi