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All Journal Router : Jurnal Teknik Informatika dan Terapan Router : Jurnal Teknik Informatika dan Terapan
Rafif Rasendriya
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Computer Science & IT

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Penggunaan Visi Komputer untuk Mengidentifiksi Jenis Buah dari Gambar Supiyandi Supiyandi; Rafif Rasendriya
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i4.287

Abstract

Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.
Penggunaan Visi Komputer untuk Mengidentifiksi Jenis Buah dari Gambar Supiyandi Supiyandi; Rafif Rasendriya
Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v2i4.287

Abstract

Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.
Co-Authors Supiyandi Supiyandi