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All Journal EMARA Indonesian Journal of Architecture Jurnal ULTIMATICS Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) ComTech: Computer, Mathematics and Engineering Applications Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Buletin Poltanesa Eduvest - Journal of Universal Studies
Daniel Martomanggolo Wonohadidjojo
Universitas Ciputra, Surabaya, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Neuroscience Social Sciences

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Journal : Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)

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Penerapan Algoritma Yolov4-Tiny Dan Efficientnetv2-S Untuk Deteksi Kesegaran Ikan Gurami Hans Richard Alim Natadjaja; Daniel Martomanggolo Wonohadidjojo
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 2 (2023): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i2.633

Abstract

Fish production is one of the largest in Indonesia. Among many fishery products, Gurami fish is one of the most widely processed by society. However, the decrease in the freshness of fishery products is susceptible to occur when they reach the hands of consumers. Several methods for detecting fish freshness have been applied to assist in obtaining fresh fish products. However, some of these methods have a lack of accuracy rate, not all of them were developed to detect gurami freshness. Therefore, a solution is needed to help people detect the freshness of Gurami fish and make it accessible on mobile devices. This solution can be realized by using Deep Learning methods. The methods proposed in this study use the Convolutional Neural Network (CNN) by utilizing the YOLOv4-Tiny algorithm to detect the Region of Interest (ROI) and the EfficientNetV2-S architecture freshness classification on Gurami fish images. The training process using both methods can produce an ROI detection model with a mean average precision of 93,58% and a classification model with an accuracy rate of 93%
Co-Authors Citra, Caecilia Citra Claudia Kenyta Dyah Kusuma Wardhani Eric Harianto Hans Richard Alim Natadjaja Hansel Santoso Indra Maryati Laura Mahendratta Tjahjono Lestari, Caecilia Citra Mychael Maoeretz Engel Rinabi Tanamal Susan Susan Yosua Setyawan Soekamto Yusuf Ariyanto Yuwono Marta Dinata