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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2093-2100

Abstract

Computer vision deals with image-based problems, such as deep learning, classification, and object detection. This study classifies the quality of dairy parents into three, namely high, medium, and low based on morphology by focusing on Bogor Indonesia farms. The morphological images used are the side and back of dairy cows and the challenge is to determine the optimal accuracy of the model for it to be implemented into an automated system. The 2-step mask region-based convolutional neural network (mask R-CNN) and Canny segmentation algorithm were continuously used to classify the convolutional neural network (CNN) in order to obtain optimal accuracy. When testing the model using training and testing ratios of 90:10 and 80:20, it was evaluated in terms of accuracy, precision, recall, and F1-score. The results showed that the highest model produced an accuracy of 85.44%, 87.12% precision, 83.79% recall, and 84.94% F1-score. Therefore, it was concluded that the test result with 2-stages of segmentation was the best.
Co-Authors Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Andri Juliyanto Anton Romadoni Junior Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Farkhina Dwi Utari Fitri Nur Masruriyah, Anis Hanny Hikmayanti Hanny Hikmayanti H Hanny Hikmayanti Handayani Indi Nurul Hassanah Indra, Jamaludin Jaman, Jajam Haerul Juwita, Ayu Ratna Lestari, Santi Arum Puspita Lutfiah Adeliana Maulana Abdur Rofik Mulya Cahya Ramadanty Murniasih Nahrowi Nahrowi Nahrowi Nofita Sari Nursela Basuni Permana, Tedi Priyatna, Bayu Rahmad Nahar Siregar Rizqi Fahrozi Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sutan Faisal Sutan Faisal Sutan Faisal Tia Astiyah Hasan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin