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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
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Journal : Computer Science and Information Technologies

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The effect of segmentation on the performance of machine learning methods on the morphological classification of Friesien Holstein dairy cows Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p59-68

Abstract

Many classification algorithms are in the form of image pattern recognition; the approach to the complexity of the problem should be a feature of feasibility for representing images. The morphology of dairy cows greatly affects their health and milk production. The paper will apply several classification methods based on the morphology of Holstein Friesian dairy cows. To improve the accuracy of the model used, the segmentation process is the right step. In this paper, we compare several machine learning algorithms to get optimal accuracy. The algorithm used a support vector machine (SVM), artificial neural networks, random forests and logistic regression. Segmentation methods used are mask region-based convolutional neural network (R-CNN) and Canny; optimal accuracy is expected to create intelligent applications. The success of the method is measured with accuracy, precision, recall, and F1 Score, as well as testing by conducting a training-testing ratio of 90:10 and 80:20. This study discovered an artificial neural network optimal model with Canny with an accuracy of 82.50%, precision of 87.00%, recall of 79.00%, F1-score of 81.62%, and testing ratio of 90:10. While the models with the highest 80:20 ratio achieved 84.39% accuracy, 88.46% precision, 80.47%, and 83.00% F1-score with mask R-CNN with logistic regression.
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