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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Pilar Nusa Mandiri Jurnal Manajemen Informatika Jurnal Teknologi dan Informasi Jurnal Sistem Komputer & Kecerdasan Buatan Abdimasku : Jurnal Pengabdian Masyarakat Jurnal AbdiMas Nusa Mandiri
Nawawi, Hendri Mahmud
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model Mustopa, Ali; Sasongko, Agung; Nawawi, Hendri Mahmud; Wildah, Siti Khotimatul; Agustiani, Sarifah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2807

Abstract

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%.
Co-Authors Agung Baitul Hikmah Agung Sasongko Agustiani, Sarifah Ali Mustopa, Ali ANI OKTARINI SARI, ANI OKTARINI Eri Bayu Pratama, Eri Bayu Gata, Windu Jajang Jaya Purnama Junianto, Rahmad Kahfi, Ahmad Hafidzul Kholifah, Desiana Nur M. Rangga Ramadhan Saelan Nugraha, Fitra Septia Ridwansyah Ridwansyah Risnandar, Risnandar Sarwono Sarwono Siti Khotimatul Wildah Susy Rosyida Thira, Indra Jiwana