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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi
Suryotomo, Andiko Putro
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Computer Science & IT Electrical & Electronics Engineering

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Saifullah, Shoffan; Drezewski, Rafal; Yudhana, Anton; Pranolo, Andri; Kaswijanti, Wilis; Suryotomo, Andiko Putro; Putra, Seno Aji; Khaliduzzaman, Alin; Prabuwono, Anton Satria; Japkowicz, Nathalie
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Input Variable Selection for Oil Palm Plantation Productivity Prediction Model Suryotomo, Andiko Putro; Harjoko, Agus
Telematika Vol 20 No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9674

Abstract

Purpose: This study aims to implement and improve a wrapper-type Input Variable Selection (IVS) to the prediction model of oil palm production utilizing oil palm expert knowledge criteria and distance-based data sensitivity criteria in order to measure cost-saving in laboratory leaf and soil sample testing.Methodology: The proposed approach consists of IVS process, searching the best prediction model based on the selected variables, and analyzing the cost-saving in laboratory leaf and soil sample testing.Findings/result: The proposed method managed to effectively choose 7 from 19 variables and achieve 81.47% saving from total laboratory sample testing cost.Value: This result has the potential to help small stakeholder oil palm planter to reduce the cost of laboratory testing without losing important information from their plantation.
Co-Authors Agus Harjoko Andri Pranolo Anton Satria Prabuwono Anton Yudhana Drezewski, Rafal Japkowicz, Nathalie Kaswijanti, Wilis Khaliduzzaman, Alin Putra, Seno Aji Saifullah, Shoffan