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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Master Pariwisata (JUMPA) Jurnal Sistem Cerdas Jurnal Mantik
Yohanssen Pratama
Institut Teknologi Del
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Journal : International Journal of Electrical and Computer Engineering

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Face recognition for presence system by using residual networks-50 architecture Yohanssen Pratama; Lit Malem Ginting; Emma Hannisa Laurencia Nainggolan; Ade Erispra Rismanda
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5488-5496

Abstract

Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture Tegar Arifin Prasetyo; Victor Lambok Desrony; Henny Flora Panjaitan; Romauli Sianipar; Yohanssen Pratama
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2908-2920

Abstract

Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.
Co-Authors Ade Erispra Rismanda Anastasya Manullang Astri Monica Sianturi Bona Simanullang Emma Hannisa Laurencia Nainggolan Ester Marbun Henny Flora Panjaitan I Gde Eka Dirgayussa Josua Rumahorbo Lit Malem Ginting Mega Sari Pasaribu Mia Hotmaria Tambunan Mukhammad Solikhin Paian Fernando Simarmata Parmonangan R. Togatorop Pebri Sangmajadi Sinaga Prasetyo, Tegar Purnama Pasaribu Riyanthi Angrainy Sianturi Romauli Sianipar Simanjuntak, Mariana Sinaga, Arnaldo Marulitua Verawaty Situmorang Victor Lambok Desrony Yonatan Parapat