Optimalisasi Hyper Parameter Convolutional Neural Networks Menggunakan Ant Colony Optimization
DOI:
https://doi.org/10.25126/jtiik.20241127105Kata Kunci:
auto-tuning hyperparameter, deep neural network, ant colony, genetic algorithmAbstrak
Berbagai bidang, termasuk pertanian dan kesehatan, mengalami masalah klasifikasi citra yang dapat diatasi melalui beberapa metode. Salah satu metode tersebut menggabungkan convolutional neural networks (CNN) dengan deep learning, tetapi hyperparameter, seperti fungsi loss, fungsi aktivasi, dan optimizers, memengaruhi kinerjanya. Hyperparameter ini memerlukan pengoptimalan, dan metode yang ada, seperti algoritma genetika dan pengoptimalan ant colony, dapat digunakan untuk tujuan ini. Pengoptimalan ant colony terbukti efektif dalam mengoptimalkan deep learning, dan penelitian ini berkontribusi pada penyetelan otomatis berbagai hyperparameter menggunakan ant colony untuk klasifikasi gambar. Pada penelitian ini menggunakan dataset MNIST yang bertujuan untuk mengidentifikasi digit pada citra. Dataset yang digunakan terbagi menjadi 2, dataset pelatihan dan dataset validasi. Dataset pelatihan terdiri dari 33.600 gambar, dan dataset validasi terdiri dari 8.400 gambar. Hasil menunjukkan bahwa optimasi ant colony mencapai akurasi 97,46% dengan data validasi dan 99,69% dengan data pelatihan, yang mengungguli algoritma genetika dengan akurasi masing-masing 94,60% dan 97,59% dengan data validasi dan pelatihan. Selain itu, pengoptimalan ant colony membutuhkan waktu 27,94 detik untuk dilatih, sedangkan algoritme genetika membutuhkan 22,25 detik.
Abstract
Various fields, including agriculture and health, have encountered image classification problems that can be addressed through several methods. One such method combines convolutional neural networks (CNN) with deep learning, but hyperparameters, such as loss functions, activation functions, and optimizers, influence its performance. These hyperparameters require optimization, and existing methods, such as genetic algorithms and ant colony optimization, can be utilized for this purpose. Ant colony optimization has shown to be effective in optimizing deep learning, and this research contributes to automatic tuning of various hyperparameters using ant colonies for image classification. In this study using the MNIST dataset, which aims to identify the digits in the image. The dataset used is divided into 2, training dataset and validation dataset. The training dataset consists of 33,600 images, and the validation dataset consists of 8,400 images. The results indicate that ant colony optimization achieves an accuracy of 97.46% with validation data and 99.69% with training data, which outperforms genetic algorithms with an accuracy of 94.60% and 97.59% with validation and training data, respectively. Additionally, ant colony optimization takes 27.94 seconds to train, while the genetic algorithm requires 22.25 seconds.
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