Modifikasi Ant Colony Optimization Berdasarkan Gradient Untuk Deteksi Tepi Citra
DOI:
https://doi.org/10.24002/jbi.v6i3.435Abstract
Abstract. Ant Colony Optimization (ACO) is an optimization algorithm which can be used for image edge detection. In traditional ACO, the initial ant are randomly distributed. This condition can cause an imbalance ants distribution. Based on this problem, a modified ant distribution in ACO is proposed to optimize the deployment of ant based gradient. Gradient value is used to determine the placement of the ants. Ants are not distributed randomly, but are placed in the highest gradient. This method is expected to be used to optimize the path discovery. Based on the test results, the use of the proposed ACO modification can obtain an average value of the Peak Signal to Noise Ratio (PSNR) of 12.724. Meanwhile, the use of the traditional ACO can obtain an average value of PSNR of 12.268. These results indicate that the ACO modification is capable of generating output image better than traditional ACO in which ants are initially distributed randomly.
Keywords: Ant Colony Optimization, gradient, Edge Detection, Peak Signal to Noise Ratio
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Abstrak. Ant Colony Optimization (ACO) merupakan algoritma optimasi, yang dapat digunakan untuk deteksi tepi pada citra Pada ACO tradisional, semut awal disebarkan secara acak. Kondisi ini dapat menyebabkan ketidakseimbangan distribusi semut. Berdasarkan permasalahan tersebut, modifikasi distribusi semut pada ACO diusulkan untuk mengoptimalkan penempatan semut berdasarkan gradient. Nilai gradient digunakan untuk menentukan penempatan semut. Semut tidak disebar secara acak akan tetapi ditempatkan di gradient tertinggi. Cara ini diharapkan dapat digunakan untuk optimasi penemuan jalur. Berdasarkan hasil uji coba, dengan menggunakan ACO modifikasi yang diusulkan dapat diperoleh nilai rata-rata Peak Signal to Noise Ratio (PSNR) 12,724. Sedangkan, menggunakan ACO tradisional diperoleh nilai rata-rata PSNR 12,268. Hasil ini menunjukkan bahwa ACO modifikasi mampu menghasilkan citra keluaran yang lebih baik dibandingkan ACO tradisional yang sebaran semut awalnya dilakukan secara acak.
Kata Kunci: Ant Colony Optimization, gradient, deteksi tepi, Peak Signal to Noise Ratio
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