Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak
DOI:
https://doi.org/10.25126/jtiik.2022945768Abstrak
Deteksi kendaraan bergerak adalah salah satu elemen penting dalam aplikasi Intelligent Transport System (ITS). Deteksi kendaraan bergerak juga merupakan bagian dari pendeteksian benda bergerak. Metode K-Means berhasil diterapkan pada piksel cluster yang tidak diawasi untuk mendeteksi objek bergerak. Secara umum, K-Means adalah algoritma heuristik yang mempartisi kumpulan data menjadi K cluster dengan meminimalkan jumlah kuadrat jarak di setiap cluster. Dalam makalah ini, algoritma K-Means menerapkan jarak Euclidean, jarak Manhattan, jarak Canberra, jarak Chebyshev dan jarak Braycurtis. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi implementasi jarak tersebut pada algoritma clustering K-Means. Perbandingan dilakukan dengan basis K-Means yang dinilai dengan berbagai parameter evaluasi yaitu MSE, PSNR, SSIM dan PCQI. Hasilnya menunjukkan bahwa jarak Manhattan memberikan nilai MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 dan PCQI = 0.79 terbaik dibandingkan dengan jarak lainnya. Sedangkan untuk waktu pemrosesan data memperlihatkan bahwa jarak Braycurtis memiliki keunggulan lebih yaitu 0.3 detik.
Abstract
Detection moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detection moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 and PCQI = 0.79 values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages
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