Peningkatan Kecepatan Algoritma k-NN Untuk Sistem Pengklasifikasian Kendaraan Bermotor
DOI:
https://doi.org/10.33633/tc.v19i2.3458Keywords:
k-nearest neighbor, clustering, klasifikasi jenis kendaraan, mesin pembelajaranAbstract
K-Nearest Neighbor (KNN) merupakan algoritma mesin pembelajaran yang memiliki akurasi yang baik meski sangat sederhana untuk diimplementasikan. Namun, salah satu kelemahan algorima KNN adalah kecepatan komputasi yang sangat tergantung pada jumlah dataset yang dimiliki. Penelitian ini mencoba mengusulkan sebuah strategi untuk meningkatkan kecepatan algoritma KNN tetapi dengan akurasi yang hampir sama dengan standard KNN. Jika pada standar KNN proses hanya dilakukan dengan menyimpan data latih, yang kemudian akan dibandingkan dengan data uji baru dengan cara menghitung jarak satu persatu, sementara strategi yang diusulkan mencoba mengurangi jumlah data latih dengan strategi clustering, sehingga jumlah data yang akan dibandingkan dengan data uji lebih sedikit. Akibatnya, diharapkan waktu prosesnya menjasi lebih cepat. Strategi yang diusulkan akan diterapkan pada kasus klasifikasi jenis kendaraan berbasis pengolahan citra digital. Untuk menghitung tingkat akurasi dan kecepatan, maka metode yang diusulkan akan dievaluasi menggunakan dataset yang dikumpulkan melalui internet.References
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