Penerapan Algoritma Knn (K-Nearest Neighbors) Dalam Penentuan Lampu Lalu Lintas Di Persimpangan  Melalui Data Kendaraan Yang Melintas Di  Persimpagan Yang Ada Di Kota Medan  Untuk Mengurangi Kemacatan

Muhammad Devri Kazza Nasution; Zuli Agustina Gultom

Authors

Muhammad Devri Kazza Nasution Universitas Muhammadiyah Sumatera Utara
Zuli Agustina Gultom Universitas Muhammadiyah Sumatera Utara

Keywords:

K-Nearest Neighbors, Lampu Lalu Lintas, Kemacetan, Persimpangan, Machine Learning

Abstract

Kemacetan pada persimpangan di Kota Medan dipengaruhi oleh volume kendaraan yang tinggi dan pengaturan lampu lalu lintas dengan waktu tetap. Penelitian ini menerapkan algoritma K-Nearest Neighbors (KNN) untuk mengklasifikasikan kondisi lalu lintas dan mendukung penentuan durasi lampu berdasarkan data kendaraan yang melintas. Penelitian menggunakan pendekatan kuantitatif dengan data kendaraan dari Dinas Perhubungan Kota Medan dan pengujian sistem berbasis web. Kinerja model dievaluasi menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Hasil menunjukkan accuracy 94,00% pada kelas merah, 92,67% pada kelas kuning, dan 94,00% pada kelas hijau. Precision masing-masing kelas sebesar 90,20%, 89,00%, dan 91,84%; recall sebesar 92,00%, 89,00%, dan 90,00%; sedangkan F1-score sebesar 91,09%, 89,00%, dan 90,91%. Pengujian fungsional menunjukkan seluruh fitur utama sistem berhasil dijalankan. Hasil penelitian menunjukkan bahwa KNN dapat digunakan sebagai dasar sistem pendukung keputusan pengaturan lampu lalu lintas yang lebih adaptif

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