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Penelitian ini bertujuan untuk mengembangkan sistem adaptif yang dapat mengatur lampu lalu lintas secara dinamis berdasarkan perhitungan kendaraan. Teknologi YOLOv8 berbasis Segmentation digunakan untuk mendeteksi jenis kendaraan dari video rekaman CCTV di Simpang Tiga Sabilillah, Kota Malang. Hasil deteksi menunjukkan bahwa YOLOv8 memiliki tingkat akurasi tertinggi sebesar 97,7% pada kelas truk dan 97,1% pada kelas bus. Namun, F1 Score yang merupakan metrik evaluasi model klasifikasi yang menghitung keseimbangan antara ketepatan prediksi (Precision) dan kemampuan menemukan semua kasus positif (Recall) melalui rata-rata harmonis keduanya mendapatkan nilai untuk kelas mobil dan sepeda motor masing-masing hanya mencapai 84,6% dan 69,3%, menunjukkan adanya tantangan dalam mendeteksi kendaraan yang lebih kecil atau lebih bervariasi. Sistem ini juga menggunakan AnyLogic sebagai simulator untuk mensimulasikan aliran lalu lintas dan mengatur prioritas lampu secara otomatis sesuai dengan kondisi aktual. Dengan implementasi sistem yang lebih adaptif berdasarkan keadaan aktual banyaknya kendaraan dalam pengelolaan lalu lintas di Kota Malang, khususnya di Simpang Tiga Sabilillah, dapat menjadi lebih efisien dan efektif dalam mengurangi kemacetan.","\u003Cp>Kemacetan lalu lintas merupakan tantangan signifikan di Kota Malang, terutama karena lampu lalu lintas masih menggunakan pengaturan waktu tetap (fixed time) yang tidak adaptif terhadap kondisi aktual di lapangan. Penelitian ini bertujuan untuk mengembangkan sistem adaptif yang dapat mengatur lampu lalu lintas secara dinamis berdasarkan perhitungan kendaraan. Teknologi YOLOv8 berbasis Segmentation digunakan untuk mendeteksi jenis kendaraan dari video rekaman CCTV di Simpang Tiga Sabilillah, Kota Malang. Hasil deteksi menunjukkan bahwa YOLOv8 memiliki tingkat akurasi tertinggi sebesar 97,7% pada kelas truk dan 97,1% pada kelas bus. Namun, F1 Score yang merupakan metrik evaluasi model klasifikasi yang menghitung keseimbangan antara ketepatan prediksi (Precision) dan kemampuan menemukan semua kasus positif (Recall) melalui rata-rata harmonis keduanya mendapatkan nilai untuk kelas mobil dan sepeda motor masing-masing hanya mencapai 84,6% dan 69,3%, menunjukkan adanya tantangan dalam mendeteksi kendaraan yang lebih kecil atau lebih bervariasi. Sistem ini juga menggunakan AnyLogic sebagai simulator untuk mensimulasikan aliran lalu lintas dan mengatur prioritas lampu secara otomatis sesuai dengan kondisi aktual. 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