Analisis Klasifikasi Kecelakaan Lalu Lintas di KotaPalembang Menggunakan Algoritma Decision Tree
Abstract
Kecelakaan lalu lintas merupakan permasalahan serius
dengan dampak sosial dan ekonomi yang signifikan. Penelitian ini
bertujuan untuk melakukan klasifikasi jenis kecelakaan serta
mengetahui pola-pola yang muncul berdasarkan karakteristik
kecelakaan di Kota Palembang tahun 2021–2024 dengan beberapa
pendekatan machine learning. Dataset berisi 2.637 baris data dengan
fitur-fitur seperti tipe kecelakaan, kondisi cahaya, cuaca, fungsi jalan,
kelas jalan, tipe jalan, bentuk geometri, dan kondisi permukaan jalan.
Setelah tahap prapemrosesan dan seleksi fitur, dilakukan pemodelan
menggunakan Decision Tree, Random Forest, K-Nearest Neighbors
(KNN), dan XGBoost. Hasil evaluasi menunjukkan bahwa model
Decision Tree memiliki performa terbaik dengan akurasi 0,64. Analisis
juga mengindikasikan bahwa kendaraan roda dua paling banyak
terlibat dalam kecelakaan, terutama di jalan arteri saat cuaca cerah.
Hasil penelitian ini diharapkan dapat menjadi bahan pertimbangan
kebijakan peningkatan keselamatan lalu lintas di Palembang.
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