Prediksi Kemacetan Lalu Lintas Menggunakan Algoritma Random Forest
- Raditia Vindua Universitas Pamulang, Tangerang Selatan, Indonesia
- Salsa Sayida Bilqis Universitas Pamulang, Tangerang Selatan, Indonesia
- Hamdan Qo’du Ilal Hakim Universitas Pamulang, Tangerang Selatan, Indonesia
-
Rido Ramadhani
*
Universitas Pamulang, Tangerang Selatan,
Indonesia
(*) Corresponding Author
Abstract
Traffic congestion is a major issue in urban and suburban areas such as Pamulang, South Tangerang, caused by increasing vehicle volume without proportional road infrastructure development. This study aims to predict traffic congestion levels using the Random Forest algorithm and identify the dominant factors influencing congestion. The research utilized the Smart Traffic Management Dataset containing 2,000 observations with 12 variables such as average speed, traffic volume, weather conditions, and accident reports. The analysis process included data cleaning, categorical variable encoding, data splitting (80% training and 20% testing), and model validation using the 5-fold cross-validation technique. The results showed that the Random Forest model achieved 95,75% accuracy, with a precision of 0.96 and recall of 0.96. The feature importance analysis indicated that the average vehicle speed variable had the highest influence on congestion levels, followed by traffic volume and accident reports. These findings demonstrate that the Random Forest algorithm is highly effective in identifying traffic patterns and can serve as a foundation for developing intelligent transportation systems in the Pamulang area to reduce congestion adaptively and in real time.
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References
Dima, J., Hamzah, Moh. S., Tallo, C. G., & Fallo, D. Y. A. (2025). Tinjauan Literatur tentang Pemanfaatan Algoritma Greedy untuk Pencarian Jalur Terpendek. Jurnal Kridatama Sains Dan Teknologi, 7(01), 519–528. https://doi.org/10.53863/kst.v7i01.1683
Farhan Raihan Wahidin, Wina Witanti, & Edvin Ramadhan. (2025). Pengontrolan Lampu Lalu Lintas Menggunakan Teknologi Deteksi Kendaraan YOLOV4. Jurnal Teknologi Informasi Dan Ilmu Komputer, 12(5), 975–984. https://doi.org/https://doi.org/10.25126/jtiik.2025125
Febrianto, M. F., Priyatno, A., Adisty, H., Saputri, A. F., Amanullah, R., Pebrianti Krissella, T., Irzavika, N., & Matondang, N. H. (2024). Prediksi Situasi Lalu Lintas Menggunakan Machine Learning Dengan Algoritma K-Nearest Neighbors Classifier. Informatik : Jurnal Ilmu Komputer, 20(1), 28–34. https://doi.org/10.52958/iftk.v20i1.7042
Gullam Almuzadid, & Egia Rosi Subhiyakto. (2025). Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest. Journal of Applied Informatics and Computing, 9(3), 828–837. https://doi.org/10.30871/jaic.v9i3.9528
Kiswanto, D., Ramadhani, F., Surbakti, N. M., & Nasution, N. A. (2025). Pengembangan dan Implementasi Sistem Deteksi Serangan DDoS Berbasis Algoritma Random Forest. 6(3). https://doi.org/10.47065/bit.v5i2.2203
Kurniawan, W. A., & Salam, M. K. A. (2025). Penggunaan Feature Space SMOTE Untuk Mengurangi Overfitting Akibat Imbalance Dataset. Jurnal Transformatika, 22(2), 140–149. https://doi.org/10.26623/transformatika.v22i2.8305
Kusuma, M. R., Informasi, P. S., Mandiri, U., Sistem, P., Informasi, T., Darunnajah, U., Seleksi, F., & Learning, A. M. (2025). Pendekatan Analisis Prediktif Regresi Menggunakan Metode Pembelajaran Mesin Untuk Memperkirakan Effort. 10(1), 23–34.
Mulyani, S., & Arifin, T. (2025). Prediksi Kelangsungan Hidup Pasien Gagal Jantung Menggunakan Pendekatan Machine Learning dengan Optimasi GridSearchCV. Jurnal Informatika Polinema, 11(4), 577–586. https://doi.org/10.33795/jip.v11i4.7938
Niron, W. P., Kuswara, K. M., Paul, D., & Tamelan, G. (n.d.). Faktor-Faktor Penyebab Kemacetan Lalu Lintas Di Ruas Jalan Herman Fernandez Larantuka Kabupaten Flores Timur Factors Causing Traffic Construction On Herman Fernandez Road, East Flores District. Jurnal Teknologi, 19(1), 2025. https://doi.org/https://ejurnal.undana.ac.id/index.php/jurnal_teknologi/article/view/22338
Pranadjaya, E., Pangestu, E. S., Sereati, C. O., Octaviani, S., & Darmawan, M. (2024). Perbandingan Algoritma Machine Learning menggunakan Orange Data Mining untuk Klasifikasi Jenis Kendaraan pada Sistem Tilang Digital. Jurnal Elektro, 17(1), 41–47. https://doi.org/10.25170/jurnalelektro.v17i1.5429
Pranata, J., Agustian, S., Jasril, J., & Haerani, E. (2024). Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas. Building of Informatics, Technology and Science (BITS), 6(3), 1668–1676. https://doi.org/10.47065/bits.v6i3.6335
Rahayu, S., Asmoro, B. R., & Rinal, E. (2023). Classification of Congestion in Jakarta Using KNN, Naïve Bayes and Decision Tree Method. Jurnal Syntax Admiration, 4(7), 928–952. https://doi.org/10.46799/jsa.v4i7.654
Rodji, A. P., Studi, P., Ilmu, M., Nusa, U., Jakarta, M., Melayu, J. C., Timur, K. J., Ibukota, D. K., Sipil, S. T., Teknik, F., Krisnadwipayana, U., Gede, P., Bekasi, K., Tree, D., & Keparahan, K. (2025). Klasifikasi Keparahan Kecelakaan Lalu Lintas Menggunakan Machine Learning. 10(1), 82–92.
Yao, J., & Ye, Y. (2020). The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety. Image and Vision Computing, 103, 103971. https://doi.org/10.1016/j.imavis.2020.103971
Zul, Z., Al, H., & Subhiyakto, E. R. (2025). Analisis Komparatif Akurasi Prediksi Kanker Payudara Menggunakan Algoritma Random Forest dan Logistic Regression. 300–311. https://doi.org/10.33364/algoritma/v.22-1.2164
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