Implementasi Artificial Neural Network dalam Identifikasi Fatalitas Kecelakaan Lalu Lintas (Studi Kasus: Kota Leeds-Inggris)
Article Sidebar
Published:
Oct 1, 2023
Keywords:
Artificial Neural Networks, Fatality Analysis, Traffic Accident, Leeds UK
Scite_ AI Article:
Connected Papers:
Statistics:
Share Article:
Main Article Content
Abstract
Traffic accidents are a serious worldwide problem, including in Leeds, England. The high fatality rate of traffic accidents is a significant challenge in improving road safety. Therefore, this research aims to implement artificial neural networks in analyzing the factors contributing to traffic accident fatalities in Leeds. The method used in this research involves collecting data of traffic accidents from 2009 to 2018 in the town of Leeds. This method was chosen because artificial neural networks can perform complex and in-depth analyses of large and complex data. This research concludes that artificial neural networks can be used as an effective tool in analyzing traffic accident data and helping policymakers improve road safety in Leeds and possibly elsewhere.
Downloads
Download data is not yet available.
Article Details
How to Cite
Aryatama, A. A., & Wowor, A. D. (2023). Implementasi Artificial Neural Network dalam Identifikasi Fatalitas Kecelakaan Lalu Lintas (Studi Kasus: Kota Leeds-Inggris). Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(4), 651–660. https://doi.org/10.35870/jtik.v7i4.1102
Section
Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Imtihan, K. and Fahmi, H., 2020. Analisis Dan Perancangan Sistem Informasi Daerah Rawan Kecelakaan Dengan Menggunakan Geographic Information Systems (GIS). Jurnal Manajemen Informatika dan Sistem Informasi, 3(1), pp.16-23. DOI: https://doi.org/10.36595/misi.v3i1.128.
An Anisarida, A. and Janizar, S.J., 2020. Besaran Biaya Korban Kecelakaan Sepeda Motor di Kota Bandung. GEOPLANART, 2(2), pp.62-74. DOI: http://dx.doi.org/10.35138/gp.v2i2.181.
Liliana, D.Y., Maulana, H. and Setiawan, A., 2021. Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes. Multinetics, 7(1), pp.48-53. DOI: https://doi.org/10.32722/multinetics.v7i1.3786.
Agus, S., 2019, October. Kajian Model Andreassen 1985 Untuk Prediksi Fatalitas Korban Kecelakaan Lalu Lintas Di Indonesia. In Prosiding Forum Studi Transportasi antar Perguruan Tinggi. pp. 19–20.
Fahmi, K., 2021. Faktor Penyebab Kecelakaan Lalu lintas dan perilaku berkendara pada Siswa Sekolah Menengah Atas di Pasir Pengaraian Riau. Jurnal Ilmiah Cano Ekonomos, 10(1), pp.1-10. DOI: https://doi.org/10.30606/cano.v10i1.1084.
Farida, I. and Santosa, W., 2018. Keselamatan angkutan bus di Kabupaten Garut. Jurnal Transportasi, 18(3), pp.211-218. DOI: https://doi.org/10.26593/jtrans.v18i3.3159.211-218.
Pinata, N.P., Sukarsa, I.M. and Rusjayanthi, N.D., 2020. Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python. Jurnal Ilmiah Merpati, 8(3), pp.188-196. DOI: https://doi.org/10.24843/jim.2020.v08.i03.p04.
Hadianto, N., Novitasari, H.B. and Rahmawati, A., 2019. Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), pp.163-170. DOI: https://doi.org/10.24843/jim.2020.v08.i03.p04.
Zhou, J., Wang, H., Wei, J., Liu, L., Huang, X., Gao, S., Liu, W., Li, J., Yu, C. and Li, Z., 2019. Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects. Optics express, 27(22), pp.32210-32216. DOI: https://doi.org/10.1364/OE.27.032210.
Pamungkas, I., 2022. Studi Komparasi Fungsi Aktivasi Sigmoid Biner, Sigmoid Bipolar dan Linear pada Jaringan Saraf Tiruan dalam Menentukan Warna RGB Menggunakan Matlab. Jurnal Serambi Engineering. 7(4), pp. 3749-3758. DOI: https://doi.org/10.32672/jse.v7i4.4776.
Rynkiewicz, J., 2019. Asymptotic statistics for multilayer perceptron with ReLU hidden units. Neurocomputing, 342, pp.16-23. DOI: https://doi.org/10.1016/j.neucom.2018.11.097.
Kurniawan, A.S., 2018. Implementasi Metode Artificial Neural Network Dalam Memprediksi Hasil Ujian Kompetensi Kebidanan (Studi Kasus Di Akademi Kebidanan Dehasen Bengkulu). Pseudocode, 5(1), pp.37-44. DOI: https://doi.org/10.33369/pseudocode.5.1.37-44.
Vujičić, D., Pavlovic, R., Milošević, D., Borislav, D., Randjić, S. and Stojić, D., 2020. Classification of asteroid families with artificial neural networks. 201, pp. 39-48. DOI: http://dx.doi.org/10.2298/SAJ2001039V.
Afaq, S. and Rao, S., 2020. Significance of epochs on training a neural network. Int. J. Sci. Technol. Res, 9(06), pp.485-488.
Regmi, R.H. and Timalsina, A.K., 2018, October. Risk Management in customs using Deep Neural Network. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 133-137). IEEE. DOI: https://doi.org/10.1109/CCCS.2018.8586834
An Anisarida, A. and Janizar, S.J., 2020. Besaran Biaya Korban Kecelakaan Sepeda Motor di Kota Bandung. GEOPLANART, 2(2), pp.62-74. DOI: http://dx.doi.org/10.35138/gp.v2i2.181.
Liliana, D.Y., Maulana, H. and Setiawan, A., 2021. Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes. Multinetics, 7(1), pp.48-53. DOI: https://doi.org/10.32722/multinetics.v7i1.3786.
Agus, S., 2019, October. Kajian Model Andreassen 1985 Untuk Prediksi Fatalitas Korban Kecelakaan Lalu Lintas Di Indonesia. In Prosiding Forum Studi Transportasi antar Perguruan Tinggi. pp. 19–20.
Fahmi, K., 2021. Faktor Penyebab Kecelakaan Lalu lintas dan perilaku berkendara pada Siswa Sekolah Menengah Atas di Pasir Pengaraian Riau. Jurnal Ilmiah Cano Ekonomos, 10(1), pp.1-10. DOI: https://doi.org/10.30606/cano.v10i1.1084.
Farida, I. and Santosa, W., 2018. Keselamatan angkutan bus di Kabupaten Garut. Jurnal Transportasi, 18(3), pp.211-218. DOI: https://doi.org/10.26593/jtrans.v18i3.3159.211-218.
Pinata, N.P., Sukarsa, I.M. and Rusjayanthi, N.D., 2020. Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python. Jurnal Ilmiah Merpati, 8(3), pp.188-196. DOI: https://doi.org/10.24843/jim.2020.v08.i03.p04.
Hadianto, N., Novitasari, H.B. and Rahmawati, A., 2019. Klasifikasi Peminjaman Nasabah Bank Menggunakan Metode Neural Network. Jurnal Pilar Nusa Mandiri, 15(2), pp.163-170. DOI: https://doi.org/10.24843/jim.2020.v08.i03.p04.
Zhou, J., Wang, H., Wei, J., Liu, L., Huang, X., Gao, S., Liu, W., Li, J., Yu, C. and Li, Z., 2019. Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects. Optics express, 27(22), pp.32210-32216. DOI: https://doi.org/10.1364/OE.27.032210.
Pamungkas, I., 2022. Studi Komparasi Fungsi Aktivasi Sigmoid Biner, Sigmoid Bipolar dan Linear pada Jaringan Saraf Tiruan dalam Menentukan Warna RGB Menggunakan Matlab. Jurnal Serambi Engineering. 7(4), pp. 3749-3758. DOI: https://doi.org/10.32672/jse.v7i4.4776.
Rynkiewicz, J., 2019. Asymptotic statistics for multilayer perceptron with ReLU hidden units. Neurocomputing, 342, pp.16-23. DOI: https://doi.org/10.1016/j.neucom.2018.11.097.
Kurniawan, A.S., 2018. Implementasi Metode Artificial Neural Network Dalam Memprediksi Hasil Ujian Kompetensi Kebidanan (Studi Kasus Di Akademi Kebidanan Dehasen Bengkulu). Pseudocode, 5(1), pp.37-44. DOI: https://doi.org/10.33369/pseudocode.5.1.37-44.
Vujičić, D., Pavlovic, R., Milošević, D., Borislav, D., Randjić, S. and Stojić, D., 2020. Classification of asteroid families with artificial neural networks. 201, pp. 39-48. DOI: http://dx.doi.org/10.2298/SAJ2001039V.
Afaq, S. and Rao, S., 2020. Significance of epochs on training a neural network. Int. J. Sci. Technol. Res, 9(06), pp.485-488.
Regmi, R.H. and Timalsina, A.K., 2018, October. Risk Management in customs using Deep Neural Network. In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) (pp. 133-137). IEEE. DOI: https://doi.org/10.1109/CCCS.2018.8586834