Implementasi Artificial Neural Network dalam Identifikasi Fatalitas Kecelakaan Lalu Lintas (Studi Kasus: Kota Leeds-Inggris)
DOI:
https://doi.org/10.35870/jtik.v7i4.1102Keywords:
Artificial Neural Networks, Fatality Analysis, Traffic Accident, Leeds UKAbstract
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
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Andrew Ananta Aryatama, Alz Danny Wowor
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
