Penerapan Amazon Quicksight dalam Menganalisis Data Kecelakaan di Kota New York

Authors

  • Annisha Maharany Universitas Sebelas April
  • David Setiadi Universitas Sebelas April

DOI:

https://doi.org/10.55606/jtmei.v4i1.4818

Keywords:

Amazon QuickSight, Analysis, Recommendations, Traffic Accidents

Abstract

This research emphasizes the use of Amazon QuickSight as a data visualization tool to analyze and generate recommendations based on traffic accident data. The case study was conducted on crash data in New York City for 2022, with a focus on identifying crash patterns and key causal factors. This research uses a methodology consisting of understanding business problems, data cleaning and processing, data analysis & insight gathering, and data visualization. The dataset analyzed includes 100,280 accident records, with variables such as ID, borough, street, contributing factors, vehicle type, date, person injured, person killed, pedestrians injured, pedestrians killed, cyclists injured, cyclists killed, motorists injured, motorists killed. Analysis results using Amazon QuickSight revealed significant accident patterns, such as a concentration of incidents in certain areas during peak hours. Recommendations include optimizing road design, improving traffic monitoring, and driver education to improve safety. This research shows the potential of Amazon QuickSight in supporting data-driven decision making for traffic policy and crash prevention measures in urban environments.

Downloads

Download data is not yet available.

References

Abdel-aty, M., & Ding, S. (2024). A matched case-control analysis of autonomous vs human-driven vehicle accidents. Nature Communications. https://doi.org/10.1038/s41467-024-48526-4

Abdouraman, B., Feudjo, &, & Roger, J. (2020). L’influence de la désirabilité perçue et de la faisabilité perçue sur la formation de l’intention entrepreneuriale des diplômés de l’enseignement supérieur au Cameroun.

Ady Bakri, A., & Botutihe, N. (2023). Analisis efektivitas penggunaan teknologi big data dalam proses audit: Studi kasus pada kantor akuntan publik di Indonesia. Jurnal Akuntansi dan Keuangan West Science, 2(03).

Ananda Lubis, F., Studi Manajemen, P., Ekonomi dan Bisnis Islam, F., & Irwan Padli Nasution, M. (2024). Penggunaan teknologi big data untuk analisis prediksi bisnis. Jurnal Ilmiah Nusantara (JINU), 1(4), 3047–9673. https://doi.org/10.61722/jinu.v1i4.1882

Facts, T. S. (2022). Speed-related crashes.

Feni, S., Mubalus, E., Analisis, /, Penyebab, F.-F., Lalu, K., Di, L., & Sorong, K. (2023). Analisis faktor-faktor penyebab kecelakaan lalu lintas di Kabupaten Sorong dan penanggulangannya. 6(1).

Hughes, J. E., Kaffine, D., & Kaffine, L. (2023). Decline in traffic congestion increased crash severity in the wake of COVID-19. 2677(4), 892–903. https://doi.org/10.1177/03611981221103239

Oktatriani, A., Destyana Putri, C., & Terttiaavini, D. (2023). Peran analisis big data dalam sektor industri di Indonesia. Jurnal Nasional Komputasi dan Teknologi Informasi, 6(3).

Penyebab, A., Lintas, K. L., Jalan, D., Kota, E., Fauzi, M., Aryatama, Z., & Widhiarto, H. (n.d.). Jurnal teknik sipil: Rancang bangun. http://ejournal.um-sorong.ac.id/index.php/rancangbangun

Rahmi, Q., Ramadhana Kamal, S., Nabilla, S., Atailah, I., & Salat, J. (2023). Analisis big data pada aplikasi e-commerce dengan pendekatan human dan komputer interaction. Jurnal Literasi Informatika, 2(4).

Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: The mediating role of knowledge management practices. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00317-6

Siswo, E., Sahputra, A., & Nendi, I. (2024). Application of big data and analytics to increase competitive advantage. Jurnal Indonesia Sosial Teknologi, 5(5). http://jist.publikasiindonesia.id/

Stoudt, S., Vásquez, V. N., & Martinez, C. C. (2021). Principles for data analysis workflows. PLoS Computational Biology, 17(3). https://doi.org/10.1371/JOURNAL.PCBI.1008770

Yang, X. J., & Jr, L. P. R. (2021). Individual differences and expectations of automated vehicles. Qiaoning Zhang, 1, 1–26.

Downloads

Published

2025-02-06

How to Cite

Annisha Maharany, & David Setiadi. (2025). Penerapan Amazon Quicksight dalam Menganalisis Data Kecelakaan di Kota New York. Jurnal Teknik Mesin, Industri, Elektro Dan Informatika, 4(1), 243–254. https://doi.org/10.55606/jtmei.v4i1.4818

Issue

Vol. 4 No. 1 (2025): JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA

Section

Articles

License

Copyright (c) 2025 Jurnal Teknik Mesin, Industri, Elektro dan Informatika

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.