Deteksi Citra Kendaraan Berbasis Web Menggunakan Javascript Framework Library
DOI:
https://doi.org/10.30812/matrik.v18i1.325Keywords:
Internet of things, javascript detection library, kemacetan jalanAbstract
Populasi penduduk yang semakin berkembang menyebabkan tingginya kemacetan jalan. Kemacetan terjadi karena jumlah kendaraan yang beroperasi tidak sebanding dengan volume jalan yang tersedia. Pemerintah sebagai pelaksana pelayanan bagi masyarakat belum mampu memberikan informasi lalu lintas yang memadai. Masyarakat membutuhkan informasi kondisi lalu lintas secara realtime, otomatis dan mudah diakses menggunakan internet. Penelitian ini bertujuan untuk memberikan informasi jumlah kendaraan dengan konsep internet of things menggunakan javascript detection library. Penelitian ini terdiri dari beberapa tahapan penelitian antara lain: survei & identifikasi, analisis data, training data, dan pengembangan aplikasi menggunakan javascript framework library. Hasil yang didapatkan adalah aplikasi mampu mendeteksi citra kendaraan berdasarkan xml training dari Matlab sebesar 95%.
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References
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[3] T. Tambunan, “Kondisi Infrastruktur Indonesia.†Kadin Indonesia, Apr-2006. W. Sediono and D. Handoko, “Pemodelan dan simulasi antrian kendaraan di gerbang tol,†presented at the Semiloka Teknologi Simul asi dan Komputasi serta Aplikasi, 2004
[4] “Undang Undang No. 14 Tahun 1992 Tentang : Lalu Lintas Dan Angkutan Jalan.†May-1992
[5] “Highway Capacity Manual Indonesia.†Directorate General Bina Marga
[6] M. Z. Irawan, T. Sumi, and A. Munawar, “Implementation of the 1997 Indonesian Highway Capacity Manual (MKJI) Volume Delay Function,†J. East. Asia Soc. Transp. Stud., vol. 8, pp. 350–360, 2010
[7] “Pengukuran Kepadatan Arus Lalu Lintas Menggunakan Sensor Kamera,†ResearchGate.[Online].Available: https://www.researchgate.net/publication/43940251_Pengukuran_Kepadatan _Arus_Lalu_Lintas_Menggunakan_Sensor_Kamera. [Accessed: 09-Jun-2017]
[8] “Intelligent Transportation Systems.†[Online]. Available: http://www.kritikalsolutions.com/intelligent-transportation-systems. [Accessed: 09-Jun-2017]
[9] S. Taghvaeeyan and R. Rajamani, “Portable Roadside Sensors for Vehicle
[10] Counting, Classification, and Speed Measurement,†IEEE Trans. Intell. Transp. Syst., vol. 15, no. 1, pp. 73–83, Feb. 2014
[11] L. Shoufeng, W. Jie, H. van Zuylen, and L. Ximin, “Deriving the Macroscopic Fundamental Diagram for an urban area using counted flows and taxi GPS,†in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013, pp. 184–188
[12] P. Srinivas and Y. L. Malathilatha, Image Processing Edge Detection Technique used for Traffic Control Problem
[13] R. Zhang, Z. Li, C. Feng, and S. Jiang, “Traffic Routing Guidance Algorithm Based on Backpressure with a Trade-Off between User Satisfaction and Traffic Load,†in 2012 IEEE Vehicular Technology Conference (VTC Fall), 2012, pp. 1–5
[14] A. Padiath, L. Vanajakshi, S. C. Subramanian, and H. Manda, “Prediction of traffic density for congestion analysis under Indian traffic conditions,†in 12th International IEEE Conference on Intelligent Transportation Systems, 2009. ITSC ’09, 2009, pp. 1–6.
Additional Files
Published
2018-11-30
Issue
Section
Articles
How to Cite
[1]
J. Budiarto and J. Qudsi, “Deteksi Citra Kendaraan Berbasis Web Menggunakan Javascript Framework Library”, MATRIK, vol. 18, no. 1, pp. 125–133, Nov. 2018, doi: 10.30812/matrik.v18i1.325.
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