System Monitoring Traffic Density Application based on Neural Network Algorithms

Ari Wijayanti, Nur Adi Siswandari, Haniah Mahmudah, Okkie Puspitorini, Chusnul Chotimah

Abstract


The increase in vehicle traffic density in a city correlates with increasing private vehicle usage. This problem is caused by the lack of public transport services. In other side, the volume of private vehicles increases the traffic density and because problems are jammed in rush hour traffic. Use of the application can help road users to know the traffic conditions at real time. The research has developed a mobile application of information about traffic condition. This application is created using Android software programming, Android Development Tool (ADT) integrated with Google Maps so it can display information points from jammed location. The information presented in the form of the location name, location coordinates (latitude, longitude), the average vehicle speed (Km / h) which pass through the area and the traffic status in the form of a solid, jammed or smoothly. The data was predicted by Backpropagation Neural Network. The performance has seen on the size of MSE (Mean Square Error). The result is the smallest MSE are 8,91x10-24, it means the chosen method has a predictability that is very close to the actual conditions of traffic situation


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References


https://www.liputan6.com/bisnis/read/2465078/10-kota-dengan-lalu-lintas-terburuk-di-dunia (diakses 20 Juli 2018)

https://urbandigital.id/aplikasi-lokasi-macet-jalan/(diakses pada tanggal 20 juli 2013)

Mihaela Cardei, Iana Zankina, Ionut Cardei, dan Daniel Raviv, “Campus Assistant Application on an Android Platform”, Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University.

sits.dishub.surabaya.go.id

Peraturan Menteri Perhubungan Nomor: KM 14 Tahun 2006 Tentang Manajemen dan Rekayasa Lalu Lintas di Jalan, 2006

Yi-Chung Hu dan Fang-Mei Tseng, “Applying Backpropagation Neural Networks to Bankruptcy Prediction”, International Journal of Electronic Bussiness Management, Vol.3, No. 2, pp. 97-103, 2005

Imam Shabri, Mike Yuliana dan Zaqiatul Darojah, “Prediksi Penggunaan Bandwidth PENS-ITS menggunakan Jaringan Syaraf Tiruan dengan Algoritma Backpropagation”, Jurnal Proyek Akhir Politeknik Elektronika Negeri Surabaya, 2012.

Sri Redjeki, “Analisis Fungsi Aktivasi Sigmoid Algoritma Backpropagation Pada Prediksi Data”, Jurnal Thesis Universitas Gadjah Mada, 2005.

Didi Supriyadi, “Sistem Informasi Penyebaran Penyakit Demam Berdarah Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation”, Thesis Universitas Diponegoro Semarang, 2012.

Daniel Soudry, Itay Hubara dan Ron Meir, “Expectation Backpropagation: Parameter-Free Training Of Multilayer Neural Networks With Continuous Or Discrete Weights”, Department of Statistics, Columbia University, 2014.




DOI: https://doi.org/10.24203/ajas.v6i6.5561

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