ANALISA DAN PERBANDINGAN AKURASI MODEL PREDIKSI RENTET WAKTU ARUS LALU LINTAS JANGKA PENDEK

Bambang Lareno

Sari


Abstrak
Terdapat banyak algoritma yang dapat dipakai untuk memprediksi arus lalu lintas, namun belum diketahui algoritma manakah yang memiliki kinerja lebih akurat untuk lalu lintas di Indonesia. Algoritma-algoritma tersebut perlu diuji untuk mengetahui algoritma manakah yang memiliki kinerja lebih akurat. Metode yang diusulkan adalah metode perbandingan tingkat akurasi dari algoritma berbasis neural network yang bisa digunakan untuk prediksi data rentet waktu. Algoritma yang akan diuji adalah back Propagation Neural Network (BP-NN), Adaptive Neuro Fuzzy Inference System (ANFIS), Wavelet Neural Network (WNN), dan Evolving Neural Network (ENN), yang digunakan untuk memprediksi arus lalulintas. Masing-masing algoritma akan implementasikan dengan menggunakan MatLab 2009b. Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) dan Mean Absolute Deviation (MAD). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dalam penelitian ini diketahui bahwa Algoritma ENN memprediksi arus lalu lintas dengan lebih akurat.


Kata Kunci


Prediksi Arus Lalu Lintas, Peramalan Rentet Waktu.

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Referensi


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DOI: http://dx.doi.org/10.22303/csrid.6.3.2014.148-158

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