Metode Hibrida K-Means dan Generalized Regression Neural Network Untuk Prediksi Arus Lalu Lintas

Saprina Mamase, Joko Lianto Buliali

Abstract


Abstract. Traffic flow forecasting is a popular research topic in the development of Intelligent Transportation System. There have been many forecasting methods used for traffic flow forecasting, such as Generalized Regression Neural Network (GRNN) which has a fairly good accuracy. One of the GRNN’s characteristics is that the number of neurons in pattern layer increases as the number of training samples raise and this can cause overfitting problem. In this research, a hybrid method to predict traffic flow is proposed, that is K-means and GRNN algorithm. K-means method aims to solve overfitting problem in GRNN model by choosing training samples based on their similar characteristics. Leave One Out Cross Validation (LOOCV) is used to select an appropriate smoothing factor parameter at each GRNN’s model. Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing process. The results show that the proposed method could improve the accuracy of predictions by reducing the value of MAPE by 0.82-3.81%.

Keywords: Traffic flow forecasting, K-means, Generalized Regression Neural Network, Leave One Out Cross Validation

Abstrak. Prediksi arus lalu lintas telah menjadi tren topik penelitian untuk pengembangan sistem transportasi cerdas. Telah banyak metode yang digunakan terkait prediksi arus lalu lintas, diantaranya yaitu Generalized Regression Neural Network (GRNN) yang memiliki akurasi yang cukup baik. Salah satu karakteristik GRNN adalah jumlah neuron pada pattern layer akan bertambah seiring meningkatnya jumlah data latih yang akan mengakibatkan masalah overfitting. Dalam penelitian ini diusulkan metode hibrida K-means dan GRNN untuk prediksi arus lalu lintas. Metode K-means bertujuan untuk mengatasi masalah overfitting pada model GRNN dengan memilih data latih berdasarkan kemiripan karateristiknya. Algoritma Leave One Out Cross Validation (LOOCV) digunakan untuk memilih parameter smoothing factor terbaik pada setiap model GRNN. Mean Absolute Percentage Error (MAPE) digunakan sebagai kriteria evaluasi model prediksi. Hasil menunjukkan bahwa metode yang diusulkan dapat meningkatkan akurasi prediksi dengan penurunan nilai MAPE sebesar 0,82-3,81%.

Kata Kunci: Prediksi arus lalu lintas, K-means, Generalized Regression Neural Network, Leave One Out Cross Validation


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DOI: https://doi.org/10.24002/jbi.v7i3.654

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