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All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics
Sri Herawati
University of Trunojoyo Madura
Computer Science & IT Electrical & Electronics Engineering

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Indonesian license plate recognition based on area feature extraction Fitri Damayanti; Sri Herawati; Imamah Imamah; Fifin Ayu M; Aeri Rachmad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.9017

Abstract

The main principle of license plate recognition is to recognize the characters in the license plate which indicates the identity of the vehicle. This research will provide a system which can be implemented to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has certain characters. These characters may become problem in the recognition process. Another problem is on the type of the license plate since Indonesia applies different color for every type of vehicle. In this research, different approaches are employed in the recognition of license plate; that is using character area as the feature value, also known as feature area, and K-Nearest Neighbor (KNN) as classification method. In addition, another method that has been used in our previous research is also employed to detect the character of license plate. The result shows very significant accuracy of 99.44%. In the process of recognition, scenario 1 gives the best accuracy at the K-1 value; that is 68.57% on the license plate and 92.72% on the characters of license plate. In the scenario 2 was obtained the license plate accuracy of 52% and license plate character accuracy of 89.36% with K-5. The system ran in a relatively short computational time.
Combined EEMD and ANN improved by GA for tourist visit forecasting Muhammad Latif; Sri Herawati
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3566

Abstract

This study has proposed forecasting tourist visits use an ensemble empirical mode decomposition (EEMD) and optimized artificial neural networks (ANN) using genetic algorithms (GA). The data used is monthly data on tourist visits in Sumenep Regency. The data was obtained from the Sumenep district government from January 2015 to December 2019. EEMD algorithm breaks down tourist visit data into several intrinsic mode function (IMF) and residues. Then, EEMD results was normalized and then learned using ANN. GA is used to optimize weight and bias of the ANN. Experiments carried out to analyze performance in forecast results of proposed method compared with the EEMD-ANN without optimization of the GA. The experimental results show that the proposed method has better performance, namely the error value is reduced by 37%, 21% for MSE, RMSE, respectively.
Co-Authors Aeri Rachmad Fifin Ayu M Fitri Damayanti Imamah Imamah Muhammad Latif