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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


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.
Efficient Kernel-based Two-Dimensional Principal Component Analysis for Smile Stages Recognition Rima Tri Wahyuningrum; Fitri Damayanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 1: March 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i1.766


 Recently, an approach called two-dimensional principal component analysis (2DPCA) has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Predicting the Final result of Student National Test with Extreme Learning Machine Eka Mala Sari Rochman; Aeri Rachmad; Fitri Damayanti
Pancaran Pendidikan Vol 7, No 2 (2018)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.663 KB) | DOI: 10.25037/pancaran.v7i1.159


The level of student achievement is a benchmark of the quality assessment of a school. This student's assessment is based on the national final exam scores every year. When the national exam score increases, it will affect the number of students who will enroll in a school. It affects the number of classes to be opened in the registration of new student candidates. This study aims to predict student achievement based on the value of subjects that become the focus on the final national examination. One method of forecasting in the Artificial Neural Network (ANN) is the Extreme Learning Machine (ELM). The working principle in this method is basically the same as ANN method in general. Namely, there are input layer, hidden layer and output layer. By randomly assigning the input parameters, the ELM generates good generalization performance. By using 20-20-1 network architecture, this research has a result in a small RMSE value of 0.314.