Research on biometric technology get much attention from researchers who interest in the recognition system. One of the biometric objects that will continue to be developed is the palmprint. The hand palm line has a unique characteristic in each person or may not be the same. The palmprint image is easy to capture because clearly visible, so it does not require a specific sensor. This paper presents the automatic extraction feature with Convolutional Neural Network (CNN) technique to get a unique characteristic of palmprint image and identify a person. CNN will get easier to classify the image database if it has many data. CNN belongs to Supervised Learning, which requires training data to create a knowledge base. In the dataset with little training data, the system must increase the training data using augmentation methods like zoom, shear, and rotate. Still, in the palmprint, that augmentation method can change the original character of the palmprint. Our proposed method is adding training data with an edge detection image from the original image. Edge detection used in our method is Canny and Sobel. The addition of Canny and Sobel edge detection for training data is the best combination scenario for palmprint recognition. The experiment results showed that palmprint recognition using Convolution Neural Network with Canny and Sobel edge detection for training data resulted in an accuracy rate of 96.5% for 200 classes, and the Equal Error Rate (ERR) value is 3.5%. This method has been able to recognize 193 palms of 200 people.
E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.
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