Similarity Comparison and Combination of Leaky ReLU and DeLU Activation Function and Three Optimizers on Deep CNN for Covid-19 Detection

COVID-19 detection is an interesting field of study in the medical world and the commonly used method is classification. In determining the best detection model, several classification architectures, such as SVM, KNN, and CNN were utilized. The CNN is a changeable architecture due to having combinations of varying numbers of hidden layers or different activation and optimizer functions. Therefore, this study uses a deep CNN architecture with a combination of Leaky ReLU activation functions and 3 different optimizers, which include Adagrad, Adadelta, and Adamax. The results showed that the combination of the Leaky ReLU activation function and the Adamax optimizer produced good and stable accuracy in the CRX and CT datasets

Comparison and Combination of Leaky ReLU and ReLU Activation Function and Three Optimizers on Deep CNN for Covid-19 Detection

COVID-19 detection is an interesting field of study in the medical world and the commonly used method is classification. In determining the best detection model, several classification architectures, such as SVM, KNN, and CNN were utilized. The CNN is a changeable architecture due to having combinations of varying numbers of hidden layers or different activation and optimizer functions. Therefore, this study uses a deep CNN architecture with a combination of Leaky ReLU activation functions and 3 different optimizers, which include Adagrad, Adadelta, and Adamax. The results showed that the combination of the Leaky ReLU activation function and the Adamax optimizer produced good and stable accuracy in the CRX and CT datasets

Perbandingan 5 Jarak K-Nearest Neighbor pada Analisis Sentimen

K-Nearest Neighbor (KNN) merupakan algoritma yang biasa digunakan untuk klasifikasi. Penelitian ini menggunakan ulasan aplikasi Maxim di Google Play Store. Pengguna yang sudah mengunduh aplikasi Maxim berhak memberikan ulasan di Google Play Store guna berbagi informasi untuk pengguna lain. Implementasi K-Nearest Neighbor (KNN) terhadap Sentiment Analysis ulasan aplikasi Maxim dapat digunakan untuk menentukan kelas ulasan bernilai positif, neutral, atau negatif. Peneliti melakukan perbandingan 5 jarak yang berbeda untuk metode KNN yaitu jarak Euclidean, Manhattan, Minkowski, Chebyshev dan Canberra. Pengujian yang telah dilakukan memberikan hasil akurasi pada klasifikasi KNN dengan jarak yang berbeda, memberikan hasil akurasi yang berbeda-beda, yaitu jarak Euclidean  84 persen, jarak Manhattan  79 persen, jarak Minkowski 84 persen, jarak Chebyshev  7 persen dan jarak Canberra =44 persen

Deep convolutional generative adversarial networks for data imbalance in convolutional neural networks for facial expression classification

Facial expression recognition technology is a critical direction of emotion computing research, and it is an essential part of human-computer interaction. The facial expression recognition method is a classification method. An excellent classification method and widely used today are the Convolutional Neural Network (CNN). However, there are still shortcomings in accuracy in the CNN method if the available dataset is minimal and imbalanced. There are two ways to overcome this, adding the training data or changing the architecture on CNN. In this research, the researcher uses the method to add to the training dataset using the Deep Convolutional Generative Adversarial Networks (DCGAN) method