Hoax Classification in Indonesian Language with Bidirectional Temporal Convolutional Network Architecture

Abstract

The increasingly massive rate of information dissemination in cyberspace has had several negative impacts, one of which is the increased vulnerability to the spread of hoaxes. Hoax has seven classifications. Classification problems such as hoax classification can be automated using the application of the Deep Learning model. Bidirectional Temporal Convolutional Network (Bi-TCN) is a type of Deep Learning architectural model that is very suitable for text classification cases. Because the architecture uses dilation factors in its feature extraction so it can generate exceptionally large receptive fields and is supported by Bidirectional aggregation to ensure that the model can learn long-term dependencies without storing duplicate context information. The purpose of this study is to evaluate the performance of Bi-TCN architecture combined with pre-trained FastText embedding model for hoax classification in Indonesian and implement the resulting model on website. Based on the research that has been done, the model with Bi-TCN architecture has satisfactory performance with an accuracy score of 92.98% and a loss value that can be reduced to 0.191. Out of a total of 13,673 data tested with this model, only 414 data or in other words around 3% of the total data were incorrect predictions.

Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

“…According to various studies, the advantages of CNN include the capacity of the CNN method to increase the speed of the face detection process based on research findings [11], [12]. the CNN method was used to perform a face detection algorithm representation using multilayer as a feature extractor to automatically obtain special features [13].…”

“…This procedure is known as the training procedure. The training procedure is divided into three phases: the convolutional layer, the pooling layer, and the fully connected layer [10], [11]. In general, the CNN model adheres to the architecture depicted in Figure 1.…”

Cloud-based machine learning algorithms for anomalies detection

“…Since the context analysis in a convolutional architecture is based on patterns across several texts, it excels in feature extraction but lacks the capacity to recall earlier knowledge. While recurrent architecture is designed to take information order into account, its feature extraction capabilities lag below those of convolutional architecture [18]. The accessibility of social media has contributed much to its meteoric rise in popularity in recent years.…”