Indonesian Clickbait Detection Using Improved Backpropagation Neural Network

Abstract

Clickbait has been considered a problem in the modern age of technology, especially in Indonesia. Various attempts have been made to research clickbait detection, however, researches which use Indonesian data are relatively scarce compared to other languages such as English because the availability of Indonesian datasets are lacking. Backpropagation Neural Network was used in clickbait detection on article's title and has achieved quite good accuracy result, however there is still a chance to improve the accuracy. This paper shows the results of using a modified backpropagation neural network algorithm to detect clickbait using article titles when compared to the standard algorithm. The research compares the results of standard stochastic gradient descent algorithm, mini-batch gradient descent algorithm, and a version of stochastic gradient descent with Adam optimizer and three hidden layers. The results show that using Adam optimizer and three hidden layers in stochastic gradient descent algorithm significantly improves the results compared to the standard architecture. The modified algorithm shows a precision score of 78% and a recall and F1 score of 76%, where the standard algorithm has a precision score of 67% and a recall and F1 score of 66%. The resulting algorithm is then implemented to a desktop application, which is considered easy to use.