A Multi-label Classification on Topics of Quranic Verses (English Translation) Using Backpropagation Neural Network with Stochastic Gradient Descent and Adam Optimizer
Nanang Saiful Huda, Mohamad Syahrul Mubarok, Adiwijaya Adiwijaya · 2019
The Quran is a guideline for all Muslims. In the Quran, many things are talked about. In Quranic studies, the Quran is first classified into several topics according to the discussions of the Quranic verses. In this research, a classification model using a Back Propagation Neural Network was built based on the verses of Al-Quran and its multi-labelled topics. This allows the Back Propagation algorithm architecture to issue labels for each class in the form of `yes' or `no' for each output neuron. When using the Back Propagation algorithm, a sentence input that has become a vector is taken. In this way, TF-IDF will be used for feature extraction. Then, the model was evaluated via calculation of Hamming Loss. To ensure an optimal Back Propagation process, a comparison was made between the Stochastic Gradient Descent (SGD) and Adam optimizers. Based on some experiments, the proposed scheme yielded the best performance with a Hamming Loss value of 0.129.