## 4 Results and Discussion  
This research aims to find the optimum word embedding method and the best LSTM architectures. Two embedding methods were evaluated for transforming the cleaned text into numerical vectors. LSTM is a recurrent neural network (RNN) variant with a more complex forget gate computation, allowing it to accommodate the influence of time-series signals from the immediate node and to retain the influence of a more extended sequence. In this research, we investigated three LSTM architectures: single layer, double layer, and triple layer, as shown in Figures 8 and 9. Training was carried out in 200 epochs to build the models. Figure 8 shows the single-, double- and triple-layer LSTM on the balanced dataset with GloVe embedding. Training accuracy and loss are plotted with the blue line, while yellow represents validation accuracy and loss. As can be seen, the single-layer LSTM shows a vast distance between the training and validation accuracy. In contrast, the double- and  
Figure 8: Training accuracy and training loss of the LSTM using GloVe embedding: (**a**) single-layer architecture; (**b**) double-layer architecture; (**c**) triple-layer architecture. triple-layer LSTM architectures show a smaller distance between the training and validation accuracy. Figure 8 demonstrates the effect of a thicker LSTM achieving convergency in earlier training iteration. Figure 9 shows the training progress on the next experiment scenario using fastText word embeeding. Figure 9 shows the single-, double-, and triple-layer LSTM on the balanced dataset with fastText embedding. As can be seen, the single-layer LSTM results show a vast distance between training and validation accuracy. In contrast, the double- and triple-layer LSTM show a smaller distance between training and validation accuracy. As can be seen in double and triple layer of LSTM, the validation accuracy cross the training accuracy at earlier epoch of training. It is indicated that the model reached convergency earlier. This fact lead  
Figure 9: Training accuracy and training loss of the LSTM using fastText embedding: (**a**) single-layer architecture; (**b**) double-layer architecture; (**c**) triple-layer architecture. us to implement early stopping technique in evaluation using the 5 fold cross validation. Table 7 presents the accuracy achieved by those architectures. In general, all of the accuracies achieved with fastText word embedding outperformed those achieved when using GloVe. Stacking more layers of LSTM led to higher accuracy in both training and testing. Triple-layer LSTM achieved better performance either double- or single-layer LSTM. This achievement, however, must be achieved at the expense of processing speed, as shown in Table 7. In this experiment, we used a CUDA GPU Titan V with a total memory of 12 GB. Table 8 compares the trainable parameters, training time, and testing time required for each LSTM design. Our results are in line with those of previous studies such as [28; 41; 42]. Although evaluations were performed on different datasets in previous works, the general trend shows the positive impact of LSTM. Regarding the embedding methods, fastText [14] demonstrates superiority compared to GloVe [15]. Our results confirm the findings reported by [41], where they demonstrated that Skip-Gram fastText performed better than Word2 vec [13] and AraVec. In our experiment, fastText word embedding performed better than GloVe. A greater number of trainable parameters leads to longer computation times, and the correlation among the trainable parameters and training time can be seen in Table 8. Training produces a model, which is compiled in the H5 file format. Testing uses the compiled model in a forward pass on the testing data. The elapsed time for each testing set consistently increases with the thickness of the implemented layers. Therefore, the triple-layer LSTM required a longer time than the other architectures.