## 4 Results
The result derived from the GA-optimized LSTM network is measured by calculating the three mentioned performance indices of the actual unemployment rate and the output of the proposed hybrid model. The results of BiLSTM, GRU, and GA-LSTM are presented below in Table 4. It should be noted that we chose the best results of the individual algorithms by testing various combinations, as previously discussed. According to the data presented in Table 5, the MSE, MAE, and MAPE metrics were used following the reference of [PERSON] and [PERSON] (2018); in this sense, they must be interpreted as follows:  
The smaller the MSE, MAE, and MAPE values, the better the prediction effect of the model. The results reveal quantitative data that allows us to obtain the following findings:  
1. As shown in Table 4, machine learning models can establish unemployment prediction models with reasonable results according to MAPE values. However, GA-LSTM performs better than BiLSTM and GRU. For MSE, the best result of GA-LSTM is 0.052, while the results of BiLSTM and GRU are 0.130 and 0.072, respectively. Meanwhile, for MAE, the best result of GA-LSTM is 0.200, while for BiLSTM and GRU, the best results are 0.291 and 0.220, respectively. For MAPE, GA-LSTM is also better. Furthermore, when the NHL is 2, the results are always better in the GRU model compared to BiLSTM. 2. The performance of GA-LSTM is better in MSE, MAE, and MAPE metrics; GRU, with two hidden layers, is the next model that presents the best predicted results. 3. The features selected in the study for GA-LSTM and the individual BiLSTM and GRU models achieved a good result. 4. The proposed GA-LSTM works well with the configurations (1, 12, 3) for the number of hidden layers, units, and windows, respectively. Additionally, the data were analyzed using the paired \(t\)-test formula. The paired \(t\)-test is a proper statistical technique to determine whether the mean of the differences between two paired contexts (two prediction models) is different from 0. The hypothesis to be tested is that the paired predictive models do not have identical performance. The statistical significance of the test is determined by looking at the \(p\)-value. The hypothesis would be validated with \(p\)-values close to zero for the paired predictive models, while values close to 1 would invalidate the hypothesis. A 1% significance level was used to validate the proposed hypothesis using a \(t\)-test (\(p\)-value \(\leq\) 0.01). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Model Configuration**} & \multicolumn{5}{c}{**Metrics**} \\ \hline \multirow{2}{*}{**Model**} & **Number of** & **Number of** & **Window** & **Batch Number** & **Number of** & **Number of** & **Number of** & **MAE** & **MAPE** \\  & **Hidden Layer(s)** & **Hidden Units** & **Number** & **(Batch Size)** & **Epochs** & **Optimizer** & **MSE** & **MAE** & **(\%)** \\ \hline BiLSTM & 1 & (2) & - & 2 & 34 & [PERSON] & 0.130 & 0.291 & 5.565 \\  & 2 & (5, 5) & - & 2 & 80 & [PERSON] & 0.135 & 0.326 & 6.168 \\ GRU & 1 & (6) & - & 10 & 70 & [PERSON] & 0.074 & 0.231 & 4.392 \\  & 2 & (5, 5) & - & 2 & 32 & [PERSON] & 0.072 & 0.220 & 4.198 \\ GA-LSTM & 1 & (12) & 3 & 10 & 5 & [PERSON] & 0.052 & 0.200 & 3.797 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance measure of models for predicting the unemployment rate. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Worse** & **Better** & **Observation** \\ \hline MSE & +\(\infty\) & 0 & The closer the MSE or MAE value is to 0, \\ MAE & +\(\infty\) & 0 & the more accurate the model will be. \\ MAPE & +\(\infty\) & 0 & The closer to 0, the better. Generally, a MAPE must be less than 20\% \\  & & & for a model to be good (reasonable) [45]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of metrics.