## 4 Results and Discussions
### Sem
#### 4.2.1 Exploratory Factor Analysis (EFA)  
The present section outlines the outcomes of the factor analysis conducted prior to the formulation of the Structural Equation Model. Exploratory Factor Analysis (EFA) was used to extract the relationships observed between the variables. Removing indicators with unacceptable loadings that have an impact on the SEM model's fitness is a crucial step. Items that had a factor loading of less than 0.6 were eliminated.  
Initially, the factorability of the four horizontal alignment indicators was investigated. Based on Table 3, the Kaiser-Meyer-Olkin (KMO) measure of adequacy for horizontal alignment was 0.767, which exceeded the threshold value of 0.6. This indicates that all indicators of horizontal alignment were suitable for analysis. The value of the KMO measure of sampling adequacy for vertical alignment indicators was mediocre, but still acceptable since it exceeded the minimum value of 0.6 (0.610). The KMO measure of the sampling adequacy for intersection indicators was high (0.828), exceeding the acceptable value of 0.6. Furthermore, the KMO measure of sampling adequacy for road accident indicators was high, with a value of 0.782, exceeding the acceptable threshold of 0.6.  
Four factors were generated based on Table 3 to represent four important variables: horizontal alignment, vertical alignment, intersection, and road accident. Based on the results of EFA as shown in Table 4, one indicator was removed for the horizontal alignment variable, and three indicators were retained. Indicators with factor loadings lower than 0.6 were discarded due to weak relationships with the extracted factors. The discarded indicators were the length of the curve and the intersection angle of the curve. Indicators that had factor loadings greater than 0.6 were deemed to have a significant correlation with the extracted factors. Therefore, these three indicators were used to test the reliability and validity of the construct. To achieve vertical alignment, it is necessary to follow the required factor loading value of more than 0.6. One indicator was removed, and three indicators were retained. Indicators with factor loadings lower than 0.6 were discarded due to weak relationships with the extracted factors. Of the various indicators, the type of curve was discarded. Indicators that had factor loadings greater than 0.6 were deemed to have a significant correlation with the extracted factors. Therefore, these three indicators were used to conduct tests for the reliability and validity of the construct.  
The intersection indicators' factor loadings were greater than 0.6, indicating a significant correlation with the extracted factors. These indicators, including the type of intersection, were used to proceed with the construction of reliability and validity tests. Three indicators were retained for road accidents, based on the required factor loading value of more than 0.6. Indicators that had factor loadings greater than 0.6 were deemed to have significant correlations with the extracted factors. Therefore, these three indicators were used to conduct reliability and validity tests.  
\begin{table}
\begin{tabular}{l c} \hline
**Latent Variable** & **KMO** \\ \hline Horizontal Alignment & 0.767 \\ Vertical Alignment & 0.610 \\ Intersection & 0.828 \\ Road Accident & 0.782 \\ \hline \end{tabular}
\end{table}
Table 3: KMO test for all latent variables.