## 3 Results and Discussion
### Analysis of Variance (ANOVA) Approach  
ANOVA is one of the statistical models approach by using computational techniques to detect the performance of factors and their interactions by comparing the percentage of contribution of each parameter to the response [33]. The primary purpose of applying ANOVA is to investigate which design parameters significantly affect the quality characteristic. Thus, it can be decided which independent factor dominates over the other and the percentage contribution of that particular independent variable. This analysis is measured by the sum of the squared deviations from the total mean signal to noise ratios (S/N) to obtain which parameters significantly affect the preparation of POA nanoparticles. Statistically, there is a tool called the F-test, which was named in honour of Sir [PERSON]. The F-test was conducted to see which design parameters have a significant effect on the quality characteristic. In this analysis, the F-ratio is a ratio of the mean square error to the residual error and is traditionally applied to determine the significance of a factor [34].  
From the results of ANOVA, the last column in Table 5 illustrated the percentage contribution of each parameter. Percentage (%) contribution is defined as the significant rate of the process parameters and larger values represent a more significant effect on the preparation of POA nanoparticles. It can be observed that the size of balls has the most significant effect with 43.42% contribution, followed by milling time (34.08%) contribution, and milling speed (15.39%) contribution. However, the significant effect of the relationship between parameters could be seen in contributing factors (%). It is observed that contributing factors (%) reveal there was an interaction effect of process parameters. Moreover, this theory supported by [PERSON] and [PERSON] [35] found out that the more significant the contributions of a particular factor to the total sum of squares, the larger the ability is of that factor to influence process parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **DoF** & **Sum of Square** & **Means Square** & **F-Test** & **Contribution (\%)** \\ \hline
**Milling Time** & 2 & 6.2614 & 3.1307 & 4.80 & 34.08 \\
**Milling Speed** & 2 & 2.8275 & 1.4138 & 2.17 & 15.39 \\
**Size of Balls** & 2 & 7.9762 & 3.9881 & 6.11 & 43.42 \\
**Error** & 2 & 1.3053 & 0.6527 & & 7.11 \\
**Total** & 8 & 18.3704 & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ANOVA approach to prepare POA nanoparticles.  
The optimization results from the combination of parameters, which were predicted as 20 mm of ball's size, 24 h milling time, and 100 rpm on milling speed, must be supported through the confirmation test. The confirmation experiments were conducted to validate the optimal parameters obtained by the Taguchi method. This test aims to validate the optimal process parameters and to show how close the respective prediction with the real experiment is. Moreover, the confirmation test is highly recommended by [PERSON] to verify the accuracy of the optimal process parameters that have been selected. Under these conditions, to validate the palm oil ash (POA) properties, the particle size distribution and morphology characteristics were carried out by particle size analyzer and TEM micrograph, respectively.