## 2 Materials and Methods
### Data Labeling  
The data labeling process in this study involved two linguists competent in text analysis and understanding the linguistic context, especially in the academic context in Indonesia. The experts were tasked with labeling the data based on predetermined sentiment categories, such as positive, negative, and neutral, and determining the main aspects reviewed, such as teaching, facilities, or curriculum [11]. Labeling was carried out independently by each expert to maintain objectivity and reduce the possibility of personal bias that could affect the analysis results [16]. This process aims to obtain accurate and consistent labels, which are essential elements in ABSA. After each expert had finished labeling, their labeling results were compared through a consistency test or inter-rater reliability test, which is a test to measure the extent of agreement between the two linguists [7]. The data labeling process in this study was conducted in two stages: manual labeling and auto-labeling. A total of 10,000 student reviews were included in the dataset. In the first stage, two expert raters with proficiency in Indonesian text analysis manually labeled 4000 student reviews. Each review was assigned an aspect category, such as Lecturer, Curriculum, Infrastructure, or Service, and a sentiment class, including Positive, Negative, or Neutral. The inter-rater agreement between the two raters was measured using Krippendorff's Alpha to ensure consistency and reliability. The inter-rater agreement between the two raters was evaluated based on their categorizations across different aspects: Lecturer, Curriculum, Infrastructure, and Services. The detailed inter-rater agreement is presented in Table 5.  
In the second stage, the remaining 6000 reviews were labeled automatically using a pre-trained sentiment analysis model. The patterns and criteria established during the manual labeling stage guided the auto-labeling process. To ensure consistency between the manually labeled subset and the automatically labeled data while significantly increasing the scalability of the dataset preparation. The final labeled dataset consisted of 10,000 reviews, organized into four aspect categories: Lecturer, Curriculum, Infrastructure, and Services, and three sentiment classes: Positive, Negative, and Neutral. An example of the labeled data is presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & \multicolumn{5}{c}{**Rater 1**} \\  & & **0** & **Lecturer** & **Curriculum** & **Infrastructure** & **Services** & **Total** \\ \hline \multirow{4}{*}{Rater 2} & 0 & 728 & 0 & 0 & 0 & 0 & 728 \\  & Lecturer & 33 & 921 & 0 & 0 & 0 & 954 \\  & Curriculum & 46 & 0 & 837 & 0 & 0 & 883 \\  & Infrastructure & 19 & 0 & 0 & 673 & 0 & 692 \\  & Services & 24 & 0 & 0 & 0 & 719 & 743 \\  & Total & 850 & 921 & 837 & 673 & 719 & 4000 \\ \hline \end{tabular}
\end{table} 
Table 5: The inter-rater agreement between the two raters. \[\alpha=1-\frac{D_{o}}{D_{e}} \tag{1}\]  
where:  
\(D_{o}\) Observed disagreement  
\(D_{e}\) Expected disagreement  
We calculate \(D_{o}\) and \(D_{e}\) using the confusion matrix provided and the formula components. Data  
From the confusion matrix:  
\(n=4000\) total label  
\(C=4\) categories: Lecturer, Curriculum, Infrastructure, Services  
Step 1: Observed Disagreement (\(D_{o}\))  
\[D_{o}=\frac{1}{n\cdot(n-1)}{\sum_{i}{\sum_{j}{f_{i}}\cdot(1-\delta_{i\ j})}} \tag{2}\]  
where  
\(f_{i\ j}\) Observed frequency of agreement for category pair (\(i,j\))  
\(\delta_{i\ j}\) Distance metric (1 for disagreement, 0 for agreement). Observed Frequencies (\(f_{i,j}\))  
From the matrix:  
* Lecturer: 921
* Curriculum: 837
* Infrastructure: 6736
* Services: 719  
Step 2: Alpha Calculation  
Combine \(D_{o}\) and \(D_{e}\), see Equation (1), I will calculate the numeric values for \(D_{o}\)\(D_{e}\) and \(\alpha\).