## 2 Materials and Methods
### Data Preprocessing  
The data we collected were initially in an unstructured format, which was unsuitable for direct analysis [17]. Therefore, a series of data preprocessing steps were designed to clean the data from irrelevant elements, such as symbols, emojis, punctuation, and other components that interfere with the quality of the text. These preprocessing stages were designed comprehensively to ensure the data became more structured and ready for in-depth analysis. A critical step in this data preprocessing is case folding, which changes all capital letters to lowercase letters. This step ensures consistency and uniformity in the data, considering that uppercase and lowercase letters can be considered different in computer analysis if not arranged accordingly. With case folding, all the exact words will be recognized uniformly, regardless of whether they were initially written in uppercase or lowercase, thereby reducing the risk of word duplication due to differences in writing formats [5]. In addition to case folding, a normalization process was also carried out, changing non-standard words into their standard form according to Indonesian language guidelines. This normalization is essential to overcome the non-standard language or slang often used in student reviews to identify each word accurately according to its true meaning. This process refers to Indonesian language guidelines and references to ensure the consistency and validity of the language used in the analysis. For example, words such as \"gak\" are changed to \"tidak\", or \"bgt\" is changed to \"banget\", which ensures that sentiment analysis can be carried out more precisely. The following process is tokenization, an essential step in text analysis. Tokenization breaks sentences into individual words or tokens, which helps identify each word separately based on specific language terminology [7]. In the Indonesian context, this process must consider the differences in language structure and idiomatic so tokenization can produce meaningful word units for analysis. This tokenization process is essential to ensure accuracy in analyzing sentiment or meaning contained in the text [18]. After tokenization, the stemming process is also carried out, changing words into their primary forms. In Indonesian, many words have affixes or variations in form, such as prefixes, suffixes, or insertions, which can affect the analysis if not simplified to their primary form. Stemming removes affixes and ensures that each word is identified in its original form, allowing analysis to focus more on its core meaning without being distracted by grammatical variations [4; 19]. For example, words like \"berman\", \"di-main\", or \"meman\" will all be converted to \"main\", so that they are recognized as the same entity. With all the preprocessing steps, including normalization, stemming, and tokenization, the data are transformed into a more structured and consistent format, making it easier to conduct further analysis. In addition to normalization, stemming, and tokenization, stopword removal was conducted to eliminate frequently occurring but uninformative words, such as \"yang\", \"dan\", and \"di\". Removing stopwords ensures that the analysis focuses on meaningful content without interference from redundant terms. These preprocessing steps, \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Value** \\ \hline Observed Disagreement \(D_{o}\) & \(1.03\times 10^{-5}\) \\ Expected Disagreement \(D_{e}\) & 0.64811 \\ [PERSON]’s Alpha \(\propto\) & 0.99998 \\ \hline \hline \end{tabular} This value of Krippendorff’s Alpha (\(\alpha\) \(>\) 0.99) indicates a near-perfect agreement between raters, suggesting high labeling consistency.  
\end{table} 
Table 7: Krippendorff’s Alpha calculation results. specifically designed for the Indonesian language, play a critical role in preparing the data for ABSA.