## 1 Introduction  
Evaluating the learning process in higher education ensures that teaching methods, facilities, and services align with students' expectations and institutional goals [1]. As centers for developing expertise and human resources, universities are responsible for ensuring a high-quality learning process that aligns with the demands of the times. Universities have increasingly relied on data-driven evaluations, including sentiment analysis of student reviews, to identify strengths and weaknesses in teaching quality, facilities, and administrative services. This evaluation is an essential component of the teaching cycle, providing constructive feedback to lecturers and institutions to enhance students' quality of learning experience [2]. While online questionnaires are commonly used to evaluate the learning process, their closed-ended format often limits the depth of collected feedback. To address this limitation, universities are now exploring free-text student reviews, which provide richer insights into student experiences but require advanced techniques like ABSA for practical analysis [3]. These questionnaires are typically limited to closed-ended responses covering a few fundamental indicators, leaving insufficient room for students to express their grievances or appreciation in depth. Consequently, universities face challenges in obtaining objective and comprehensive feedback. To address these limitations, universities are now exploring alternative methods by allowing students to provide more in-depth online reviews related to the learning process. The unstructured nature of free-text student reviews, combined with the linguistic complexity and cultural nuances of Indonesian feedback, poses challenges for traditional analysis methods. By leveraging the IndoBERT model, this study addresses these challenges to extract actionable insights from student feedback with higher precision [4]. This study aims to capture these subtleties, providing a more comprehensive analysis that can yield actionable insights for educational institutions. Additionally, such reviews often contain various intertwined emotions and aspects, making them challenging to interpret without in-depth analysis. Previous studies have shown that sentiment analysis has been widely applied to identify the sentiment or opinion embedded within sentences. While previous studies have generally focused on analyzing overall sentiment in sentences or documents, aspect-based sentiment analysis has been developed to extract more specific information, allowing for a more detailed evaluation of the learning process. In educational evaluation, it is essential to understand the overall sentiment and particular aspects of learning, such as teaching methods, interactions with lecturers, facilities, and the provided materials. Sentiment analysis can be conducted at various levels, such as sentence, document, or aspect levels. Sentiment analysis at the sentence or document level typically aims to identify the overall sentiment within a single text unit [5]. In contrast, ABSA seeks to identify sentiments associated with specific aspects of the text, providing a richer and more detailed understanding of the reviews [6]. In the context of student reviews, the ABSA approach can help identify sentiments related to essential aspects of learning, such as the clarity of material delivery, availability of learning resources, lecturer-student interactions, and assignment assessment. This approach enables universities to obtain more specific and contextual feedback as a foundation for improving teaching quality. Developing ABSA presents unique challenges involving two main tasks: aspect detection and sentiment classification [7]. Aspect detection refers to identifying words or phrases that indicate specific components within the text [8]. At the same time, sentiment classification determines whether the sentiment associated with those aspects is positive, negative, or neutral.