<![CDATA[The massive volume of consumer reviews on the social commerce platform TikTok Shop makes it difficult for local shoe brands such as Ventela to understand consumer perception in a structured manner, while Indonesian-language Aspect-Based Sentiment Analysis (ABSA) studies on this platform remain very limited. This study aims to apply fine-tuned IndoBERT for aspect-based sentiment classification and to measure consumer perception of four product aspects, namely Comfort, Design, Durability, and Price. Using a computational experiment approach, 1,000 reviews were collected, automatically annotated using a lexicon-based method with negation handling, restructured into 706 review-aspect pairs and divided using an 80:20 stratified split, and used to train and compare three models: TF-IDF with Logistic Regression, TF-IDF with Linear SVM, and fine-tuned IndoBERT. Testing on 142 test samples shows that fine-tuned IndoBERT is superior, achieving an Accuracy of 0.8521 and an F1-Macro of 0.7813 and surpassing both baselines on four of five primary metrics. Analysis of 706 review-aspect pairs identifies Design (75.6% positive) and Price (71.8% positive) as the main strengths, while Comfort (32.7% negative) and Durability (30.8% negative) emerge as improvement areas related to sizing and the quality of adhesive and stitching. This study enriches Indonesian ABSA literature in the social commerce domain and delivers a ready-to-use web-based simulator built with Gradio to facilitate periodic consumer-perception monitoring for data-driven decision-making processes.]]>
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