PANCASILA-BASED ASPECT CATEGORY SENTIMENT ANALYSIS FOR DETECTING NEGATIVE CONTENT IN INDONESIAN CODE-MIXED SOCIAL MEDIA

Stefani Tasya Hallatu; Ratih Nur Esti Anggraini; Adhatus Solichah Ahmadiyah

doi:10.53806/jmscowa.v7i1.1561

Authors

Stefani Tasya Hallatu Informatics, Institut Teknologi Sepuluh Nopember Surabaya, Surabaya, 60111, Indonesia
Ratih Nur Esti Anggraini Informatics, Institut Teknologi Sepuluh Nopember Surabaya, Surabaya, 60111, Indonesia
Adhatus Solichah Ahmadiyah Informatics, Institut Teknologi Sepuluh Nopember Surabaya, Surabaya, 60111, Indonesia

DOI:

https://doi.org/10.53806/jmscowa.v7i1.1561

Keywords:

Aspect Category Sentiment Analysis; Code-Mixed; Fine-Tuned Transformer; Pancasila; Zero-Shot Annotation.

Abstract

Hate speech and value-violating content on Indonesian social media, compounded by code-mixed language, threaten social cohesion. This study proposes a Pancasila-based Aspect Category Sentiment Analysis framework grounded in Indonesia’s five foundational values: Divinity, Humanity, Unity, Democracy, and Social Justice. Four Transformer models were evaluated under Full Fine-Tuning, LoRA, and QLoRA on 41,138 Indonesian code-mixed texts (confidence >= 0.75), annotated via zero-shot LLM inference and validated by two independent experts (k = 0.82; LLM-expert k = 0.81). IndoBERT LoRA achieved the highest in-pipeline F1-Score (0.77), though bootstrap intervals show this isstatistically indistinguishable from several top configurations. Againstan independent expert-validated ground truth (n = 1,000), all models dropped 6.7% on average; IndoBERTweet QLoRA obtained the highest point-estimate generalization (GT F1 = 0.71, 10.27 MB adapter storage), best read as the top of a statistically indistinguishable cluster rather than a confirmed single best model. The framework offers a culturally grounded approach to AI-assisted content moderation in Indonesia.