An Automated System for Detecting and Improving Academic Text Politeness Using IndoBERT and IndoT5 - Belina Eka Ariesty; Chanifah Indah Ratnasari (SISTEMASI, 2026)
The increasing use of digital communication in academic interactions between students and lecturers is often not accompanied by consistent application of language politeness norms, potentially affecting the effectiveness of academic interactions. To date, efforts to enhance language politeness have predominantly relied on manual and subjective evaluation. This study aims to develop an automated system for detecting and improving politeness in Indonesian academic text communication. The proposed approach integrates IndoBERT as a classification model to identify levels of text politeness and IndoT5 as a generative model to transform sentences identified as impolite into more appropriate academic forms. The dataset consists of 6,230 labeled sentences collected through Google Forms, TikTok, and additional synthetic data generated using ChatGPT. Experimental results show that the IndoBERT model achieves an accuracy of 97.11% in classifying academic text politeness, while IndoT5 is capable of transforming impolite sentences into more appropriate academic expressions, as demonstrated by evaluations using BLEU, ROUGE, and METEOR metrics. This study results in an integrated deep learning–based system capable of automatically detecting and improving academic text politeness within a unified processing framework.