Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification
Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classific...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6100 |
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| author | Fatma Indriani Radityo Adi Nugroho Mohammad Reza Faisal Dwi Kartini |
| author_facet | Fatma Indriani Radityo Adi Nugroho Mohammad Reza Faisal Dwi Kartini |
| author_sort | Fatma Indriani |
| collection | DOAJ |
| description | Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments. |
| format | Article |
| id | doaj-art-2926b4954dc64e769d867dfbaa212002 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-2926b4954dc64e769d867dfbaa2120022025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018674875710.29207/resti.v8i6.61006100Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback ClassificationFatma Indriani0Radityo Adi Nugroho1Mohammad Reza Faisal2Dwi Kartini3Universitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung MangkuratUniversitas Lambung MangkuratStudent feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6100bert modelseducation datafinetuningmultilabel classificationsequence lengthstudent feedback |
| spellingShingle | Fatma Indriani Radityo Adi Nugroho Mohammad Reza Faisal Dwi Kartini Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) bert models education data finetuning multilabel classification sequence length student feedback |
| title | Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification |
| title_full | Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification |
| title_fullStr | Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification |
| title_full_unstemmed | Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification |
| title_short | Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification |
| title_sort | comparative evaluation of indobert indobertweet and mbert for multilabel student feedback classification |
| topic | bert models education data finetuning multilabel classification sequence length student feedback |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6100 |
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