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All Journal Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika English Education, Linguistics and Literature Journal
Addhiny, Tuning Ridha
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Education Languange, Linguistic, Communication & Media Mathematics Physics Other

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Zero-Shot Sentiment Analysis on Student Feedback: A Comparative Study of Multilingual and Translate-Test Approaches in Indonesian Higher Education Bachtiar, Adam; Rizal, Ahmad Ashril; Addhiny, Tuning Ridha
Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika Vol. 9 No. 3 (2025): November
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/e-saintika.v9i3.4002

Abstract

The evaluation of academic services in Indonesian higher education is often hindered by the scarcity of labeled datasets (cold start problem) and the complexity of culturally implicit feedback. This study evaluates the efficacy of Zero-Shot Classification by benchmarking two distinct inference paradigms: the Direct Multilingual approach (XLM-RoBERTa-large-xnli) and the Translate-Test approach (Facebook/BART-large-mnli). Using a dataset of 280 student reviews validated by human annotators ( =0.844), the research reveals a significant performance trade-off. While XLM-RoBERTa greater robustness in maintaining global performance equilibrium (Macro F1-Score: 0.67), it exhibits a pronounced ‘Politeness Bias’, frequently failing to detect negative reviews masked by courteous language (Recall: 0.48). Conversely, the Translate-Test approach (BART) shows higher sensitivity in capturing negative sentiments (Recall: 0.77). Qualitative analysis suggests that the translation process potentially functions as a dual-mechanism: acting as a cultural decontextualization filter that isolates implicit criticism and a denoising layer that normalizes informal slang and typographical errors. However, this enhanced sensitivity results in an approximate 2.6x increase in computational latency and weaker neutral class detection. These findings indicate that while XLM-RoBERTa offers balanced generalization for broad analysis, the Translate-Test strategy is highly effective for accurately uncovering latent student grievances obscured by local linguistic styles.
The Correlation between Students English Classroom Speaking Anxiety and Speaking Fluency Sirat, Rusbihan; Ribahan, Ribahan; Jannah, Miftahul; Assapari, Muhammad Mugni; Addhiny, Tuning Ridha
English Education, Linguistics, and Literature Journal Vol. 5 No. 1 (2026): August - January
Publisher : Program Studi Magister Tadris Bahasa Inggris UIN Sultan Maulana Hasanuddin Banten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32678/ell.v5i1.12904

Abstract

This study aimed to determine the level of students’ English classroom speaking anxiety, identify the level of students’ speaking fluency, and examine the correlation between English classroom speaking anxiety and speaking fluency among fourth semester students of TBI UIN Mataram. Quantitative correlational design was employed. The participants consisted of 35 fourth semester students. Data were collected using a speaking anxiety questionnaire and a speaking test assessed through a speaking fluency The data were analyzed using descriptive statistics, normality testing, linearity testing, and Pearson Product Moment Correlation through SPSS. The findings showed that the mean score of speaking anxiety was 77.43, indicating a relatively high level of speaking anxiety, while the mean score of speaking fluency was 51.29, indicating a moderate level of speaking fluency. The Pearson correlation analysis revealed that the correlation coefficient -0.767, with a significance value of 0.000 (p < 0.05). This result indicates a strong negative correlation between the two variables. In conclusion, English classroom speaking anxiety has a significant and strong negative relationship with students’ speaking fluency.
Zero-Shot Sentiment Analysis on Student Feedback: A Comparative Study of Multilingual and Translate-Test Approaches in Indonesian Higher Education Bachtiar, Adam; Rizal, Ahmad Ashril; Addhiny, Tuning Ridha
Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika Vol. 9 No. 3 (2025): November
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/e-saintika.v9i3.4002

Abstract

The evaluation of academic services in Indonesian higher education is often hindered by the scarcity of labeled datasets (cold start problem) and the complexity of culturally implicit feedback. This study evaluates the efficacy of Zero-Shot Classification by benchmarking two distinct inference paradigms: the Direct Multilingual approach (XLM-RoBERTa-large-xnli) and the Translate-Test approach (Facebook/BART-large-mnli). Using a dataset of 280 student reviews validated by human annotators ( =0.844), the research reveals a significant performance trade-off. While XLM-RoBERTa greater robustness in maintaining global performance equilibrium (Macro F1-Score: 0.67), it exhibits a pronounced ‘Politeness Bias’, frequently failing to detect negative reviews masked by courteous language (Recall: 0.48). Conversely, the Translate-Test approach (BART) shows higher sensitivity in capturing negative sentiments (Recall: 0.77). Qualitative analysis suggests that the translation process potentially functions as a dual-mechanism: acting as a cultural decontextualization filter that isolates implicit criticism and a denoising layer that normalizes informal slang and typographical errors. However, this enhanced sensitivity results in an approximate 2.6x increase in computational latency and weaker neutral class detection. These findings indicate that while XLM-RoBERTa offers balanced generalization for broad analysis, the Translate-Test strategy is highly effective for accurately uncovering latent student grievances obscured by local linguistic styles.
Co-Authors Assapari, Muhammad Mugni Bachtiar, Adam Miftahul Jannah Ribahan Ribahan Rizal, Ahmad Ashril Sirat, Rusbihan