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All Journal Turbo : Jurnal Program Studi Teknik Mesin JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL BIOSAINSTEK Budimas : Jurnal Pengabdian Masyarakat ILTEK : Jurnal Teknologi
ARIYANTO, MUHAMMAD
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Co-Authors Alzami, Farrikh Arifin, Muhammad Farhan Asih Rohmani, Asih Devi Andriani Dewi, Sukhma Kusuma Gaio, Agapito Hammada Abbas Indra Gamayanto Iswahyudi Jamaluddin Jamaluddin Kinanty, Septyana Retno Muhammad Naufal Nurhaidah, Nurhaidah Pangestu, Estri Pramudia Ramadhan Rakhmat Sani Sejati, Priska Trisna Sri Winarno Suhendra, Diky Rahmat Syahnul Sardi Titaheluw Tri Pratomo Tri Rijanto Umar Tangke, Umar Wijayanti, Selvi