Improving Indonesian Recipe Named Entity Recognition via Weighted Ensemble and Semi-Supervised Learning

Abstract

Background: The rapid growth of online culinary platforms has produced a vast amount of unstructured Indonesian recipe texts. Extracting structured information such as ingredients, quantities, and measurement units002C is essential for downstream applications, including nutritional computation and diabetes-oriented dietary recommendation systems. However, Indonesian recipe texts present substantial challenges due to linguistic variability, informal writing styles, and limited availability of annotated corpora. Objective: This study investigates effective modeling strategies for Indonesian recipe Named Entity Recognition (NER) in low-resource settings and to identify reliable approaches to improve the robustness and generalization of extraction. Methods: A quantitative experimental approach was employed to evaluate the IndoBERT, BiLSTM-CRF, IndoBERT-CRF, and hybrid IndoBERT-BiLSTM-CRF architectures. A reliability-weighted voting ensemble combining heterogeneous models was proposed, and high-confidence ensemble predictions were used for pseudo-labeling in a semi-supervised setting. Performance was assessed using entity-level precision, recall, and F1-score. Results: IndoBERT achieved a strong baseline performance (F1-score = 96.61%). IndoBERT-CRF and the hybrid IndoBERT-BiLSTM-CRF model achieved competitive F1-scores (92.99% and 92.82%, respectively) but did not outperform the Transformer-only baseline. The proposed weighted ensemble achieved the best supervised performance (F1-score = 96.79%). Semi-supervised training with ensemble-generated pseudo-labels maintained competitive performance (F1-score = 96.72%) while improving recall and generalization to unseen ingredient expressions. Conclusion: Increased architectural complexity does not necessarily improve performance on the Indonesian recipe NER task. Reliability-weighted ensemble learning combined with pseudo-labeling provides a stable and effective approach for low-resource culinary NER and supports future nutrition-oriented recipe processing. Keywords: Indonesian Recipe Text; Pseudo-Labeling; Named Entity Recognition; Semi-Supervised Learning; Weighted Ensemble