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All Journal Scientific Journal of Informatics Journal of Soft Computing Exploration
Ristu Saptono
Department of Informatics, Universitas Sebelas Maret, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Which Features Matter Most? Evaluating Numerical and Textual Features for Helpfulness Classification in Imbalance Dataset using XGBoost Anindita Putri Kirani; Ristu Saptono; Rini Anggrainingsih
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.33443

Abstract

Purpose: This study aims to develop and realistically evaluate a reliable model for identifying helpful online reviews, particularly in the context of Indonesian-language texts, which are often informal and challenging. Methods: This study addresses several key challenges in predicting review helpfulness: the relative effectiveness of numerical features from metadata compared with traditional text representations (TF-IDF, FastText) on noisy data; the impact of severe class imbalance; and the limitations of standard validation compared with time-based validation. To address these challenges, we built an XGBoost model and evaluated various feature combinations. A hybrid approach combining SMOTE and scale_pos_weight was applied to handle class imbalance, and the best configuration was further assessed using time-based validation to better simulate real-world conditions. Result: The results show that the model based on numerical features consistently outperformed the text-based model, achieving a peak macro F1-score of 0.7214. Compared to the IndoBERT baseline (F1-score = 0.6400) and the RCNN FastText baseline (F1-score = 0.5317), this indicates that simpler feature-driven models can provide more reliable predictions under noisy review data. Time-based validation further revealed a performance decline of up to 8.06%, confirming the presence of concept drift and highlighting that standard validation tends to yield overly optimistic estimates. Novelty: The main contribution of this research lies in offering a robust methodology while demonstrating the superiority of metadata-based approaches in this context. By quantifying performance degradation through temporal validation, this study provides a more realistic benchmark for real-world applications and highlights the critical importance of regular model retraining. 
Rethinking the role of three-star ratings: Handling inconsistency in indonesian tourism reviews usingcost-sensitive learning XGBoost Muhammad Nur Hikmah Ramadhan; Akhmad Syaifuddin; Ristu Saptono
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.159

Abstract

Indonesian people often give contrary online reviews, for instance ratings that do not always match the actual feelings. This can make it difficult for tourism sector, such as Solo Safari, to handle complaints and improve service quality. Consequently, this study has formulated a rating prediction model using the XGBoost algorithm with a dataset of 2,047 reviews that have been relabeled. The best model, the FastText-XGBoost with Cost-Sensitive Learning, signify that it performs quite well with an average difference the prediction is only 0.04 points from the actual rating. However, the results are not optimal because the meaning of the review text still feels fuzzy even though being relabeled. This problem arises because reviewers tend to position sentiment very positively or negatively in the moderate category, so the perimeters between classes become less clear. This study then proposed an extreme class restructuring by simplifying the category by removing the 3-star rating. This method can increase the accuracy of the model to 0.9440 and clarify the category limits. Therefore, the model can be used on the service dashboard to help Solo Safari management respond to critical feedback faster.
Co-Authors Akhmad Syaifuddin Anindita Putri Kirani Muhammad Nur Hikmah Ramadhan Rini Anggrainingsih