@inproceedings{annas-siagian-2025-anaselka,
    title = "Anaselka at {S}em{E}val-2025 Task 9: Leveraging {SVM} and {MNB} for Detecting Food Hazard",
    author = "Annas, Anwar  and
      Siagian, Al Hafiz",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.111/",
    pages = "807--811",
    ISBN = "979-8-89176-273-2",
    abstract = "Our system for the Sub-task 1 of SemEval-2025 Task 9 has been designed to tackle the complexities of identifying and categorizing food safety incidents from textual data. Through a rigorous experimental setup, we have developed a text classification solution that leveraged state-of-the-art techniques in data preprocessing, feature engineering, and model optimization."
}