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Article type: Research Article
Authors: Sholawatunnisa, Dinda Pusparahmia | Suadaa, Lya Hulliyyatusa; b; * | Nugraha, Usepb | Pramana, Setiaa; b
Affiliations: [a] Politeknik Statistika STIS, Jakarta, Indonesia | [b] BPS Statistics Indonesia, Indonesia
Correspondence: [*] Corresponding author: Lya Hulliyyatus Suadaa, Politeknik Statistika STIS, Otto Iskandardinata 64C, Jakarta 13330, Indonesia. E-mail: [email protected].
Abstract: Gross Domestic Product (GDP) stands as a pivotal indicator, offering strategic insights into economic dynamics. Recent technological advancements, particularly in real-time information dissemination through online economic news platforms, provide an accessible and alternative data source for analyzing GDP movements. This study employs online news classification to identify patterns in the movement and growth rate of Indonesia’s GDP. Utilizing a web scraping technique, we collected data for analysis. The classification models employed include transfer learning from pre-trained language model transformers, with classical machine learning methods serving as baseline models. The results indicate superior performance by the pre-trained language model transformers, achieving the highest accuracy of 0.8880 and 0.7899. In comparison, hyperparameter-tuned classical machine learning models also demonstrated commendable results, with the best accuracy reaching 0.845 and 0.7811. This research underscores the efficacy of leveraging online news classification, particularly through advanced language models. The findings contribute to a nuanced understanding of economic dynamics, aligning with the contemporary landscape of information accessibility and technological progress.
Keywords: Pre-trained language model transformers, machine learning, transfer learning, natural language processing, economic growth
DOI: 10.3233/SJI-230038
Journal: Statistical Journal of the IAOS, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
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