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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JUITA : Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jusikom : Jurnal Sistem Komputer Musirawas JURNAL ILMU KOMPUTER INDONESIA Journal of Electrical, Electronics and Informatics Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) TECHNOVATE Galaksi KOMET
Sandhiyasa, I Made Subrata
STMIK STIKOM Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Sentiment Analysis on Rupiah Depreciation Against USD Using XGBoost Indrayuni, Ni Komang Purnama; Desmayani, Ni Made Mila Rosa; Pramawati, I Dewa Ayu Agung Tantri; Sandhiyasa, I Made Subrata; Widiartha, Komang Kurniawan
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10751

Abstract

The depreciation of the rupiah against the United States dollar (USD) affects purchasing power and economic stability. Public responses are widely expressed through social media such as X and Instagram. This study aims to analyze public sentiment using the Extreme Gradient Boosting (XGBoost) algorithm. Data were collected through crawling and scraping, consisting of 13,443 X comments and 11,287 Instagram comments between January 2024 until April 2025. Preprocessing included emoji conversion, cleaning, case folding, normalization, tokenization, stopwords removal, and Stemming. Sentiment labeling was performed using the InSet Lexicon, TF-IDF weighting, and data splitting   into 70:30, 80:20, and 90:10. The XGBoost model was trained with parameters: 100 estimators, learning rate 0.1, max depth 6, and subsample 0.8. Results showed accuracies of 74–76% on X data and stable 77% on Instagram. Model evaluation using precision, recall, and F1-score confirmed consistency: precision 0.76% – 0.84%, recall 0.86%–0.88%, and F1-score 0.82%–0.86%, reflecting a balance between accuracy and robustness in detecting sentiments. Sentiment distribution revealed that X is dominated by negative opinions (38%), while Instagram is more positive (41%). These findings confirm the effectiveness of XGBoost in sentiment classification and provide valuable insights for policymakers to design adaptive communication and monetary strategies based on digital public opinion.
Co-Authors Arditya, I Kadek Dito Asana, I Made Dwi Putra Atmaja, Ketut Jaya Christina Purnama Yanti Desak Made Dwi Utami Putra Fibriyanthini, Luh Gede Indrawan Hendrawati, Theresia I Gede Aris Gunadi I Gede Iwan Sudipa I Kadek Dwi Gandika Supartha I Made Oka Widyantara I Nyoman Tri Anindia Putra I Putu Yoga Indrawan I Wayan Agus Surya Darma Indrayuni, Ni Komang Purnama Ketut Sepdyana Kartini Meinarni, Ni Putu Suci Muni, Gede Dharma Sahasra Ni K. N Noviani Pande Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Widantari Suandana Nirwana, Ni Kade Ayu Nopianti, Ni Kadek Winda Oka, I Dewa Gede Ari Pande, Ni Kadek Nita Noviani Parniti, I Gusti Agung Ayu Pira Pramawati, I.D.A Tantri Pramest, Ni Luh Gede Sintia Pranata, I Made Dicky Putu Gede Surya Cipta Nugraha Saraswati, Ni Wayan Sumartini Savitri, Ni Kadek Wiliya Widiartha, Komang Kurniawan Wiguna, I Komang Arya Ganda Wijaya, Bagus Kusuma Yuri Prima Fittryani, Yuri Prima