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All Journal Jurnal Mantik Penusa SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan SENTIA 2017 SENTIA 2015 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Varian Jurnal Bumigora Information Technology (BITe) SOLID Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi SATIN - Sains dan Teknologi Informasi INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT Alamtana Prosiding Seminar Nasional CORISINDO
Santoso, Heroe
Universitas Bumigora
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ANALISIS SENTIMENT KEUANGAN MENGGUNAKAN FINE-TUNED FINBERT Heroe Santoso; Raisul Azhar; Suryati, Suryati; Melati Rosanensi; I Made Yadi Dharma; Husain, Husain; Ahmat Adil; Muhamad Azwar; I Putu Hariyadi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.316

Abstract

Financial information is a critical type of data for analysis. However, because much of it is unstructured and widely dispersed, an appropriate analytical method is required, one of which is sentiment analysis. In the financial context, sentiment analysis is employed by the industry to assess public perceptions of companies or market conditions. This study implements a fine-tuned FinBERT model to perform sentiment analysis in the financial sector. The dataset used is a combination of FiQA (Financial Question Answering) and The Financial PhraseBank, consisting of English sentences labeled with negative, neutral, and positive sentiments. The research process involved data preprocessing, tokenization, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the model achieved 82% accuracy, with its best performance in the positive class (F1-score 0.88) and the neutral class (F1-score 0.85), but weaker performance in detecting the negative class (F1-score 0.49). These findings indicate that the fine-tuned FinBERT is effective for financial sentiment analysis, particularly for positive and neutral sentiments, though improvements are needed in negative sentiment detection, potentially through expanding training data diversity or applying data augmentation techniques
Co-Authors Afriandi, Mahsus Agus Pribadi Ahmad Muzanni Ahmad Wilda Yulianto Ahmat Adil Al-Mu’min, Al-Mu’min Amin, Farda Milanda Andi Sofyan Anas Anthoni Anggrawan Anthony Anggrawan Ardiasyah, Muhammad Irwan Augustin, Kartarina Azhar, Lalu Zazuli Azhar, Raisul Dadang Pyanto Daniel Happy Putra Darma, I Made Yadi Fadiel Rahmad Hidayat Faisal Faisal Fathurrahman Fathurrahman Fitriani, Nurul Hairani Hairani Hamdika Budiman Hammad, Rifqi Hariyadi, I Putu Hasan, Abd. Hasbullah Helna Wardhana Hengki Tamando Sihotang Husain Husain I Gede Bagus Arthana I Made Yadi Darma I Made Yadi Dharma I Made Yadi Dharma I Putu Hariyadi Ismarmiaty, Ismarmiaty Khairan marzuki Khasnur Hidjah Kusuma, April Lalu Sofiyandi Prayatna Lanang Sakti Lela Rahmawati Lestari, Humaediah Lilik Widyawati Mardedi, Lalu Jazuli Azhar Mayadi Mayadi, Mayadi Melati Rosanensi Miftahul Madani Muh Thariq Ali Mujitomo Muhamad Azwar Muhamad Azwar Muhamad Azwar, Muhamad Muhamad Wisnu Alfiansyah Muhammad Irwan Ardiasyah Muhammad Munawir Gazali Mujitomo, Muh Thariq Ali Muliadi Muliadi Murianto Murianto Ni GustiAyu Dastriani Nurul Fitriani Ondi Asroni Qulub, Mudawil Rahmawati, Lela Sukatin, Sukatin Sunardy Kasim Suriyati ., Suriyati Suriyati Suriyati Suryati Suryati Suryati Suryati Syahrir, Moch. Syamsurrijal Syamsurrijal Tajuddin, Muhammad Triwijoyo, Bambang Krismono Vidiasari, Viviana Herlita Wira Hendri Yayan Suganda Zulkipli Zulkipli