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Search 10.35870/jimik.v4i3.404 , by doi

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ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Julinar Sari Hutagalung; Rasiban
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.404

Abstract

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.
ANALISIS SENTIMEN KEUANGAN (DATA FIQA AND FINANCIAL PHRASEBANK) MENGGUNAKAN ALGORITMA LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE Hutagalung, Julinar Sari; Rasiban
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.404

Abstract

Finance is a very vital sector in a Company and institution because it has a very important strategic role in creating a conducive environment, especially for the improvement of the national economy. Through a combination of FiQA and Financial PhareBank text datasets, an analysis of positive, negative and neutral sentiments related to finance is carried out that can be taken into consideration to make a policy in the financial sector or context in achieving this strategic role. Application of sentiment analysis using hyperparameter tuning in Logistic Regression and Support Vector Machines algorithms, with TF-IDF and Smote weighting on training data. The best model results of 70.70% accuracy on the Support Vector Machine algorithm during model training using training data that is not done Smote class imbalance.

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