Analisis Sentimen Pasar melalui Berita Finansial untuk Prediksi Harga Saham PT Bank Rakyat Indonesia Tbk

Ferdyansyah Permana Putra, Mukti Ratna Dewi, Fausania Hibatullah

Abstract

Abstrak:

Sentimen pasar merupakan salah satu faktor yang mempengaruhi fluktuasi harga saham yang dapat bersumber dari masyarakat umum maupun berita-berita yang terkait dengan saham. Dalam penelitian ini, pengaruh sentimen pasar melalui berita keuangan dianalisis terhadap harga saham PT Bank Rakyat Indonesia Tbk (BBRI). Penelitian ini menggunakan pendekatan machine learning dengan metode Support Vector Regression (SVR) untuk memprediksi harga penutupan saham BBRI berdasarkan sentimen berita. Model SVR dioptimalkan dengan algoritma Fruit Fly Optimization Algorithm (FOA). Sentimen pasar terlebih dahulu dievaluasi menggunakan metode IndoBERT yang menunjukkan tingkat akurasi sentimen keseluruhan di atas 90%. Setelah itu, empat skenario pemodelan diusulkan untuk menemukan model prediksi terbaik: (1) model tanpa sentimen, (2) model dengan sentimen pada periode , (3) model dengan sentimen pada periode , dan (4) model dengan sentimen pada periode  dan periode . Hasil akhir menunjukkan bahwa model pada skenario (1) memiliki kesalahan prediksi terendah dibandingkan dengan model lainnya

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Abstract:

Market sentiment is one of the factors that influences the fluctuation of stock prices, which can originate from the general public or news related to stocks. In this study, we explore the effect of market sentiment through financial news on the stock price of PT Bank Rakyat Indonesia Tbk (BBRI). This research adopts a machine learning approach using the Support Vector Regression (SVR) method to predict the closing price of BBRI stock based on news sentiment, and the function is later optimized with the Fruit Fly Optimization Algorithm (FOA) algorithm. The market sentiment is first evaluated using the IndoBERT method, which shows an overall sentiment accuracy level above 90%. Afterward, four modeling scenarios are proposed to find the best prediction model: (1) a model without sentiment, (2) a model with sentiment at period , (3) a model with sentiment at period , and (4) a model with sentiment at both period  and period . The final results indicate that the model in scenario (1) has the lowest prediction error compared to other models

Keywords

Sentiment Analysis, Stock Prediction, Support Vector Regression, Fruit Fly Optimization Algorithm, IndoBERT

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References

[1]

I. CNN, "Cetak Laba Kuartal I, Saham BBRI Terbang," 3 Rabu 2023. [Online]. Available: https://www.cnnindonesia.com/teknologi/20230501124025-303-943910/cetak-laba-rp1556-triliun-di-kuartal-i-2023-saham-bbri-terbang.

[2]

M. R. I. Taufani, "SVB Krisis Teknologi atau Krisis Perbankan? Ini Penjelasannya," 23 April 2023. [Online]. Available: https://www.cnbcindonesia.com/research/20230423200317-128-431925/svb-krisis-teknologi-atau-krisis-perbankan-ini-penjelasannya.

[3]

C.-J. Lu, "Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression," Decision Support Systems, pp. 115-125, 2009.

[4]

M. F. Rohmah, I. K. G. Putra, R. S. Hartati and A. L, "Comparison Four Kernels of SVR to Predict Consumer Price Index," Journal of Physics: Conference Series, pp. 2-10, 2021.

[5]

E. S. Hedianti, Peramalan Harga Saham Dengan Menggunakan Metode Support Vector Regression, Surabaya: ITS Press, 2019.

[6]

D. I. Purnama and S. Setianingsih, "Support Vector Regression (SVR) Model for Forecasting Number of Passengers on Domestics Flights at Sultan Hasanudin Airport Makassar," Jurnal Matematika, Statistika, dan Komputasi, pp. 391-403, 2020.

[7]

Mustakim, A. Buono and I. Hermadi, "Performance Comparison Between Support Vector Regression And Artificial Neural Network For Prediction of Oil Palm Production," Jurnal Ilmu Komputer dan Informasi, pp. 1-8, 2016.

[8]

L. W. G. Cao, "Support Vector Regression with Fruit Fly Optimization Algorithm for Seasonal Electricity Consumption Forecasting," Energy, pp. 734-745, 2016.

[9]

Z. Li, Y. Fan, B. Jiang, T. Lei and W. Liu, "A Survey On Sentiment Analysis and Opinion Mining for Social Multimedia," Multimedia Tools and Applications, pp. 6939-6967, 2019.

[10]

Mihalcea and Ignatow, A Guidebook for the Social Sciences, UK: SAGE Pulibcations, 2017.

[11]

M. Turland, Php "Architect's Guide to Web Scraping", Canada: Marco Tabini & Associates, 2016.

[12]

J. Devlin, M. Chang, K. Lee and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformets for Language Understanding," in 2019 Conference of The North American Chapter of the Association for Computational Linguistics: Human Language Technologies , North American, 2019.

[13]

B. Wilie, K. Vincentio, G. I. Winata, S. Cahyawijaya, X. Li, Z. Y. Lim, S. Soleman, M. R, P. Fung, S. Bahar and A. Purwatianti, "IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding," Proceedings of the 1st Conference of the Asia-Pasific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference Natural Language Processing, pp. 843-857, 2020.

[14]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez , L. Kaiser and I. Polosukhin , "Attention is All You Need : Advances in Neural Information Processing Systems," Nips, pp. 5999-6009, 2017.

[15]

W. C. Hong, "Electric Load Forecasting by Support Vector Model," Applied Mathematical Modelling Vol. 33, pp. 2444-2454, 2008.

[16]

W. K. Haerdle and C. M. D. D & Hafner, "Support Vector Machines with Evolutionary Model Selection for Deafult Prediction, in The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics," eds. Racine, JS, Su, L, and Ullah, A, Oxford, pp. 346-373, 2014.

[17]

W. T. Pan, "A New Fruit Fly Optimization Algorithm: Taking The Financial Distress Model As An Example," Knowledge-Based System, No. 26, pp. 68-74, 2012.

[18]

W. W. Wei, Time Series Analysis Univariate and Multivariate MEthods 2nd Edition, USA: Pearson Education, 2006.

[19]

Z. Chen, "Gaussian Process Regression Methods and Extensions for Stock Market Prediction (Doctoral Disertation)," University of Leicester, UK, 2017.

[20]

A. Hotho, A. Numberger and G. Paab, "A Brief Survey of Text Mining," In Ldv Forum, pp. Vol. 20(1), p. 19-62, 2005.

[21]

G. B. Malkiel, "Efficient Market Hypothesis," in Finance, London, Palgrave Macmillan UK, 1989, pp. 127-134.

[22]

D. Rustiana and S. Ramadhani, "Strategi di Pasar Modal Syariah," Jurnal Ilmu Komputer, Ekonomi, dan Manajemen (JIKEM), pp. 1578-1589, 2022.

[23]

T. Purwanti, "Ini Saham Sektoral yang Jadi Favorit Investasi Anak Muda," 12 April 2022. [Online]. Available: https://www.cnbcindonesia.com/market/20220414133554-17-331731/ini-saham-sektoral-yang-jadi-favorit-investasi-anak-muda.

[24]

A. Malik, "The Big 4 Bank Diprediksi Cemerlang, Puluhan Reksadana Borong Saham BMRI, BBRI, BBNI, dan BBCA," 24 Februari 2023. [Online]. Available: https://www.bareksa.com/berita/saham/2023-02-24/the-big-4-bank-diprediksi-cemerlang-puluhan-reksadana-borong-saham-bmri-bbri-bbni-dan-bbca.

DOI: https://doi.org/10.20961/ijai.v9i1.94692

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