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All Journal Jurnal Informatika Jurnal Infra Seminar Nasional Informatika (SEMNASIF) JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining International Journal of Engineering, Science and Information Technology Jurnal Pengabdian Masyarakat Bangsa Bulletin of Social Informatics Theory and Application
Agustinus Noertjahyana
Program Studi Teknik Informatika Universitas Kristen Petra
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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices Pangestu, Resza Adistya; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Noertjahyana, Agustinus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28594

Abstract

Stock price prediction is a complex and important challenge for stock market participants. The difficulty of predicting stock prices is a major problem that requires an approach method in obtaining stock price predictions. This research proposes using machine learning with the Support Vector Regression (SVR) model and linear regression for stock price prediction—the dataset used in the daily Apple Inc historical data from 2018 to 2023. The hyperparameter tuning technique uses the Grid Search method with a value of k = 5, which will be tested on the SVR and Linear Regression methods to get the best prediction model based on the number of cost, epsilon, kernel, and intercept fit parameters. The test results show that the linear regression model with all hyperparameters k = 5 with the average taken performs best with a True intercept fit value. The resulting model can get an excellent error value, namely the RMSE value of 0.931231 and MSE of 0.879372. This finding confirms that the linear regression model in this configuration is a good choice for predicting stock prices.
Co-Authors A.A. Ketut Agung Cahyawan W Agus Setiawan Suhariono Agustinus Darmawan Andilolo Ahmad, Sharifah Sakinah Syed Albert Halim, Albert Alexander Setiawan Alvin Assianto Leiman Anastasia Lidya Maukar Andreanus Agung Andreas Handojo Andrey Hariyanto Anik Vega Vitianingsih Anita Nathania Purbowo Che Pee, Ahmad Naim Daniel Satriautama, Daniel Daniel Wilhenson Kuntani, Daniel Wilhenson David Harjowinoto, David Della Nova Ongkodjojo Denissa Alfiany Luhulima Devi Christiani Angir, Devi Christiani Endhy, Kezia Tiatira Fanny Febriani Santoso, Fanny Febriani Fujianto - Genadiarto, Anthony Sutera Go David Gunawan Gregorius Satia Budhi Handajani, Endah Tri Esti Handojo, Steven Halim Hartanto Rusli Heinrich Wiradinata Hendy Santoso Henry Novianus Palit Ibnu Gunawan Istiadi Justinus Andjarwirawan Kabzar, Vladimir Kartika Gunadi Kevin Darmawan Limantoro Leo Willyanto Santoso M Zainal Arifin, M Zainal Machael Cahyadi, Machael MARIFANI FITRI ARISA Maukar, Anastasia L Maulana, Sandy Yunan Michael Christian Wibisono Mohamed Asghaiyer, Asghaiyer Natanael, Theodorus Nico Hartono, Nico Ocky Mahendra Alim, Ocky Mahendra Osbert Tjitro Sampurna Palit, Henry N Pangestu, Resza Adistya Paul Agustinus Praseptiawan, Mugi Raymond Adrian Hardha, Raymond Adrian Regina, Gladys O Renardi Dewanto Cahyadi, Renardi Dewanto Richard Pangalila, Richard Rosaline Debora Kusuma Rudy Adipranata Sandy Sulistio Shandy Widjaya, Shandy Silvia Rostianingsih Slamet Kacung, Slamet Stien Tjiangdiono, Stien Surjo, Gregorius Maria Vianny Novita, Vianny Vincentius Aditya Nugraha Widya Ongels, Widya William Andi Pattera Yulia Yulia Zakaria, Mohd Hafiz