Article Per Year (5 Year)
p-Index From 2019 - 2024
0.61
P-Index
This Author published in this journals
All Journal Jurnal Teknik Industri Terintegrasi (JUTIN) Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Journal of Digital Community Services
Lailatul Syifa Tanjung
Universitas Pahlawan Tuanku Tambusai
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Teknik Industri Terintegrasi (JUTIN)

 1 
Comparison Random Forest Regression and Linear Regression For Forecasting BBCA Stock Price Arif Mudi Priyatno; Lailatul Syifa Tanjung; Wahyu Febri Ramadhan; Putri Cholidhazia; Putri Zulia Jati; Fahmi Iqbal Firmananda
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 6 No. 3 (2023): July 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v6i3.16933

Abstract

Stock trading is a popular financial instrument worldwide. In Indonesia, the stock market is known as the Indonesia Stock Exchange (BEI), and one actively traded stock is PT Bank Central Asia (BBCA). However, predicting stock price movements is challenging due to various influencing factors. Investors use fundamental and technical analyses for decision-making, but results often vary. Machine learning, particularly random forest regression and linear regression algorithms, can be used for stock price forecasting. In this paper, we compares these two machine learning methods to forecast BBCA stock prices, aiming to provide more accurate and effective solutions for investor's investment and trading decisions. The evaluation results of cross-validation mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for linear regression were 0.12848, 0.35807, 0.29570, and 0.0036%, respectively, while for random forest regression were 27473.76, 158.04, 142.70, and 1.7153%. These findings indicate that linear regression outperforms in forecasting performance.
Co-Authors Arif Mudi Priyatno Fahmi Iqbal Firmananda Hidayat Hidayat Hidayati Rusnedy Putri Cholidhazia Putri Zulia Jati Wahyu Febri Ramadhan Sudirman Winario, Mohd.