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All Journal JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (JHSSB)
Sembodo, Eko
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Law, Crime, Criminology & Criminal Justice Social Sciences

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Analysis of Stock Price Prediction for PT Mayora Indah Tbk Using ARIMA and Prophet Models Tukiyat, Tukiyat; Nuraini, Ani; Sembodo, Eko; Supriatna, Dahlan; Sova, Maya
JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS Vol. 4 No. 4 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jhssb.v4i4.1903

Abstract

The instability of stock market prices necessitates the utilization of precise predictive models, with ARIMA and Prophet providing alternative methods for addressing patterns, seasonal variances, and changes in value. This study aims to compare the forecasting performance of ARIMA and Prophet models in predicting the stock price of PT Mayora Indah Tbk. (MYOR.JK) using daily closing price data obtained from Yahoo Finance, spanning the period from January 1, 2018, to May 2, 2025. ARIMA was employed for its robustness in handling stationary and linear time series, whereas Prophet was applied due to its flexibility in capturing nonlinear components, seasonal fluctuations, and sudden market changes. The models were developed and evaluated in RStudio, with accuracy measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ARIMA (1,1,1) model produced a MAPE of 3.21% and white noise residuals, signifying reliable short-term predictions yet limited adaptability to complex long-run dynamics. Conversely, the Prophet model achieved a lower MAPE of 2.87%, exhibiting superior predictive accuracy, trend adaptability, and sensitivity to abrupt price movements. Overall, the findings indicate that Prophet outperforms ARIMA for daily stock price forecasting and underscore the importance of selecting appropriate models in financial time series analysis, while also encouraging future exploration of hybrid or deep learning-based approaches such as Long Short-Term Memory (LSTM) networks to further enhance prediction accuracy.
Co-Authors Ani Nuraini, Ani Maya Sova Supriatna, Dahlan Tukiyat Tukiyat, Tukiyat