Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal EduMatika: Jurnal MIPA
Anatasia Faradhilla
Universitas Negeri Medan
Chemistry Education Mathematics Physics

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

Found 3 Documents
Search

 1 
Stock Closing Price Prediction of PT Bank Central Asia Tbk (BBCA) with Long Short-Term Memory (LSTM) Febry Vista Kristen Tarigan; Amelia Putri; Jogi Nicolas; Anatasia Faradhilla; Lirana Sapriani Gulo; Arnita Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1104

Abstract

Stock price volatility remains one of the key challenges for investors in making accurate investment decisions in Indonesia’s capital market. To address this issue, predictive approaches based on machine learning—such as the Long Short-Term Memory (LSTM) algorithm—are increasingly utilized due to their effectiveness in processing time series data. This study aims to develop a model for predicting the closing price of PT Bank Central Asia Tbk (BBCA) shares using the LSTM method. The dataset consists of historical daily stock prices of BBCA from 2015 to mid-2025, obtained from Yahoo Finance. The research stages include data preprocessing, normalization, sequence generation, LSTM model construction, training and validation, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the LSTM model successfully predicted closing stock prices with high accuracy, as indicated by a very low validation loss and prediction curves that closely follow actual price trends. This suggests that LSTM has a strong generalization ability and is effective in capturing complex stock movement patterns. The novelty of this research lies in the practical implementation of LSTM for BBCA stock price prediction and its potential application in real-time decision support systems for investors.
Analysis of Book Production Quality Using Ewma Control Chart: Comparison of Alpha Parameters, Trend Prediction, and Production Correlation Amelia Putri; Aulia Hani; Anatasia Faradhilla; Risca Octaviyani Hutapea
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1106

Abstract

In the printing industry, maintaining product quality is a critical factor for ensuring customer satisfaction and competitive advantage. This study applies the Exponentially Weighted Moving Average (EWMA) control chart to monitor and control the quality of book production at CV Renjana Offset. Using secondary data from November 2023 to July 2024, the analysis focuses on the percentage of rejected products as a key indicator of quality performance. The EWMA method, with a smoothing constant of ? = 0.3, effectively highlights small shifts in the production process. Results show that all EWMA values lie within the control limits (UCL = 22.54%, LCL = 0.00%), indicating the process is statistically under control. Furthermore, comparison of different alpha values demonstrates the trade-off between sensitivity and stability. A moderate negative correlation (r = -0.505) between production volume and reject percentage suggests increased efficiency at higher production scales. The predicted reject percentage for August 2024 is 4.74%, indicating process stability. Overall, EWMA proves to be a valuable tool in continuous quality monitoring and data-driven decision-making in book production.
Volatility dynamics and value at risk (var) estimation: an application of the arima-garch model to icbp and indf stocks Dinie Triana; Anatasia Faradhilla; Alya Nabilla Putri
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1397

Abstract

Capital market uncertainty during the COVID-19 pandemic and the economic recovery period emphasizes the importance of accurate volatility modeling and investment risk measurement. This study aims to analyze the volatility dynamics and estimate the risk of losses in shares of PT Indofood CBP Sukses Makmur Tbk (ICBP) and PT Indofood Sukses Makmur Tbk (INDF) from January 2019 to November 2025 using the ARIMA–GARCH and Value at Risk (VaR) approaches. The study uses daily closing stock price data transformed into logarithmic returns. The analysis stages include the Augmented Dickey–Fuller (ADF) stationarity test, ARIMA model identification, ARCH-LM heteroscedasticity testing, GARCH modeling, VaR estimation at the 95% confidence level, and backtesting using the Kupiec test. The results show that the ARIMA(2,0,1) and ARIMA(1,0,1) models are selected for ICBP and INDF shares, respectively, while GARCH(1,1) is the best volatility model for both. The daily VaR estimate indicates a loss risk for INDF of 2.92%, slightly higher than ICBP's 2.86%. The Kupiec test indicates that the frequency of VaR violations corresponds to the expected level. These findings demonstrate that the ARIMA–GARCH approach can be used to describe volatility dynamics and support the risk evaluation of individual stock investments.
Co-Authors Alya Nabilla Putri Amelia Putri Arnita Arnita Aulia Hani Dinie Triana Febry Vista Kristen Tarigan Jogi Nicolas Lirana Sapriani Gulo Risca Octaviyani Hutapea