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All Journal EduMatika: Jurnal MIPA Economic: Journal Economic and Business
Faradhilla, Anatasia
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Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Physics

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The Influence of Financial Ratios and Company Size on Bond Ratings of Banking Companies Listed on the Indonesia Stock Exchange in 2019-2024 Triana, Dinie; Putri, Amelia; Faradhilla, Anatasia; Putri, Alya Nabila
Economic: Journal Economic and Business Vol. 4 No. 2 (2025): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v4i2.961

Abstract

This study analyzes the influence of financial ratios and firm size on bond ratings of banking companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2024 period. The independent variables include liquidity (Current Ratio), leverage (Debt to Equity Ratio), profitability (Return on Assets), and firm size (Size), with bond ratings as the dependent variable. A quantitative method with ordinal logistic regression is employed to examine the relationships among these variables. Secondary data were obtained from banking companies’ financial reports and bond ratings issued by PT Pefindo. The findings indicate that profitability (ROA) has a significant negative effect on bond ratings, suggesting that highly profitable companies do not necessarily receive better ratings. Meanwhile, leverage (DER), liquidity (CR), and firm size do not significantly impact bond ratings. The regression model is validated with a Nagelkerke R2 value of 0.784, indicating that the independent variables explain 78.4% of the variation in bond ratings. These results provide insights for investors and stakeholders that profitability plays a more critical role in determining bond ratings than other financial factors. Therefore, banking companies should optimize their profitability management to enhance bond credibility in financial markets.
Analysis of Book Production Quality Using Ewma Control Chart: Comparison of Alpha Parameters, Trend Prediction, and Production Correlation Putri, Amelia; Hani, Aulia; Faradhilla, Anatasia; Hutapea, Risca Octaviyani
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.
Stock Closing Price Prediction of PT Bank Central Asia Tbk (BBCA) with Long Short-Term Memory (LSTM) Tarigan, Febry Vista Kristen; Putri, Amelia; Nicolas, Jogi; Faradhilla, Anatasia; Gulo, Lirana Sapriani; 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.
Co-Authors Arnita Arnita Gulo, Lirana Sapriani Hani, Aulia Hutapea, Risca Octaviyani Nicolas, Jogi Putri, Alya Nabila Putri, Amelia Tarigan, Febry Vista Kristen Triana, Dinie