Proceeding of The Symposium on Data Science (SDS)
type: channel
description: Symposium of Data Science (ISSN online:Â 2808-1846)Â aims to provide a forum for scholars and lecturers to discuss and exchange ideas about their research. In addition, providing opportunities for students to present student research projects in solving financial problems and related problems using a data science approach. In this activity, all participants are encouraged to be creative so as to generate ideas and innovations in the participants' research. The scope of this event is, but not limited to, in the fields of : application of data science in finance; actuarial science; Finance; and risk management. Â
language: en-US
publication name: Proceeding of The Symposium on Data Science (SDS)
issn: 2808-1846
items: Prediction of Weekly Stock Price of PT Indofood CBP Sukses Makmur Tbk (ICBP) with ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of Apple Inc by using ARIMA model
items: Forecasting Weekly Stock of PT. Astra International Tbk (ASII) using ARIMA Model
items: Value-at-Risk Calculation for Banking Portfolio using Variance-Covariance and Markowitz Optimization
items: Forecasting Weekly Stock Price of PT. Bank Central Asia Tbk (BBCA) Using ARIMA Box-Jenkins Method
items: Forecasting the Weekly Stock Price of Apple Inc. (AAPL) using Autoregressive Integrated Moving Average Method
items: Forecasting Daily Gold Price Using ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of Gold Futures (GCM5) Using ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of PT Gudang Garam Tbk (GGRM) Using ARIMA Box-Jenkins Method
items: Application of the Box-Jenkins ARIMA Method in Forecasting Weekly the Stock Price of PT Indofood Sukses Makmur Tbk
items: Forecasting Weekly Stock Price of Microsoft Corporation (MSFT) Using ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of Jakarta Stock Exchange Composite Index (IHSG) Using ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk (BBNI) Using ARIMA Box-Jenkins Method
items: Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk Using ARIMA Method
items: Forecasting Weekly Stock Price of PT Ciputra Development Tbk. (CTRA.JK) Using ARIMA Box-Jenkins Method

Prediction of Weekly Stock Price of PT Indofood CBP Sukses Makmur Tbk (ICBP) with ARIMA Box-Jenkins Method
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description: This research aims to forecast the weekly stock prices of PT Indofood CBP Sukses Makmur Tbk (ICBP) using the ARIMA model based on the Box-Jenkins methodology. Weekly closing price data from January 1 2024 to January 26 2025 is utilized as the basis for analysis. The data is first tested for stationarity, and appropriate differencing is applied to stabilize the mean. The ARIMA(2,1,0) model is identified as the best-fitting model. Model diagnostics, including the Ljung-Box test, confirm that the residuals exhibit white noise characteristics, indicating a good model fit. Forecasting performance is assessed using the Mean Absolute Percentage Error (MAPE), which resulted in a value of 1.58%, suggesting a high level of forecasting accuracy. The findings demonstrate that the ARIMA model is effective for short-term stock price prediction and can serve as a useful tool for investors and financial analysis.
creator: Putri Felicia
rights: Copyright (c) 2025 Putri Felicia
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date: 2025-08-13
publication date: 2025-08-13
volume: 5
starting page: 1
ending page: 13

Forecasting Weekly Stock Price of Apple Inc by using ARIMA model
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description: Stock price forecasting remains a critical field in financial research, offering practical benefits for portfolio optimization and risk management. This study focuses on forecasting the weekly closing prices of Apple Inc. (AAPL) from March to April 2025 using the Autoregressive Integrated Moving Average (ARIMA) model. The objective is twofold: to assess the forecasting accuracy and to validate the statistical adequacy of the model. Employing the Box-Jenkins methodology, the process includes stationarity testing via the Augmented Dickey-Fuller test, model identification through ACF and PACF plots, parameter estimation, and residual diagnostics using the Shapiro-Wilk and Ljung-Box tests. ARIMA(6,1,0) was selected based on the lowest AIC and BIC values and diagnostic compliance. The model achieved a low Mean Absolute Percentage Error (MAPE) of 2.6%, indicating strong predictive accuracy. Forecasts were accompanied by 95% confidence intervals, enhancing interpretability. The results confirm that the ARIMA model is suitable for short-term financial forecasting where model transparency and statistical validity are essential.
creator: Auriellia Theresia Kusmanto
rights: Copyright (c) 2025 Auriellia Theresia Kusmanto
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date: 2025-08-13
publication date: 2025-08-13
volume: 5
starting page: 14
ending page: 22

Forecasting Weekly Stock of PT. Astra International Tbk (ASII) using ARIMA Model
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description: Abstract— PT. Astra International Tbk (ASII) is one of the listed companies is (Indonesia Stock Exchange) IDX which focuses in multiple sectors. Stock is one of the important factors in the economy. This paper forecast the weekly stock of ASII from April 6, 2025 until April 27, 2025 by using the data from May 05, 2024 to March 23, 2025. By using Arima Box-Jenkins, the best ARIMA model for this data is the ARIMA (1,1,1) with the MSE value of 24463.64, RMSE of 156.4086, MAE value of 146.184 and MAPE of 3.0737%.
creator: Adelia Sutany
rights: Copyright (c) 2025 Adelia Sutany
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date: 2025-08-23
publication date: 2025-08-23
volume: 5
starting page: 23
ending page: 32

Value-at-Risk Calculation for Banking Portfolio using Variance-Covariance and Markowitz Optimization
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description: Risk measurement and investment decisions are closely related because investors try to strike a balance between possible returns and financial market uncertainties. The purpose of this study is to use Markowitz Portfolio Optimization and the Value at Risk (VaR) approach to evaluate and quantify the risk of an Indonesian banking stock portfolio that is listed on the LQ45 index. The study examines data from November 2024 to January 2025 to see how the portfolio's closing prices are affected by the portfolio structure determined through Markowitz Optimization. The Variance-Covariance method is then used to calculate the risk of the optimized portfolio. According to the VaR calculation using the Variance-Covariance method at two confidence levels, the maximum daily loss risk estimate is IDR 2,404,894.13 at the 95% confidence level and IDR 3,401,287.66 at the 99% confidence level. The backtesting results also show that the number of failures at both confidence levels is less than the chi-square critical value, indicating that the null hypothesis is not rejected. Based on these results, it is clear that the combination of Markowtiz Optimization and VaR calculation using Variance-Covariance works well and supports one another.
creator: Angelique Melinda Rohsinarni Sumbayak
creator: Maria Yus Trinity Irsan
rights: Copyright (c) 2025 Angelique Melinda Rohsinarni Sumbayak
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date: 2025-08-23
publication date: 2025-08-23
volume: 5
starting page: 33
ending page: 40

Forecasting Weekly Stock Price of PT. Bank Central Asia Tbk (BBCA) Using ARIMA Box-Jenkins Method
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description: The dynamic fluctuation of stock prices in financial markets highlights the importance of accurately forecasting future price movements, particularly for investors and financial institutions making strategic decisions, This study focuses on forecasting the weekly stock price of PT Bank Central Asia Tbk (BBCA) using the Autoregressive Integrated Moving Average (ARIMA) method based on the Box-Jenkins approach. The dataset consists of weekly closing stock prices from January 7, 2024, to December 29, 2024, totaling 51 observations obtained from Investing.com. Following data preprocessing, including stationarity testing and model selection. through ACF and PACF analysis, several ARIMA models were evaluated. The ARIMA(2,2,0) model was identified as the best-fitting model based on error metrics, achieving a Mean Absolute Percentage Error (MAPE) of 2.29%. This result demonstrates a high level of forecasting accuracy, providing valuable insights for investment planning and financial risk management. The findings confirm that ARIMA remains a reliable and practical method for short-term stock price forecasting in dynamic financial environments.
creator: Dheeya Raja Noorlesmana
rights: Copyright (c) 2025 Dheeya Raja Noorlesmana
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date: 2025-08-23
publication date: 2025-08-23
volume: 5
starting page: 41
ending page: 54

Forecasting the Weekly Stock Price of Apple Inc. (AAPL) using Autoregressive Integrated Moving Average Method
type: item
description: The stock price of Apple Inc. (AAPL) has been a subject of significant interest due to its volatility and market dynamics. In this study, we aimed to forecast the stock price of Apple Inc. from April 10, 2024-April 10, 2025, using the ARIMA(0,1,3) model. This model was selected due to its ability to capture key patterns in the time series data while minimizing forecasting errors. After differencing the data to ensure stationarity, the ARIMA model was fitted, incorporating two autoregressive (AR) terms and two moving average (MA) terms. Residual analysis, including the Ljung-Box and Shapiro-Wilk tests, confirmed that the residuals were uncorrelated and normally distributed, validating the model's appropriateness. The ARIMA(0,1,3) model produced reliable forecasts with minimized error metrics, offering a robust method for short-term stock price predictions. This study demonstrates the potential of ARIMA models in financial forecasting, particularly in predicting stock prices with a reasonable degree of accuracy.
creator: Sandrina Alya Harvinanda
rights: Copyright (c) 2025 Sandrina Alya Harvinanda
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date: 2025-08-25
publication date: 2025-08-25
volume: 5
starting page: 55
ending page: 63

Forecasting Daily Gold Price Using ARIMA Box-Jenkins Method
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description: In this research, the ARIMA time series model is utilized to make predictions of gold prices from March 1, 2025, to May 12, 2025. The prediction of the movement of the price of gold from May 13 to May 17, 2025, is intended. A number of ARIMA settings were tested manually whereby the order of differencing was. designed to be two to deal with non-stationarity. Using AIC and BIC as criteria, the best models were the ARIMA (4,2,0) and ARIMA (4,2,1). These models produced short-term forecasts. The approach seeks transparency in model selection as well as interpretability in order to facilitate informed forecasting.
creator: Fernando William Alexander
rights: Copyright (c) 2025 Fernando William Alexander
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publication date: 2025-08-25
volume: 5
starting page: 64
ending page: 75

Forecasting Weekly Stock Price of Gold Futures (GCM5) Using ARIMA Box-Jenkins Method
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description: The pattern of gold futures prices demands accurate forecasting to support rational decision-making on the part of investors and market experts. The study applies the ARIMA (Autoregressive Integrated Moving Average) model using the Box-Jenkins method for forecasting the weekly Gold Futures stock price (GCM5). The model was fit with 52 weekly closing prices from May 5, 2024, to April 27, 2025. For stationarity. Augmented Dickey-Fuller (ADF) test was conducted and then differencing. ACF, PACF, and "auto.arima(data)" were used to identify the model, and ARIMA(0,1,1) was found to be the best-fitting model. Diagnostic tests like Shapiro-Wilk and Ljung-Box tests confirmed the model fitness with residuals normally distributed and not having any serious autocorrelation. The forecast accuracy was checked using the Mean Absolute Percentage Error (MAPE), which yielded a value of 0.72%, indicating a very precise prediction. These results confirm that the ARIMA(0,1,1) model is a good representation of the trend and volatility of gold futures prices. This study provides real-world information to market participants and demonstrates the continued relevance of traditional time series models to financial forecasting.
creator: Naufal Pradipta Adriansyah
rights: Copyright (c) 2025 Naufal Pradipta Adriansyah
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publication date: 2025-08-25
volume: 5
starting page: 76
ending page: 84

Forecasting Weekly Stock Price of PT Gudang Garam Tbk (GGRM) Using ARIMA Box-Jenkins Method
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description: Stock price change prediction ability is paramount in directing investment decision-making in the dynamic financial environment. In this study, Box-Jenkins and the Autoregressive Integrated Moving Average (ARIMA) model are applied in predicting the monthly stock price of PT Gudang Garam Tbk (GGRM). Closing prices every month from January 1, 2024, to January 1, 2025, make up the dataset. It was discovered that the ARIMA (0.1.0) model was the most suitable based on the study; it achieved a satisfactory level of forecasting accuracy with a Mean Absolute Percentage Error (MAPE) of 3.7%. In empowering the analysts and investors to make sense of potential price trends, the research findings are in a position to empower them to make more informed investment decisions.
creator: Farabi Alqurthuby Fahlevi
creator: Edwin Setiawan Nugraha
rights: Copyright (c) 2025 Farabi Alqurthuby Fahlevi
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volume: 5
starting page: 85
ending page: 92

Application of the Box-Jenkins ARIMA Method in Forecasting Weekly the Stock Price of PT Indofood Sukses Makmur Tbk
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description: PT Indofood Sukses Makmur Tbk (INDF.JK) is the highest-ranked Indonesian food company with a diversified spread in some of the biggest sectors such as consumer branded food, flour mills, and agro-businesses. As one of the major forces behind the country's economy, the fluctuation of PT Indofood's share price is a serious. indicator to both investors and financial analysts. The Box-Jenkins ARIMA method is used in this study to forecast PT Indofood Sukses Makmur Tbk's stock price weekly from January 1, 2021, to September 24, 2023. Past stock price data were used to determine the best ARIMA model to use in forecasting future stock prices. The ARIMA (0.2.1) was identified as the most accurate model with the lowest MSE of 41366,51, RMSE of 203,3876, and MAPE of 2.6795%. This result is meaningful information to aid investors and companies in forecasting stock price change, facilitating more informed and strategic investment planning.
creator: Rosda Renata Kezia Simanjuntak
rights: Copyright (c) 2025 Rosda Renata Kezia Simanjuntak
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date: 2025-08-26
publication date: 2025-08-26
volume: 5
starting page: 93
ending page: 104

Forecasting Weekly Stock Price of Microsoft Corporation (MSFT) Using ARIMA Box-Jenkins Method
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description: The dynamic movement of stock prices in the financial market demands the ability to forecast future trends, particularly for investors and institutions aiming to make strategic decisions. This study focuses on forecasting the weekly stock price of Microsoft Corporation (MSFT) using the Autoregressive Integrated Moving Average (ARIMA) method, following the Box-Jenkins approach. The data used in this research consist of 54 weekly closing prices from April 29, 2024 to April 29, 2025. Several ARIMA models were tested, and the ARIMA(0.1.1) model was identified as the most suitable based on its accuracy metrics. This model achieved the lowest error values, including a Mean Absolute Percentage Error (MAPE) of 2.37%, indicating a high level of prediction accuracy. The results of this study provide useful insights for investors and financial analysts to better anticipate stock price fluctuations and enhance data-driven decision-making in portfolio management and investment strategies.
creator: Nur Farida
creator: Edwin Setiawan Nugraha
rights: Copyright (c) 2025 Nur Farida
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publication date: 2025-08-26
volume: 5
starting page: 105
ending page: 113

Forecasting Weekly Stock Price of Jakarta Stock Exchange Composite Index (IHSG) Using ARIMA Box-Jenkins Method
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description: Stock price movements in the capital market reflect investor sentiment and economic trends, requiring accurate forecasting methods to support decision-making. One method that can be used for forecasting is the Autoregressive Integrated Moving Average (ARIMA). The application of the ARIMA method to the weekly stock price data of the Jakarta Stock Exchange Composite Index (IHSG) from November 2023 to April 2025 produces the equation Y, 0.874-1-0.5359+0.66193 + 0.041661-1+0.9721e2+ 0.1892-3+ et which is obtained from ARIMA(2,1,3) as the best model based on the lowest error values (MAE, MSE, RMSE, and MAPE). This model passed residual diagnostics and demonstrates strong potential in forecasting IHSG stock prices.
creator: Geovani F Simanjuntak
rights: Copyright (c) 2025 Geovani F Simanjuntak
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publication date: 2025-08-26
volume: 5
starting page: 114
ending page: 130

Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk (BBNI) Using ARIMA Box-Jenkins Method
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description: Dynamic stock price movements in the economic world demand the ability to predict future trends, especially for investors and companies that make strategic decisions. This research focuses on forecasting the weekly stock price of PT Bank Negara Indonesia Tbk (BBNI) using the Autoregressive Integrated Moving Average (ARIMA) method based on the Box-Jenkins approach. The data analyzed consists of weekly closing prices from January 6, 2024 to November 2, 2024 as many as 44 observations. The results show that the ARIMA (2,1,0) model is the best fit for this dataset, with relatively highly accurate prediction results, as indicated by a Mean Absolute Percentage Error (MAPE) of 4.09%. These results offer valuable insights to help investors and companies anticipate stock price changes, supporting more informed and strategic investment planning.
creator: Nazwa Indry Chintya
rights: Copyright (c) 2025 Nazwa Indry Chintya
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date: 2025-08-27
publication date: 2025-08-27
volume: 5
starting page: 131
ending page: 144

Forecasting Weekly Stock Price of PT. Bank Negara Indonesia Tbk Using ARIMA Method
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description: This study focuses on forecasting the stock price of PT. Bank Negara Indonesia Tbk (BBNI) using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are weekly stock prices from March 3, 2024, to February 23, 2025, with a total of 51 observations. The model aims to forecast stock prices for the next four weeks, from March 2 to March 23, 2025. The Box-Jenkins method is used to identify and evaluate the best-fitting ARIMA model. After conducting stationarity tests, ACF and PACF plots, and performing residual analysis, the ARIMA (1,2,1) is chosen to be the best model. This model gives the lowest error, with a Mean Absolute Percentage Error (MAPE) of 8.58% indicating very accurate predictions. The results of this study show that the ARIMA model is reliable for short-term stock price forecasting which offers valuable insight to help investors. make better decisions by providing a simple but effective forecasting tool.
creator: Celia Christy Merlinda Tantono
creator: Edwin Setiawan Nugraha
rights: Copyright (c) 2025 Celia Christy Merlinda Tantono
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date: 2025-08-28
publication date: 2025-08-28
volume: 5
starting page: 145
ending page: 154

Forecasting Weekly Stock Price of PT Ciputra Development Tbk. (CTRA.JK) Using ARIMA Box-Jenkins Method
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description: The volatility of stock prices in the market demands the ability to predict future prices accurately which helps investors and companies to maximize the profits by risk management and investments allocation, PT Ciputra Development Tbk. is one of the leading property and real estate companies in Indonesia, offering modern concepts. This study focuses on forecasting the weekly stock price of PT Ciputra Development Tbk. using historical weekly closing stock price data from January 01, 2024 to December 31, 2024. The forecast utilized Autoregressive Integrated Moving Average (ARIMA) model. Based on the calculations, it is acquired that ARIMA (2, 2, (0) is the best and most efficient model to predict the future stock price, as it achieved the highest accuracy, indicated by the mean absolute percentage error (MAPE) value at 3.213681%.
creator: Ivana Felixia Suharsono
creator: Edwin Setiawan Nugraha
rights: Copyright (c) 2025 Ivana Felixia Suharsono
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date: 2025-08-28
publication date: 2025-08-28
volume: 5
starting page: 155
ending page: 162