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All Journal Jurnal Ilmiah Wahana Pendidikan Jurnal Nasional Holistic Science EduMatika: Jurnal MIPA Economic: Journal Economic and Business
Tarigan, Febry Vista Kristen
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Religion Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Efficiency of Newton Polynomial Interpolation Method in Determining Stock Price Movements in a Certain Time Tampubolon, Bungaria; Tarigan, Febry Vista Kristen; Daulay, Nurfitri Humayro; Hani, Aulia
Holistic Science Vol. 4 No. 3 (2024): Jurnal Nasional Holistic Science
Publisher : Lembaga Riset Mutiara Akbar NOMOR AHU-0003295.AH.01.07 TAHUN 2021

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/hs.v4i3.790

Abstract

This research evaluates the efficiency of the Newton polynomial interpolation method in predicting stock price trends from historical data. The study was conducted using Microsoft stock price data according to Nasdaq for the period 4 November to 29 November 2024. This method creates an example of a polynomial based on divided disparities to describe patterns stock price convoy. The calculation results show that Newton interpolation is able to form stock price predictions with good accuracy, for example the stock price prediction in the 20 off index is $417.05, which is close to the historical data trend. The graph obtained also illustrates the mathematical interaction between the free index and stock prices visually. However, the accuracy of predictions is largely determined by the amount and quality of data used. Therefore, Newton's interpolation can be used as a a simple and efficient analytical tool, especially when applied in conjunction with other methods to deal with the complexity of the stock market.
Aplikasi Pembelajaran Metode Regresi Logistik Biner Dalam Mengidentifikasi Karakteristik Prokok Aktif Di Provinsi Sumatera Barat Tahun 2020 Payana, Sandi Dwi; Effendy, Fachriz; Andari, Arnis Wulan; Tarigan, Febry Vista Kristen; Arnita, Arnita
Jurnal Ilmiah Wahana Pendidikan Vol 11 No 2.C (2025): Jurnal Ilmiah Wahana Pendidikan 
Publisher : Peneliti.net

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Abstract

This study aims to identify the characteristics of active smokers in West Sumatra Province in 2020 using binary logistic regression. The dependent variable in this study is smoking status (active smoker or non-smoker), while the independent variables analyzed include the highest level of education, per capita expenditure, gender, and homeownership. According to data from the Central Bureau of Statistics (BPS), the percentage of the population over 15 years old who smoke in Indonesia in 2020 was 28.69%, with monthly per capita expenditure on cigarettes amounting to 5.99%. This indicates that cigarettes have become a highly favored commodity, even surpassing basic foodstuffs. Additionally, data from the 2018 Basic Health Research (Riskesdas) recorded the prevalence of smoking-related diseases, such as Acute Respiratory Infections (ARI) at 9.3%, heart disease at 1.5%, and hypertension at 34.11%. Despite the health risks of smoking being widely communicated, smoking consumption remains high, while the number of people quitting smoking is relatively low. According to Riskesdas 2018, the proportion of former smokers increased from 4% in 2013 to 5.3% in 2018, while the proportion of non-smokers decreased from 66.6% to 65.9% in the same period. Using binary logistic regression, this study analyzes how the independent variables education level, and per capita expenditure affect an individual's likelihood of being an active smoker. The results indicate that these variables significantly influence the likelihood of an individual being an active smoker. This study provides valuable insights for public health policy in designing more targeted smoking prevention programs, especially in West Sumatra Province, by considering the socio-economic characteristics that influence smoking behavior.
Apple Stock Price Prediction Using Stochastic Model: A Geometric Brownian Motion Study Tampubolon, Bungaria; Tarigan, Febry Vista Kristen; Daulay, Nurfitri Humayro; Hani, Aulia
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.v4i2.1004

Abstract

The dynamic and fluctuating nature of stock prices poses a challenge in investment decision-making. This study aims to analyze and predict the stock price of Apple Inc. (AAPL) using the Geometric Brownian Motion (GBM) stochastic model. Historical stock price data for Apple was collected from Yahoo! Finance, including opening price, highest price, lowest price, closing price, and trading volume. The model utilizes mean return and volatility estimates to conduct a Monte Carlo simulation of potential future stock price movements. The simulation results indicate that within the next one year, Apple's stock price is predicted to be approximately $295.15, with possible variations reflecting market volatility. Sensitivity analysis reveals that mean return has a greater impact on stock prices than volatility, emphasizing the importance of a company's fundamentals in long-term investments. Model evaluation using Mean Absolute Percentage Error (MAPE) shows a low error rate, indicating that the predictions generated are fairly accurate. These findings provide insights for investors in understanding stock price behavior and developing more effective investment strategies.
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 Andari, Arnis Wulan Arnita Arnita Daulay, Nurfitri Humayro Effendy, Fachriz Faradhilla, Anatasia Gulo, Lirana Sapriani Hani, Aulia Nicolas, Jogi Payana, Sandi Dwi Putri, Amelia Tampubolon, Bungaria