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All Journal Jurnal Nasional Holistic Science EduMatika: Jurnal MIPA Economic: Journal Economic and Business
Tampubolon, Bungaria
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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.
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.
Development of Deep Learning Model Based on Convolutional Neural Network (CNN) for Brain Tumor Classification Using MRI Images Sinaga, May Rani Tabitha; Tampubolon, Bungaria; Daulay, Nurfitri Humayro; Triana, Dinie; Hani, Aulia; Simanjuntak, Ferdyanto Abangan; 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.v5i2.1105

Abstract

Brain tumor classification using MRI images presents a critical challenge in medical radiology. This study develops a deep learning model based on Convolutional Neural Network (CNN) to classify brain MRI images into four categories: Normal, Glioma, Meningioma, and Pituitary. A publicly available dataset from Kaggle consisting of 20,672 images was used, with preprocessing and data augmentation applied. The model architecture includes convolutional, pooling, flatten, dense, and dropout layers, optimized using the Adam optimizer and categorical crossentropy loss function. The evaluation results show that the model achieved an overall accuracy of 96% with high f1-scores across all classes, particularly for the Pituitary class (0.98). The main contribution of this study lies in the integration of diverse data augmentation techniques and Explainable AI (XAI) methods, enabling the visualization of key areas in MRI images that support classification decisions. The proposed model is not only accurate but also demonstrates strong generalization and interpretability, making it a promising tool for clinical decision support systems in brain tumor diagnosis.
Co-Authors Arnita Arnita Daulay, Nurfitri Humayro Hani, Aulia Simanjuntak, Ferdyanto Abangan Sinaga, May Rani Tabitha Tarigan, Febry Vista Kristen Triana, Dinie