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All Journal Jurnal Nasional Holistic Science EduMatika: Jurnal MIPA EDUCTUM: Journal Research Argopuro: Jurnal Multidisiplin Ilmu Bahasa Economic: Journal Economic and Business Argopuro : Jurnal Multidisiplin Ilmu Bahasa
Daulay, Nurfitri Humayro
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Religion Humanities 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.
Protection and Education of Street Children in Islam: A Study of Their Life Experiences Triana, Dinie; Nurmayani, Nurmayani; Putri, Amelia; Daulay, Nurfitri Humayro; Hani, Aulia
EDUCTUM: Journal Research Vol. 4 No. 2 (2025): Eductum: Journal Research
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejr.v4i2.962

Abstract

The phenomenon of street children is one of the complex social issues in Indonesia. Many factors contribute to children living on the streets, such as poverty, family instability, lack of access to education, and exploitation by certain parties. This study aims to analyze the protection and education of street children from an Islamic perspective, as well as the challenges they face in their daily lives. The research employs a qualitative method using interviews and observations of several street children. The findings indicate that these children encounter various obstacles, including social stigma, lack of family attention, and limited access to education and healthcare. From an Islamic perspective, children have the right to receive protection, affection, and proper education. Therefore, collaboration between families, communities, religious institutions, and the government is necessary to provide sustainable solutions for street children.
ANALISIS PENGGUNAAN BAHASA INDONESIA YANG SALAH DALAM KEHIDUPAN SEHARI-HARI Silaban, Dewi Fortuna; Surbakti, Arnis Wulan Andari; Sinaga, May Rani Tabitha; Faradhilah, Anatasia; Daulay, Nurfitri Humayro; Khairani, Ita
Argopuro: Jurnal Multidisiplin Ilmu Bahasa Vol. 7 No. 2 (2025): Argopuro: Jurnal Multidisiplin Ilmu Bahasa
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6734/argopuro.v7i2.12044

Abstract

Bahasa memiliki peran yang sangat penting dalam kehidupan manusia sebagai alat komunikasi utama. Bahasa Indonesia, sebagai bahasa nasional, tidak hanya berfungsi sebagai alat komunikasi tetapi juga sebagai identitas kebangsaan yang mencerminkan budaya, nilai, dan sejarah bangsa. Namun, penggunaan bahasa Indonesia yang sesuai dengan kaidah kebahasaan semakin terpinggirkan, terutama dalam media tulis seperti spanduk. Kesalahan dalam penggunaan ejaan, tata bahasa, dan diksi sering ditemukan akibat kurangnya pemahaman masyarakat terhadap Pedoman Umum Ejaan Bahasa Indonesia (PUEBI) serta pengaruh bahasa daerah dan bahasa nonformal. Penelitian ini bertujuan untuk menganalisis kesalahan berbahasa yang terdapat pada spanduk dan mengidentifikasi strategi efektif dalam meningkatkan kesadaran masyarakat mengenai pentingnya penggunaan bahasa yang baik dan benar. Dengan menggunakan metode deskriptif kualitatif, penelitian ini mengumpulkan data melalui observasi dan dokumentasi, lalu menganalisisnya berdasarkan kategori kesalahan kebahasaan. Hasil penelitian menunjukkan bahwa kesalahan umum yang ditemukan meliputi ketidaktepatan dalam ejaan, penggunaan kata serapan yang tidak sesuai, serta penyimpangan dalam pembentukan kata. Kurangnya kesadaran dan pemahaman terhadap kaidah bahasa menjadi faktor utama dalam kesalahan penggunaan bahasa Indonesia di ruang publik. Oleh karena itu, diperlukan upaya edukatif melalui mekanisme peneguran yang efektif serta kampanye kesadaran berbahasa agar penggunaan bahasa Indonesia yang baik dan benar dapat diterapkan dengan lebih luas di masyarakat.
ANALISIS PENGGUNAAN BAHASA INDONESIA YANG SALAH DALAM KEHIDUPAN SEHARI-HARI Silaban, Dewi Fortuna; Surbakti, Arnis Wulan Andari; Sinaga, May Rani Tabitha; Faradhilah, Anatasia; Daulay, Nurfitri Humayro; Khairani, Ita
Argopuro: Jurnal Multidisiplin Ilmu Bahasa Vol. 7 No. 2 (2025): Argopuro: Jurnal Multidisiplin Ilmu Bahasa
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.6734/argopuro.v7i2.12044

Abstract

Bahasa memiliki peran yang sangat penting dalam kehidupan manusia sebagai alat komunikasi utama. Bahasa Indonesia, sebagai bahasa nasional, tidak hanya berfungsi sebagai alat komunikasi tetapi juga sebagai identitas kebangsaan yang mencerminkan budaya, nilai, dan sejarah bangsa. Namun, penggunaan bahasa Indonesia yang sesuai dengan kaidah kebahasaan semakin terpinggirkan, terutama dalam media tulis seperti spanduk. Kesalahan dalam penggunaan ejaan, tata bahasa, dan diksi sering ditemukan akibat kurangnya pemahaman masyarakat terhadap Pedoman Umum Ejaan Bahasa Indonesia (PUEBI) serta pengaruh bahasa daerah dan bahasa nonformal. Penelitian ini bertujuan untuk menganalisis kesalahan berbahasa yang terdapat pada spanduk dan mengidentifikasi strategi efektif dalam meningkatkan kesadaran masyarakat mengenai pentingnya penggunaan bahasa yang baik dan benar. Dengan menggunakan metode deskriptif kualitatif, penelitian ini mengumpulkan data melalui observasi dan dokumentasi, lalu menganalisisnya berdasarkan kategori kesalahan kebahasaan. Hasil penelitian menunjukkan bahwa kesalahan umum yang ditemukan meliputi ketidaktepatan dalam ejaan, penggunaan kata serapan yang tidak sesuai, serta penyimpangan dalam pembentukan kata. Kurangnya kesadaran dan pemahaman terhadap kaidah bahasa menjadi faktor utama dalam kesalahan penggunaan bahasa Indonesia di ruang publik. Oleh karena itu, diperlukan upaya edukatif melalui mekanisme peneguran yang efektif serta kampanye kesadaran berbahasa agar penggunaan bahasa Indonesia yang baik dan benar dapat diterapkan dengan lebih luas di masyarakat.
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 Faradhilah, Anatasia Hani, Aulia Ita Khairani NURMAYANI NURMAYANI Putri, Amelia Silaban, Dewi Fortuna Simanjuntak, Ferdyanto Abangan Sinaga, May Rani Tabitha Surbakti, Arnis Wulan Andari Tampubolon, Bungaria Tarigan, Febry Vista Kristen Triana, Dinie