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All Journal JURNAL MANAJEMEN PENDIDIKAN Jurnal Nasional Holistic Science PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam EduMatika: Jurnal MIPA EDUCTUM: Journal Research Economic: Journal Economic and Business
Hani, Aulia
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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.
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.
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.
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.
PEMBELAJARAN IQRA’ DI TAMAN PENDIDIKAN QUR’AN (DITINJAU DARI PERSPEKTIF TEORI PERKEMBANGAN KOGNITIF JEAN PIAGET) hani, Aulia; Iswati; Bayu Ardiwansyah
PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam Vol. 6 No. 1 (2025): JULI-DESEMBER
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/profetik.v6i1.10880

Abstract

Perkembangan materi Iqra’ yang cara belajarnya dengan melafalkan bacaan dari tingkat 1 sampai 6 yang bergantung pada daya tangkap. Hal ini searah dengan pernyataan Piaget dalam teori perkembangan kognitif anak yaitu pertambahan usia anak akan memengaruhi perkembangan belajar mereka, karena usia berpengaruh pada daya tangkap anak. Oleh karena itu, penelitian ini bertujuan: (1) Mengetahui pembelajaran Iqra’ santri dengan kondisi sebenar-benarnya di lapangan, (2) Hasil dari tujuan pertama diteliti kembali dan ditinjau menggunakan perspektif teori perkembangan kognitif Jean Piaget, dan (3) Solusi dari kendala dalam pembelajaran Iqra’ yang ada di lokasi penelitian. Penelitian ini menggunakan jenis penelitian kualitatif dengan pendekatan fenomenologi. Hasil penelitian (1) Pembelajaran Iqra’ di lapangan tidak ada kurikulum tiap semester, dan cara belajarnya dengan metode privat. (2) (a) Tahap Pra-Operasional pada subtahap simbolik berada pada Iqra’ tingkat 1, dan subtahap intuitif berada tingkat 4, dan 5, (b) Pada tahap operasional konkret berada ditingkat 4 sampai 6, (c) Tahap operasional formal berada tingkat 6. Selain usia, ada pengaruh lainnya, seperti latar belakang keluarga, cepat atau lambatnya anak mulai belajar serta memahami materi. Solusi atas kendala tersebut dengan sering tanya-jawab, memahami proses belajar, serta membentuk kurikulum Iqra’ disesuaikan perkembangan santri sebelumnya.
Analisis Volatilitas dan Forecasting Return Saham Bank Mandiri Menggunakan ARIMA-GARCH: Proyeksi 5 Bulan ke Depan Putri, Amelia; Hani, Aulia; Maulana, Bintang
JURNAL MANAJEMEN PENDIDIKAN Vol 13, No 2 (2025): Jurnal Manajemen Pendidikan
Publisher : Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/jmp.v13i2.13120

Abstract

ANALYSIS OF VOLATILITY AND FORECASTING RETURN OF BANK MANDIRI SHARES USING ARIMA-GARCH: PROJECTIONS FOR THE NEXT 5 MONTHSIndonesia’s capital market continues to demonstrate strong dynamics, particularly among blue-chip stocks such as PT Bank Mandiri (Persero) Tbk (BMRI), which exhibit high volatility influenced by macroeconomic variables, global conditions, and market sentiment. To obtain a more accurate projection of BMRI’s price movement, this study applies the ARIMA-GARCH model to forecast stock prices for the five-month period from November 2025 to April 2026. The dataset consists of daily closing prices from November 2020 to November 2025. Preprocessing involved handling outliers using winsorizing and conducting a stationarity test, in which the Augmented Dickey-Fuller result indicated that the return series was stationary with a p-value of 0.0000. Based on information criteria, ARIMA(0,0,1) was selected as the optimal mean model with an AIC value of –3985.7041. Volatility was subsequently modeled using GARCH(1,1) with a normal distribution, showing a persistence level of 0.8598, suggesting that BMRI volatility remains elevated and requires time to stabilize after a shock. Diagnostic tests confirmed the absence of residual autocorrelation (Ljung-Box p-value 0.05) and no remaining ARCH effect (p-value 0.1744), validating the adequacy of the combined model. Forecasting through 1,000-iteration Monte Carlo simulation produced an average projected final price of Rp 4,960, up from Rp 4,730, indicating an expected change of +4.82%. Risk metrics showed a 95% Value at Risk (VaR) of Rp 3,550 and an Expected Shortfall of Rp 3,340. The model’s performance on the test dataset yielded an RMSE of 0.014704 and an sMAPE of 75.7663%.
Co-Authors Arnita Arnita Bayu Ardiwansyah Daulay, Nurfitri Humayro Faradhilla, Anatasia Hutapea, Risca Octaviyani Iswati Maulana, Bintang NURMAYANI NURMAYANI Putri, Amelia Simanjuntak, Ferdyanto Abangan Sinaga, May Rani Tabitha Tampubolon, Bungaria Tarigan, Febry Vista Kristen Triana, Dinie