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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Rekayasa Teknologi dan Sains Terapan Jurnal Teknik Mesin Mechanical Xplore Proceeding of the International Conference on Family Business and Entrepreneurship (ICFBF) Jurnal Pemberdayaan Umat Applied Quantitative Analysis (AQA) Journal of Actuarial, Finance, and Risk Managment
Hamzah, Dadang Amir
President University
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Forecasting the Stock Price of PT Astra International Using the LSTM Method Nugraha, Edwin Setiawan; Alika, Zalfani; Amir Hamzah, Dadang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5699

Abstract

Stocks are one of the long-term investment options and represent ownership in a company that can be acquired through buying and selling. Investment carries both the profit potential and the risks that investors must face when providing their capital to companies. Accurate stock price forecasts are very important because they provide an estimate of risk. This research aims to forecast the stock price of PT Astra International Tbk (ASII.JK) using a Long Short-Term Memory (LSTM) method. Data set closing stock prices were taken from January 2, 2015, to December 30, 2020, with a total observation of 1506. This data set is divided into 80% for training and 20% for training. The forecasting results show that the best performances have MSE, MSE, MAE and MAPE are 151.910, 23076.561, 118.128, and 2.3%, respectively. The model has a batch size of 4 and epochs of 50. This research recommends that other parties consider this method when they need to manage their investment risk in stocks.
RUIN PROBABILITY IN THE CLASSICAL RISK PROCESS WITH WEIBULL CLAIMS DISTRIBUTION Hamzah, Dadang Amir; Siahaan, Theresia Stefany Anawa; Pranata, Vania Chrestella
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2351-2358

Abstract

In the classical risk process, ruin is the situation when the surplus falls below zero. Ruin probability is a tool used to predict bankruptcy in the insurance company. The ruin probability can be determined by solving the Integral-Differential equation that arises from the classical risk process. In this paper, we are interested in calculating the ruin probability when the claim distribution follows the Weibull distribution. Based on the Weibull parameter, the calculation is divided into two cases: when alpha equals 1 and when . The Laplace transform gives the analytical solution of the Integral-Differential equation. However, when the analytical solution cannot be determined since the Laplace transform is no longer applicable due to the presence of an improper integral that is not possible to solve analytically. Therefore, for the case alpha greater than 1, Euler’s method is applied to determine its numerical solution. The accuracy of the numerical solution is validated by comparing it with the analytical solution for the case Then, using the accuracy determined from the first case, we apply the Euler method to determine the numerical solution for the case . The numerical method gives good accuracy to the analytical solution with the order of calculated from until
Forecasting UNTR Weekly Stock Price using ARFIMA Singgih, Gabriella Maria; Hamzah, Dadang Amir
Journal of Actuarial, Finance, and Risk Management Vol 4, No 1 (2025)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jafrm.v4i1.6284

Abstract

Predicting stock prices plays a pivotal role in the decision-making processes of organizations and individual investors. This research focuses on the predicting weekly closing stock prices, specifically for UNTR, using the ARFIMA method. The ARFIMA method shows promise in handling long-memory data, but its effectiveness in predicting UNTR's stock prices requires thorough examination to ensure its applicability and reliability. The aim of this study is to predict the weekly closing prices of UNTR stocks using the ARFIMA method. The training data used spans from January 1, 2020, to December 31, 2022, with the objective of predicting the period from January 1, 2023, to February 28, 2023. The result shows that the ARFIMA (10; 0.4993; 3) model was selected due to its optimal performance, having the lowest RMSE and MAPE values, specifically an RMSE of 0.4 and a MAPE of 4.16%. This model successfully captures the long-term memory patterns in the data, generating accurate predictions for the projected period. 
Predicting Travel Insurance Policy Claim with Logistic Regression Hamzah, Dadang Amir; Kalambe, Averia A; Goklas, Lucky S; Alkhayyat, Naufal G
Applied Quantitative Analysis Vol. 1 No. 1 (2021): June 2021
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/quant.613

Abstract

This paper analyzes the characteristics that influence the travel insurance claim based on existing data records. Using logistic regression, the dependent variable is the feature that determines whether there is a claim or no claim. On the other hand, the independent variables are analyzed using exploratory data analysis to identify which characteristic has the highest correlation with the dependent variable. Based on selected features, the logistic regression model is created and used to generate the prediction claim data. The predicted data gives an excellent approximation to the actual data.
Co-Authors Akbar Jabbarudin Alika, Zalfani Alkhayyat, Naufal G Ana Fitriani Annisa Sentya Hermawan Bayu Robiyana Fernando, Agus Goklas, Lucky S Haura Nizar Nabila Kalambe, Averia A Mochamad Abdul Muftinur Muchammad Chusnan Aprianto Muhammad Alfarisi Nugraha, Edwin Setiawan Pranata, Vania Chrestella Ranny Febrianti Salwa Fayza Alkatraz Shintya Jasmine Pertiwi Siahaan, Theresia Stefany Anawa sigalingging, fika lestauli Singgih, Gabriella Maria Syarifah Intan Nabilah Zievan Ananta Pahlevi