Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences
Ahmad Fitri
Unknown Affiliation
Education Environmental Science Mathematics Physics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Prediksi Kebangkrutan Perusahaan Asuransi di Bursa Efek Indonesia Menggunakan Model Altman Z-Score Sansivani Suvanissa; Fithri Sri Mulyani; Ahmad Fitri
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 9 No. 2 (2026): Exploring Mathematics through Education, Modeling, Finance, and Cultural Perspe
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v9i2.9056

Abstract

With the increasing economic uncertainty and claims risk that can affect the solvency level of companies, evaluating the financial condition of insurance companies becomes important. Management and regulators need to use analyzes that can identify bankruptcy risks early to make decisions.This study aims to analyze the financial condition of insurance companies listed on the Indonesia Stock Exchange for the 2021–2025 period using two main indicators, namely Risk Based Capital (RBC) and Altman Z-Score. The research method used is quantitative with a descriptive approach based on secondary data from annual financial reports. The results of the study show that most insurance companies have RBC above the minimum requirement of the Financial Services Authority (OJK) of 120%, with VINS and ABDA recording the highest RBC of 1,685.96% and 744.06% in 2025. Based on the modified Altman Z-Score, of the eight insurance companies studied, four companies are in the safe category, namely PT.AMAG, PT.ABDA, PT.VINS and PT.AHAP. And the other four are in the gray zone category, namely PT.YOII, PT.ASBI, PT.MREI and PT.ASDM. The conclusion of this study is that the majority of insurance companies have healthy financial conditions during the 2021–2025 period, with liquidity management, profitability, and capital structure being key factors in reducing bankruptcy risk.
Pengukuran Risiko Value At Risk (Var) Pada Investasi Saham Menggunakan Metode Simulasi Monte Carlo Studi Kasus: PT. Ultrajaya milk indusrty Tbk Fajar Maulana; Fitri Sri Mulyani; Ahmad Fitri
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 9 No. 2 (2026): Exploring Mathematics through Education, Modeling, Finance, and Cultural Perspe
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v9i2.9353

Abstract

This study aims to measure the investment risk of PT Ultrajaya Milk Industry Tbk's stock using the Value at Risk (VaR) method with the Monte Carlo Simulation approach. This quantitative study was conducted at the Actuarial Science Study Program, Universitas Cipasung, Tasikmalaya. The population comprises all stock price data of PT Ultrajaya Milk Industry Tbk listed on the Indonesia Stock Exchange (www.idx.co.id) and Yahoo Finance, with the sample determined through purposive sampling consisting of quarterly stock return data for the 2019–2024 period (24 observations). The research was conducted by analyzing stock return data and testing data normality using the Kolmogorov-Smirnov and Shapiro-Wilk tests. The results indicate that the stock return data are normally distributed, with significance values of 0.200 for the Kolmogorov-Smirnov test and 0.701 for the Shapiro-Wilk test, both exceeding the 0.05 significance level. The VaR calculation at the 95% and 99% confidence levels produced a value of 1,153.482. This value represents the maximum potential loss that investors may experience within the specified investment period. The findings show that the Monte Carlo Simulation method is effective in measuring stock investment risk because it provides a more comprehensive risk assessment by simulating various possible market conditions. The results of this study are expected to serve as a useful reference for investors in making investment decisions and managing stock investment risks.
Penerapan Model Geometric Brownian Motion dalam Memprediksi Harga Penutupan Saham Sektor Asuransi di Bursa Efek Indonesia (Studi Kasus: PT. Asuransi Multi Artha Guna Tbk) Syifa Ajmilatunnisa; Fithri Sri Mulyani; Ahmad Fitri
Venn: Journal of Sustainable Innovation on Education, Mathematics and Natural Sciences Vol. 5 No. 4 (2026): MIPA dan dan Pendidikan lingkup MIPA
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/venn.v5i4.514

Abstract

Ideal stock price prediction model should capture the random and fluctuating nature of stock price movements to produce more accurate forecasts. Since stock prices are highly dynamic and subject to unexpected changes, predicting future prices remains challenging due to market uncertainty and volatility. Nevertheless, many previous studies have relied on deterministic approaches, such as linear regression and ARIMA, which often fail to adequately represent stochastic market behavior. Therefore, this study aims to develop a mathematical model for predicting future stock prices using the Geometric Brownian Motion (GBM) model, particularly to support investors in selecting companies within the insurance sector. This research employs descriptive and predictive quantitative approaches. The descriptive approach examines the historical characteristics of stock price data, while the predictive approach applies the GBM model to represent asset price movements as a stochastic process influenced by return and volatility parameters. The study focuses on PT Asuransi Multi Artha Guna Tbk (AMAG) during the 2024–2026 period, using approximately 500 historical data observations collected from Investing.com. The findings indicate that the GBM model achieved a Mean Absolute Percentage Error (MAPE) of 8.09%, which is below the 10% threshold and demonstrates a very high level of predictive accuracy. These results suggest that the GBM model effectively captures the price dynamics of AMAG stock and can serve as a reliable forecasting tool for investment analysis. Future studies are recommended to incorporate external economic factors or compare GBM with other stochastic models to further improve prediction accuracy and reduce forecasting errors.
Co-Authors Fajar Maulana Fithri Sri Mulyani Fitri Sri Mulyani Sansivani Suvanissa Syifa Ajmilatunnisa