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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS) Ascendia: Journal of Economic and Business Advancement
Nurul Maqfirah Rauf
Institut Teknologi Kalimantan
Economics, Econometrics & Finance Mathematics

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Classification of Money Market Mutual Fund Products in Indonesia By Using Mahalanobis Distance and Manhattan Distance Indrawan; Muhammad Azka; Isti Kamila; Nurul Maqfirah Rauf; Eka Krisna Santoso
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6143

Abstract

This study aims to classify money market fund products listed and supervised by the Financial Services Authority (OJK) with minimal classification error. Mahalanobis distance and Manhattan distance were employed to classify these products. Data was sourced from the Indo Premier Online Technology (IPOT) application. Variables utilized in this research include percentage return, Sharpe ratio, unit growth, and Asset Under Management (AUM) . Additionally, Principal Component Analysis (PCA) was employed to reduce data dimensionality by linearly combining correlated original variables into new variables (principal components). PCA was used to visualize data with more than three dimensions. Based on the principal component analysis, the first two principal components captured 74.43% of the original data information, while the first three principal components captured 98.94%. Classification results using three principal components and standardized data showed the same error rates: 13.33% for Mahalanobis distance and 6.67% for Manhattan distance. For the two principal components, both Mahalanobis and Manhattan distances resulted in an error rate of 13.33%. Therefore, Manhattan distance is the most effective method for classification. Forecasting results indicate that mutual fund A is a good investment choice, while mutual fund B is a poor one. Keywords: Mahalanobis distance; Manhattan distance; Principal Component Analysis
Stability Analysis of a Time-Delayed Disease Transmission Model in Prey–Predator Populations Incorporating a Holling Type II Functional Response Nurul Maqfirah Rauf; Paian Sianturi; Jaharuddin Jaharuddin
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7428

Abstract

Abstrak. This article presents a comprehensive study of a mathematical model describing the spread of infectious disease within a prey–predator population, incorporating the Holling type II functional response and a delay parameter, denoted as τ, representing the incubation or infection period. The model captures the interactions among four population groups: susceptible prey, infected prey, susceptible predators, and infected predators. Through analytical investigation, six fixed points (equilibrium points) of the system were identified. The stability of these fixed points was examined using the eigenvalues of the Jacobian matrix, and one locally stable fixed point was found, while the others were identified as saddle points or unstable. To gain deeper insights into the model’s behavior over time, numerical simulations were conducted for different values of the delay parameter . The results indicate that the presence of a time delay significantly affects the dynamics of all four population groups. Specifically, the infection delay can suppress or slow the spread of the disease by delaying the transition from susceptible to infected classes. Oscillatory behavior emerged in certain population groups when the delay was introduced, especially among infected prey and predators, before gradually stabilizing toward the disease-endemic equilibrium. These findings highlight the critical role of time delay in disease transmission dynamics in ecological systems and provide a framework for further research on delay-induced phenomena in epidemiological models. Keywords: Prey–Predator, Disease Spread, Delay Time, Functional Response, Stability Analysis.
Mortality Risk Modeling for Patients with Lung Disease Using Cox Regression and Gradient Boosting Survival at Pertamina Hospital in Balikpapan Nurul Maqfirah Rauf; Isti Kamila; Muhammad Azka; Indrawan Indrawan; Nur Qadri Bahar
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/137g3361

Abstract

Lung disease is a health issue associated with mortality risk; therefore, an analysis is needed to identify factors associated with the risk of death and to model patient survival risk. This study aims to identify factors associated with mortality risk in patients with lung disease using Cox Proportional Hazards Regression and to develop a predictive model using Gradient Boosting Survival. The study utilized medical records from 1,119 inpatients with lung disease at Pertamina Hospital in Balikpapan during the 2024–2025 period. Analyses were conducted using Kaplan-Meier, Cox Proportional Hazards Regression, and Gradient Boosting Survival. Hyperparameter optimization for Gradient Boosting Survival was performed using Grid Search with 5-fold stratified cross-validation on the training data. The results of the Cox Proportional Hazards Regression showed that BMI, diastolic blood pressure, and Chronic Obstructive Pulmonary Disease (COPD) were significantly associated with the risk of mortality, with a C-index of 0.6500. Gradient Boosting Survival yielded the optimal hyperparameters: a learning rate of 0.10, n_estimators of 200, max_depth of 2, and min_samples_leaf of 10, with a C-index of 0.6576 on the training data and 0.5575 on the testing data. Feature importance analysis shows that BMI, diastolic blood pressure, systolic blood pressure, SpO₂, and COPD make the largest contributions to the model. Cox Proportional Hazards Regression provides information on the direction and magnitude of the relationship between risk factors through the hazard ratio, while Gradient Boosting Survival provides information on the relative contributions of variables through feature importance. Thus, both approaches provide complementary information in modeling the risk of mortality in patients with lung disease.
Analisis Investasi Saham Sektor Energi Menggunakan Metode Value at Risk (VAR) dan Conditional Value at Risk (CVAR) dengan Pendekatan Single Index Model: Studi Kasus Indeks LQ45 Tahun 2025 Ayu Nor Aisah; Eka Krisna Santoso; Nurul Maqfirah Rauf
Journal of Economic and Business Advancement Vol. 1 No. 3 (2026): March: Ascendia: Journal of Economic and Business Advancement
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/xxd11m37

Abstract

The energy sector is characterized by high volatility, making comprehensive risk analysis essential for investment decision-making. This study examines the risk and return characteristics of energy sector stocks included in the LQ45 index in 2025 by integrating the Single Index Model with Value at Risk (VaR) and Conditional Value at Risk (CVaR). Daily closing price data from February 2022 to July 2025 are employed to estimate stock returns, market sensitivity, and portfolio risk. The Single Index Model identifies three optimal stocks AKRA, MEDC, and PGAS based on their Excess Return to Beta and positive Zᵢ values, resulting in portfolio weights of 39.6%, 51.8%, and 8.5%, respectively. Monte Carlo simulation is applied to estimate portfolio risk at a 95% confidence level. The results indicate that the portfolio’s VaR reflects a manageable level of potential daily loss, while the CVaR reveals the average loss under extreme market conditions beyond the VaR threshold. Overall, the findings suggest that the optimal energy sector portfolio offers a balanced risk–return profile with controlled exposure to market and tail risks, providing valuable insights for risk-aware investors.                             
Co-Authors Ayu Nor Aisah Eka Krisna Santoso Eka Krisna Santoso Indrawan Isti Kamila Jaharuddin Jaharuddin Muhammad Azka Muhammad Azka Nur Qadri Bahar Paian Sianturi