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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

Classification of Money Market Mutual Fund Products in Indonesia By Using Mahalanobis Distance and Manhattan Distance Indrawan; Azka, Muhammad; Kamila, Isti; Rauf, Nurul Maqfirah; Santoso, Eka Krisna
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 Rauf, Nurul Maqfirah; Sianturi, Paian; 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.