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Mikailalsys Journal of Mathematics and Statistics
Published by Lembaga Yasin Alsys
ISSN : 30308399     EISSN : 3030816X     DOI : https://doi.org/10.58578/mjms
Core Subject : Education, Engineering,
Engineering Mathematics Mechanical Engineering
The journal contains scientific articles covering topics such as mathematical theory, statistical methods, the application of mathematics in various disciplines, and statistical data analysis. The primary objective of this journal is to promote a better understanding of mathematical and statistical concepts and to encourage advancements in the methods and applications of mathematics and statistics in various contexts. The journal serves as a platform for researchers, academics, and practitioners to share knowledge and the latest research findings in the fields of mathematics and statistics. MJMS publishes three editions a year in February, June, and October.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 79 Documents
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Statistical Estimation and Inference of Board Composition on Financial Performance of Oil and Gas Companies in Nigeria Idi, Danjuma; Stephen, Mathew
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3516

Abstract

This study examines the relationship between board characteristics and financial performance of listed oil and gas firms in Nigeria, highlighting the gap in existing literature on the topic. The purpose of this study is to investigate the impact of board independence, board size, and gender diversity on financial performance. A sample of three listed oil and gas firms on the Nigerian Exchange Group (NGX) was selected, and secondary data from annual financial statements for 2010-2021 were analyzed using panel regression and correlation analysis. The findings reveal that female board members have a positive and significant impact on financial performance, while board independence and board size have a positive but insignificant impact. The study concludes that gender diversity on boards is a key factor in driving financial success, and recommends increasing the number of female board members to improve financial performance. The results contribute to the understanding of the relationship between board characteristics and financial performance in the Nigerian oil and gas industry.
Analysis the Effect of Inflation, Gold Prices in Dollars, Rupiah Exchange to Bank Indonesia Monthly Rates After the COVID 19 Seputro, Dimas Nugroho Dwi; Dani, Andrea Tri Rian; Fauziyah, Meirinda; Adrianingsih, Narita Yuri; Putra, Fachrian Bimantoro
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3767

Abstract

The Covid-19 pandemic has caused economic turmoil to become uncertain, affecting all aspects of Indonesian society's lives. This research aims to determine the relationship between the inflation rate, the transaction price of the last issuer of gold and the rupiah exchange rate that occurred in the period after the Covid-19 pandemic on the monthly interest rate of Bank Indonesia, both together and each variable on the monthly interest rate of Bank Indonesia. This research details the research steps starting from classical assumption test analysis, multiple linear regression, coefficient of determination to hypothesis testing. The research results show that from the inflation rate, the price of gold in dollars together has a significant influence on the dependent variable, namely the Bank Indonesia monthly interest rate. Inflation and gold prices in dollars partially have a significant influence on Bank Indonesia's monthly interest rate, while the rupiah exchange rate variable partially does not have a significant influence on Bank Indonesia's monthly interest rate. Inflation is the most dominant variable in Bank Indonesia's monthly interest rate after the Covid-19 pandemic.
Comparison of Value at Risk (VaR) in Risk Analysis: Historical, Variance Covariance and Monte Carlo Methods Fauziyah, Meirinda; Dani, Andrea Tri Rian; Koirudin, Hadi; Budi, Ennesya Estya; Avrilia, Khairunnisa; Watika, Noor Hikmah
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3778

Abstract

Value at Risk (VaR) is a method used to measure financial risk in a company. VaR calculations are often used to calculate the level of loss from shares in a company, such as bank shares. The aim of this research is to determine the level of losses in Bank Central Asia shares using the historical method, the Variance-covariance method, and the Monte Carlo method. the results showed that with an initial investment of $50 and using the Historical method at a significant level of 95%, the VaR value was obtained at $16.42 or IDR. 267.301 and at the 90% significant level, the VaR value was obtained at $12.41 or IDR. 202.022. Based on the Variance-covariance method with an initial investment of 50$ at the 95% significant level, the VaR value is obtained at $16.42 or IDR. 267,301 and at the 90% significant level, the VaR value is obtained at $12.79 or IDR. 208.208. Meanwhile, based on the Monte Carlo method with an initial investment of $50, at a significant level of 95%, the VaR value is obtained at $16.46 or IDR. 267,952 and at the 90% significance level, the VaR value is obtained at $12.84 or IDR. 209.022. Based on the three methods used, it was concluded that the Monte Carlo method gave greater results compared to the other two methods.
Novel Extended Weibull Regression Model for Investigating the Survival Times of Breast Cancer Patients Abdulkadir, Ahmed; Adubisi, Obinna Damian; Madaki, R. M.
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3840

Abstract

The new five-parameter alpha power generalized odd generalized exponentiated Weibull distribution is introduced, and some of its structural properties are derived. Its parameters are estimated by maximum likelihood, and a simulation study examines the accuracy of the estimates. A regression model is constructed based on the logarithm of the proposed distribution to investigate the survival times of breast cancer patients in Bauchi State, Nigeria. The applicability and flexibility of the novel model is proven by means of cancer dataset.
Inference and Asymmetric GARCH-Model with a New Distributed Innovation Adubisi, O. D.; Adashu, D. J.
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3863

Abstract

A novel generalized-odd-generalized exponentiated skew-t (GOGEST) innovation density for the generalized autoregressive conditional heteroskedasticity (GARCH) models is proposed. The features of the proposed distribution were derived. The parameter estimates of the proposed distribution through simulation were carried-out with maximum likelihood estimation technique. The performance of the asymmetric GARCH-GOGEST model relative to five other asymmetric GARCH-various existing innovation densities in volatility modeling was investigated using the Bitcoin log-returns. The empirical results showed that the asymmetric GARCH-GOGEST models were superior over the other asymmetric GARCH models. However, the threshold GARCH-GOGEST model outperformed the other models in terms of volatility predictability (out-of-sample).
Machine Learning Algorithm for Enhanced Cybersecurity: Identification and Mitigation of Emerging Threats Nnamani, Chinenye Cordelia
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3917

Abstract

Machine learning (ML) methodologies have significantly transformed cybersecurity by offering sophisticated instruments that not only identify but also avert and alleviate cyber threats. This research study seeks to examine the convergence of machine learning and cybersecurity, focusing on diverse methodologies and their use in enhancing cybersecurity measures. The study examines several machines learning methods, including Graph Neural Networks, Adversarial Learning, Federated Learning, Explainable AI, and Reinforcement Learning. Every algorithm is essential for enhancing the identification and mitigation of cyber assaults. Graph Neural Networks facilitate the modelling of intricate linkages within cybersecurity data. It aids not just in forecasting future events but also in identifying anomalies and analyzing network traffic. Adversarial Learning assists in training machine learning models to address the difficulty of producing misleading input data that can deceive any model, hence enhancing their efficacy. Federated Learning is examined as a method for training machine learning models across decentralized networks while preserving data privacy and enhancing model accuracy. Explainable AI methodologies primarily offer transparency and interpretability in machine learning-driven cybersecurity decisions, which are crucial for comprehension and confidence in automated security systems. Reinforcement Learning is focused on a trial-and-error methodology, wherein the model acquires new tasks through a system of rewards and penalties. These sophisticated algorithms jointly improve the effectiveness, precision, and clarity of cybersecurity protocols, offering strong protection against emerging cyber threats.
Lebesgue Measure and Integration on Subsets of R^d Kumar, Nand Kishor; Pokhrel, Chudamani; Yadav, Dipendra Prasad
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 1 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i1.3958

Abstract

Henri Lebesgue, a French mathematician, discovered centuries ago that the Riemann Integral does not work well on unbounded functions. It prompts him to consider another way to integration known as Lebesgue Integral. This paper discusses the Riemann integral's shortcomings and introduce a more thorough concept of integration, the Lebesgue integral, repeated integration. There is also some debate about the Lebesgue measure, which determines the Lebesgue integral. Some examples are given, such as F_σ -set, G_δ -set and Cantor function. In this article, we first look into a unified theory for d-dimensional volume based on the concept of a measure, and then we will use that theory to build a stronger and more flexible theory for integration.
Mathematical Model of Counteract Bandits Terrorism in Nigeria O, Akpienbi Isaac; Jude, Iroka
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 1 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i1.3969

Abstract

This paper explores a mathematical model for analyzing the dynamics of banditry and terrorism, focusing on equilibrium states and stability conditions. Key concepts include the reproduction number Rb, which serves as a threshold parameter indicating the potential for spread or decline of these activities. The study finds that a banditry-free equilibrium is locally asymptotically stable when Rb < 1, suggesting that the activities will decline over time. In contrast, Rb>0 indicates the possibility of globally stable coexistence equilibrium, where banditry and terrorism persist at endemic levels. The model also identifies invariant regions, ensuring that the system's trajectories remain within certain bounds, and guarantees unique and positive solutions, essential for real-world applicability. These findings provide critical insights for developing comprehensive strategies to mitigate banditry and terrorism, highlighting the importance of addressing both root causes and immediate symptoms
Inference of Some Macroeconomic Variables on Nigeria Unemployment Rate John, David Ikwuoche; Saheed, Salawu I.; Mary, Adehi U.
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 1 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i1.4053

Abstract

This research investigates the impact of foreign direct investment (FDI), government expenditure (GOE), and inflation rate (IFR) on the unemployment rate (UPR) in Nigeria from 1985 to 2021 through Autoregressive Distributed Lag (ARDL) modeling approach. An initial assessment to test the significance of signals between each independent variable and UPR was performed using rolling correlation analysis. Subsequently, the bounds test methodology to examine cointegration among between the FDI, GOE, IFR, and UPR was performed. Additionally, the causal relationship between these economic variables was performed through the Error Correction Model (ECM) approach. The estimated ARDL model parameters stability was determined using the cumulative sum (CUSUM) of squares chart. The Augmented Dickey Fuller unit root test suggests that the variables are stationary at first differences (I(1)). The bounds test revealed that the variables are cointegrated at 1%, 5%, and 10% indicating a long run relationship between UPR and FDI, GOE, and IFR. The ARDL results indicates that at 5%, a unit increase in FDI at lag one have a long run significant decreasing impact on UPR by 19.96%. But in the short run, the FDI at lag one has a significant increasing effect on UPR by 7.23%. However, the CUSUM of square chart shows unstable parameter estimation based on the Akaike Information Criteria selected model, ARDL (1, 3, 0, 0). The study concludes that UPR is being influenced by FDI in reducing UPR in the long run. Recommendation based on the findings is that FDI should be considered most important when policies are drafted for tackling the issue bordering UPR in Nigeria.
A Study on Alzhemier Disease in Takum, Taraba State, Nigeria: An ARIMA Model Approach Akobi, Clement; Idi, Danjuma; Michael, Ibrahim; Stephen, Mathew
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 1 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i1.4113

Abstract

Alzheimer’s disease, a progressive neurodegenerative disorder, represents a significant and growing public health burden, particularly as life expectancy increases worldwide. In sub-Saharan Africa, including Nigeria, the disease's prevalence is rising due to aging populations and urbanized lifestyles that elevate risk factors for cognitive decline. This study investigates the trends and projected incidence of Alzheimer’s disease in Takum, Taraba State, Nigeria. The research utilizes patient records from General Hospital Takum, spanning from 2012 to 2021. Following diagnostic tests and data transformations, the ARIMA(1,2,0) model was selected as the best fit for predicting future case counts. The findings reveal a steady increase in Alzheimer’s cases, consistent with global patterns, highlighting the need for proactive measures in healthcare planning. The study thus recommends the need for increased public awareness, investment in diagnostic infrastructure, and support systems for caregivers.

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