International Journal of Mathematics, Statistics, and Computing
International Journal of Mathematics, Statistics, and Computing (IJMSC) is an official journal of the Communication in Research and Publications (CRP) and publishes original research papers that cover the theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). IJMSC will act as a platform to encourage further research in Mathematics, Statistics, and Computing, theory and applications. The rapid development of science and technology has had a significant impact on various aspects of human life, including in the fields of economy, education, culture and government. The positive impacts of science and technology include facilitating access to information and communication, accelerating production and service processes, as well as providing new business and investment opportunities. Mathematics, statistics, and computer science have a very important role for the advancement of science and technology. Among them are as a basis for computer programming, basic calculations in the development of modern tools, can solve a problem even with big data. The mission of the International Journal of Mathematics, Statistics, and Computing (IJMSC) is to enhance the dissemination of knowledge across all disciplines in theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). The above discipline is not exhaustive, and papers representing any other social science field will be considered. The IJMSC particularly encourage manuscripts that discuss the latest research findings or contemporary research that can be used directly or indirectly in addressing critical issues and sharing of advanced knowledge and best practices in Mathematics, Statistics, and Computing (MSC). The essential but not exclusive, audiences are academicians, graduate students, researchers, policy-makers, regulators, practitioners, and others interested in business, management, economics, and social development studies. For ensuring a wide range of audiences, this journal accepts only the articles in English. The scope of mathematics are: Algebra, Applied Mathematics, Financial Mathematics, Approximation Theory, Combinatorics, Computing in Mathematics, Operations Research Methodology, Discrete Mathematics, Mathematical Physics, Geometry and Topology, Logic and Foundations of Mathematics, Number Theory, Numerical Analysis, and other relevant matters. The scope of statistics are: Probability Theory, Central Limit Theorem Computation, Sample Survey, Statistical Modeling, Statistical Theory, Computational Statistics, Data Sciences, Actuarial Sciences, Regression Models, Time Series Models, and other relevant matters. The scope of computing are: Algorithms and Data Structures, Computer Architecture, Software Engineering, Artificial Intelligence and Robotics, Human and Computer Interaction, Informatics Organizations, Programming Languages, Operating Systems and Networks, Databases, Computer Graphics, Computing Science, BioInformatics, Information Technology, and other relevant matters.
Articles
60 Documents
<![CDATA[Construction of the Mortality Table with Gompertz's Law Using the 2019 TMI Reference ]]>
<![CDATA[Janna, Nur]]>;
<![CDATA[Tri Ayu Mulyani]]>;
<![CDATA[Monica Dwi Kusumaningsih]]>;
<![CDATA[Nur Amel Fitriyan]]>;
<![CDATA[Agung Prabowo]]>;
<![CDATA[Yaza Azzahara Ulyana]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 4 (2023): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v1i4.58
<![CDATA[The Indonesian Mortality Table, which was specifically based on the real conditions of Indonesia, was successfully created for the first time in 1993. The mortality table was called the Indonesian Mortality Table (IMT) I of 1993. In its preparation from IMT II to IMT III, the time span was long enough that the Asosiasi Asuransi Jiwa Indonesia (AAJI) and Persatuan Aktuaris Indonesia (PAI) formed a team to prepare the Indonesian Mortality Table IV (IMT IV) which studied data during the study period between 2013-2017. Finally, the IV Indonesian Mortality Table was compiled in 2019. In this study, the mortality table used was the 2019 Indonesian Mortality Table issued by Persatuan Aktuaris Indonesia (PAI), as well as a modification of the 2019 IMT with Gompertz Law. In Gompertz's Law, one method that can be used is the least power square method. This study will show the suitability of Gompertz's Law against TMI 2019 for men and TMI 2019 for women, as well as to determine the suitability of Gompertz's Law using the Least Square Method for IMT 2019 for men and IMT 2019 for women.]]>
<![CDATA[Bayes Estimator for Dagum Distribution Parameters Using Non-Informative Prior Rules with K-Loss Function and Entropy Loss Function ]]>
<![CDATA[Asti Ralita Sari]]>;
<![CDATA[Sirait, Haposan]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 1 No. 4 (2023): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v1i4.59
<![CDATA[The parameter estimator discussed is the p parameter estimator of the Dagum distribution with the K-loss function and the entropy loss function using the Bayes method. To get the Bayes estimator from the scale parameter of the Dagum distribution, the Jeffrey non-informative prior distribution is used based on the maximum likelihood function and the loss function for the K-loss function and the entropy loss function to obtain an efficient estimator. Determination of the best estimator is done by comparing the variance values generated from each estimator. An estimator that uses the entropy loss function is the best method for estimating the parameters of the Dagum distribution of the data population with efficient conditions met.]]>
<![CDATA[Optimizing the LQ45 Stock portfolio using Piecewise Linear Function: A Case Study from an Investor's Point of View ]]>
<![CDATA[Azis, Chusnul Chatimah]]>;
<![CDATA[Halim, Nurfadhlina Abdul]]>;
<![CDATA[Suhaimi, Nurnisaa binti Abdullah]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.61
<![CDATA[In optimization problem solving, both linear and nonlinear approaches can be used, with nonlinear programming considering constraints or not. One effective method of nonlinear programming is the Piecewise Linear approach, which breaks down complex nonlinear functions into straight-line segments to make their solution easier. This method can be applied in the financial sphere, such as in stock investment. This study discusses the application of piecewise linear function in optimizing investment portfolios in Bank Jago Tbk. (ARTO), Barito Pacific Tbk. (BRPT), and Go To Gojek Tokopedia Tbk. (GOTO) stocks. The purpose of this study is to provide insight that Piecewise Linear can provide optimal solutions in managing investment portfolios by calculating the risks and returns of selected stocks. The results showed a risk with a level ???? as big as 0.01, the expected profit from the investment of the three stocks in the calculation period reached IDR 1,803,109. On the other hand, for investors who are more cautious and have a Level ???? as big as 1, the anticipated profit in the same investment is around IDR 1,275,052.]]>
<![CDATA[Application of the Compound Interest Model in Analyzing Long-Term Investments on Nvdia Company Stock ]]>
<![CDATA[Sanita, Indri]]>;
<![CDATA[Ananta, Galen]]>;
<![CDATA[Laksito, Grida Saktian]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.62
<![CDATA[Long-term investment is a strategic decision that requires careful planning and in-depth analysis. One approach to analyze the potential returns from long-term investments is through the application of the compound interest model. This model considers the accumulation of interest on both the principal amount and the previously earned interest, providing a more realistic picture of investment growth over time.This research aims to explore how the application of the compound interest model can enhance the analysis of long-term investments. The analysis of these variables provides a comprehensive picture of the dynamics of investment growth and opens up opportunities for more informed investment decision-making. This research emphasizes the importance of a thorough understanding of the key variables in the compound interest model to enhance the accuracy of investment decisions.The findings of this research are expected to offer practical guidance for investors and financial practitioners in managing their portfolios more intelligently and measurably. These findings make a significant contribution to the development of more effective long-term investment strategies, thereby enhancing portfolio performance and overall investment outcomes.]]>
<![CDATA[Optimizing Investment Strategies: A Case Study on JPMorgan Chase & Co. Stock Options Using the Black-Scholes Model and What-If Analysis in Excel ]]>
<![CDATA[Fernanda, Adeliya]]>;
<![CDATA[Putri, Najmah Rizqya Maliha]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.63
<![CDATA[This research focuses on applying the Black-Scholes Model to evaluate European options on JPMorgan Chase & Co. stocks. This model has been a critical foundation in evaluating financial instruments, especially options, since its development in 1973 by Fisher Black, Myron Scholes, and Robert Merton. The study utilizes secondary data from some sources to obtain current information regarding stock prices, strike prices, expiration time, volatility, and relevant risk-free interest rates for option valuation as of December 19, 2023. Through this approach, our aim is to gain a better understanding of how the Black-Scholes Model is used as a framework in determining option prices for these stocks. The research methodology involves What-If analysis, exploring variations in key variables such as current stock price, strike price, expiration time, stock price volatility, and risk-free interest rates to assess how these changes affect the prices of both call and put options. Additionally, the study presents graphs representing stock prices, strike prices, interest rates, time, and volatility to visually support the research findings. The analysis results reveal that the prices of both call and put options are highly responsive to changing market conditions. An increase in the current stock price tends to raise the call option price while reducing the put option price. Conversely, an increase in the strike price has the opposite effect. Moreover, variations in the risk-free interest rates influence the option prices, with rising rates increasing the call option price and decreasing the put option price. Furthermore, as the expiration time approaches or stock price volatility increases, both call and put option prices tend to rise. These findings provide a comprehensive understanding of the dynamics of JPMorgan Chase & Co.'s stock option pricing, offering a foundation for investors to make informed and adaptable investment decisions amid constantly evolving financial markets. Sensitivity to changes in key variables is an essential aspect to consider in designing effective investment strategies in the face of ever-changing financial markets.]]>
<![CDATA[The Implementation of Roy's Safety-First Criterion in Stock Portfolio Selection ]]>
<![CDATA[Amal, Moh. Alfi]]>;
<![CDATA[Laksito, Grida Saktian]]>;
<![CDATA[Salih, Yasir]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.64
<![CDATA[Statistical data shows that the Indonesian capital market has experienced significant growth. This growth is attributed to public awareness of the benefits of stock investments. However, with an increasing number of new investors entering the stock market, attention to investment risks deepens. Many investors prefer stocks that are easily predictable and have low risk, as higher volatility increases the level of uncertainty in obtaining returns. The relatively high risk level in investing requires investors to minimize risks, one of which is by diversifying funds into various investment assets, commonly known as optimal portfolio selection. An optimal portfolio can be formed using various methods and approaches. One method for portfolio selection is the application of the Safety First Criterion, a method dependent on downside risk, referring to risks that result in losses. This article conducts a simulation of the implementation of Roy's Safety First Criterion using stock data from the largest companies in eleven sectors over the past year. These sectors include basic materials, communication services, consumer cyclical, consumer defensive, energy, financial services, healthcare, real estate, technology, and utilities. Based on this analysis, out of the eleven stocks, six stocks meet Roy's criteria: LIN, GOOG, AMZN, BRK-B, LLY, and AAPL, with respective weights of LIN=10.84%, GOOG=12.61%, AMZN=24.67%, BRK-B=1.37%, LLY=34.17%, and AAPL=16.35%. Using Roy's Safety First Criterion for selected stocks from various sectors indicates that the resulting portfolio has a very low risk level, specifically 1%. This means that by using the portfolio obtained from the eleven stocks, investors can achieve minimal risk, making this portfolio secure for new investors.]]>
<![CDATA[Multiobjective Optimization of Stock Portofolio ]]>
<![CDATA[Cahyandari, Rini]]>;
<![CDATA[Kamelia, Susan]]>;
<![CDATA[Rusyn, Volodymyr]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.65
<![CDATA[Diversification is a method used to reduce risks by allocating several financial, industrial, and other instruments. Investors might need to use this method to allocate their companies’ funding as efficient as they should be. Mean variance portfolio is a diversification theory designated for investors who are aware of potential risks. On the other hand, multi-objective portfolio optimization is another theory that suits for investors who are more unaware, or choose to neglect potential business risks. Multi-objective optimization can boost source of income and minimize the risks while utilizing k weighting coefficient as risk aversion index. This research aims to form an optimal portfolio from each perspective of selected investors using multi-objective optimization, as well as to analyze the benefits and risks that the investors will have. Samples used in this research are sharia stocks actively involved in Jakarta Islamic Index (JII) and non-sharia stocks which are actively involved in LQ-45 from January 2013 to January 2018.]]>
<![CDATA[Factors Affecting Cases of Dengue Hemorrhagic Fever in Riau Province ]]>
<![CDATA[fitriani, selvi]]>;
<![CDATA[sirait, haposan]]>;
<![CDATA[nurnisaa]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 1 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i1.66
<![CDATA[One infectious disease that has a high morbidity and mortality rate is Dengue Hemorrhagic Fever. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus transmitted through the bite of the Aedes aegypti mosquito. In general, the habitat of Aedes mosquitoes is in areas with tropical climates, high rainfall, and hot and humid temperatures. The number of patients and the area of distribution are increasing along with increasing mobility and population density. Improper sanitation can also be a cause of DHF. In Riau province, dengue cases in 2022 continue to increase compared to 2021, with the most cases in Pekanbaru City. This study was conducted to see the factors that influence dengue cases in Riau Province. Using multiple linear regression can measure what factors affect the number of dengue cases. From the results, it was found that population density and sanitation had a significant effect on dengue cases in Riau Province. And judging from the coefficient of determination, it can be interpreted that the variables of population density (X1) and sanitation (X2) simultaneously affect the variable of dengue cases (Y) by 77.8%. While the remaining 22.2% was influenced by other variables that were not studied in this study]]>
<![CDATA[Analysis and Visualization of Advertising Sales Data Using Python Software Through an Internship Program ]]>
<![CDATA[Ardiansyah, Muhammad Naufal]]>;
<![CDATA[kalfin]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 2 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i2.76
<![CDATA[This research discusses the use of data visualization as a tool for analyzing and presenting information effectively. The main goal of this research is visualization that allows better understanding of complex data. In this context, research explores various data visualization techniques, including the use of bar charts, correlation heatmaps, and interactive technology to present information intuitively. In improving decision-making abilities, identifying patterns or trends, and exploring relationships and insights contained in large and heterogeneous datasets. The research methodology includes comparative analysis of various visualization methods, user experiments, and the application of new techniques to evaluate the effectiveness and usefulness of data visualization in advertising video sales data at PT XXX. Based on the research results, it was found that data visualization involves presenting data information in graphic or image form to facilitate understanding. This helps in explaining the facts and determining the steps that need to be taken. The research results are expected to provide in-depth insight into the development of data visualization techniques. The results of this data visualization can be widely applied in various fields, especially creative, marketing and sales management.]]>
<![CDATA[Comparison of Weights in Weighted Least Square Method For Handling Heteroscedasticity on Multiple Regression Model ]]>
<![CDATA[Virgantari, Fitria]]>;
<![CDATA[Widyastiti, Maya]]>;
<![CDATA[Ir Seno, Natalia]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 2 No. 2 (2024): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v2i2.93
<![CDATA[Regression analysis is the most popular and commonly used to determine causality between two or more variables. In regression analysis there are several assumptions that must be held, so that the property of the best linear unbiased estimator (BLUE) is still guaranteed. In fact, we often found violations of the assumptions. One of them was violations of the homoscedasticity or occurs heteroscedasticity. The impact of heteroscedasticity in the regression model is that the ordinary least square (OLS) estimator no longer has a minimum variance although still linear and unbiased. To handle this, weighted least square (WLS) regression is used instead, which giving weights on the observations. But the problem often encountered is choosing which the best weight in WLS method. This paper aimed to compare and determine the best weight among 1/X, 1/ , 1/Y and 1/s in multiple regression model. Human development index factors data, which were obtained from the Indonesian Central Bureau of Statistics, were used. The results showed that the best weight on human development index data was 1/s. The coefficient of determination was 98.7% indicating that the model was very good.]]>
