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
54 Documents
<![CDATA[Optimization of Stock Portfolio in Indonesian Health Sector using Markowitz Modern Portfolio Theory ]]>
<![CDATA[Kalfin]]>;
<![CDATA[Hidayana, Rizki Apriva]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i1.182
<![CDATA[This study analyzes the optimization of the health sector stock portfolio on the Indonesia Stock Exchange using the Markowitz Modern Portfolio Theory method. The data used are the daily closing prices of health sector stocks over the last three years obtained through web scraping techniques from Yahoo Finance. The analysis includes the calculation of daily returns, daily risks, and covariance matrices between stocks. The results of the portfolio optimization show that out of the ten stocks analyzed, the optimal portfolio consists of four stocks, namely MIKA.JK (62.82%), KLBF.JK (15.58%), CARE.JK (15.37%), and SAME.JK (6.23%). This portfolio generates a daily return of 0.216% with a risk level of 1.996%. MIKA.JK contributes the highest return of 0.02063% with a risk of 1.52601%. This study provides guidance for investors in optimizing fund allocation in the health sector stock portfolio in Indonesia.]]>
<![CDATA[Prediction Of Cigarette And Tobacco Price Index In Tangerang City Using Ses And Double Linear Exponential Smoothing ]]>
<![CDATA[Saudi, Jeremy Heriyandi]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 1 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i1.183
<![CDATA[The cigarette and tobacco price index is a crucial indicator that reflects changes in prices and demand in the tobacco market. Accurate predictions of this index are essential for the government and industry players in planning policies and business strategies. This study aims to forecast the cigarette and tobacco price index in Banten Province using the Single Exponential Smoothing (SES) and Double Linear Exponential Smoothing (DES) methods. The data used in this research comprises monthly cigarette and tobacco price index data from January 2021 to December 2023. SES and DES models are applied for prediction, and their results are evaluated using performance indicators such as Mean Absolute Percentage Error (MAPE). The research findings indicate that both methods are highly effective in predicting the cigarette and tobacco price index, with the SES method providing slightly more accurate predictions than the DES method. The MAPE error value for the SES method is 0.51%, while the DES method has a MAPE error value of 0.65%. These results are expected to contribute to policymakers and industry players in understanding price trends and making more informative decisions.]]>
<![CDATA[APPLICATING KAPLAN-MEIER SURVIVAL ANALYSIS ON EMPLOYEE’S TENURE BY OVERTIME AVAILABILITY STATUS ]]>
<![CDATA[Widjaya, Ferdinand Nathaniel]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.201
<![CDATA[This study aimed to analyze the impact of overtime work on the tenure of employees using the Kaplan-Meier method. The data used in this research encompassed employees’ tenure records and their overtime work status in a specific company. Utilizing the Kaplan-Meier method, the analysis revealed that employees engaged in overtime work exhibited a tenure end probability higher than those who did not. The Kaplan-Meier Survival Analysis shows a much steeper step-down probability for Overtime than those who don’t. Despite the substantial disparity in tenure probabilities on the graph, furthermore, statistical analysis (log-rank test) indicated that an employee engaged in overtime work did significantly influence the duration of their tenure. These findings provide crucial insights into workload during overtime, and overtime work policies within organizations could affect the employee significantly.]]>
<![CDATA[Matching Riders to Drivers Under Uncertain Wait Times in Ride-Hailing Systems: A Robust Optimization Approach with Box Uncertainty ]]>
<![CDATA[Megantara, Tubagus Robbi]]>;
<![CDATA[Hidayana, Rizki Apriva]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.202
<![CDATA[The advent of ride-hailing systems has revolutionized urban mobility, yet efficient vehicle assignment remains challenging due to inherent uncertainties in passenger waiting times. This study addresses the ride-hailing matching problem under uncertain wait times, proposing a robust optimization model with a box uncertainty set to mitigate the impact of variability in service delivery. We first contextualize the problem by examining the evolution of transportation systems, emphasizing how ride-hailing services complicate traditional matching paradigms. Existing approaches often fail to account for real-world unpredictability, leading to suboptimal assignments. To bridge this gap, we formulate a data-driven robust optimization framework that bounds waiting time fluctuations within a box uncertainty set, ensuring reliable performance under worst-case scenarios. Using simulation data from Manhattan taxi trips, we compare our robust model against deterministic benchmarks, demonstrating its superiority in reducing average waiting times and enhancing system reliability, even under high uncertainty. Our results highlight the practical viability of robust optimization for ride-hailing platforms operating in dynamic environments.]]>
<![CDATA[Stock Price Prediction of PT. Pertamina Geothermal Energy Tbk Using Gated Recurrent Unit (GRU) Model ]]>
<![CDATA[Saputra, Renda Sandi]]>;
<![CDATA[Hasan, Mohammad Tanzil]]>;
<![CDATA[Azahra, Astrid Sulistya]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.203
<![CDATA[This study aims to predict the stock price of PT. Pertamina Geothermal Energy Tbk (PGEO.JK) using the Gated Recurrent Unit (GRU) model, a neural network architecture in the Recurrent Neural Network (RNN) category that is known to be effective in handling time series data. The data used is historical stock price data from 2022 to 2024 taken from Yahoo Finance. The GRU method was chosen because of its ability to remember long-term information and overcome the vanishing gradient problem. In the research process, the data was divided into two parts, namely training data and testing data. The GRU model was trained without adjusting hyperparameters to measure its performance by default. Model evaluation was carried out using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²) metrics. The results of the study indicate that the GRU model is able to provide good prediction results with an RMSE value of 0.0271, MAE of 0.0180, MAPE of 22.25%, and an R² value of 0.9112. These values indicate that the GRU model is quite accurate in predicting the price of PGEO.JK shares. These findings indicate that GRU is a potential method in stock prediction analysis, especially in the renewable energy sector.]]>
<![CDATA[Application of the Leslie Matrix Model in Predicting Population Growth Rates and Livestock Harvesting ]]>
<![CDATA[Muntafi’ah, Naailah]]>;
<![CDATA[Prabowo, Agung]]>;
<![CDATA[Suroto]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.204
<![CDATA[Leslie matrix model is a population growth model that can be used to predict the number and growth rate of populations that are female in population and female in animals. In animal populations, the Leslie matrix can be used in harvesting. This study aims to apply the Leslie matrix model to predict the number and growth rate of female cattle and determine the proportion of female cattle population harvesting. The female cattle populations used were female dairy cattle and female beef cattle. The results showed that the prediction of female dairy cattle population in 2022 - 2025 decreased every year. As for the female beef cattle population, the prediction results show that the number populaton always increase every year. Furthermore, to determine harvesting is only applied to the female beef cattle population. For uniform harvesting, the result is 11.4% of the population of each class and for the youngest class, the result is 50.5% of the population of the first age class.]]>
<![CDATA[Application of Benford's Law to Detect Fraud in Customers’ Ending Balances Using First Digit Test, Second Digit Test, and First Two Digits Test ]]>
<![CDATA[Firdaus, Muhammad Ilham]]>;
<![CDATA[Prabowo, Agung]]>;
<![CDATA[Istikanaah, Najmah]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.205
<![CDATA[Bank fraud involves several actions such as manipulating duplication, forgery, changing accounting records and so on. This study aims to detect the potential for fraud in banking reports on customer final balances. The types of tests used to detect potential fraud in this study are the First Digit Test, Second Digit Test and First Two Digit Test Benford's Law. Benford's law states that the proportion of occurrences of numbers in certain numbers is not the same. In addition to the three Benford's Law tests, further statistical tests were carried out to determine the magnitude of the deviation between the actual proportion of occurrences and the expected proportion of Benford's Law using the Mean Absolute Deviation (MAD), Chi-square test, and Z test. This study uses secondary data on the final balance of customer deposits as of July 2023 as much as 20,105 data. The results showed that there were indications of fraud in the form of rounding and duplication of data on the customer's final balance. MAD results show that the proportion of occurrence of actual numbers is quite consistent with the proportion of occurrence of Benford's Law expectations. Based on the Z test, the balance that has the potential for fraud is the value with the first digit '5', the second digit '3' and the first two digits '23'. These numbers can be found in balances with a nominal value of Rp5000 and Rp5636 in the first digit '5', and Rp23038 in the second digit '3' and the first two digits '23'.]]>
<![CDATA[Clustering of Sub-districts in Cilacap Regency Based on the Number of Health Facilities, Active KB Participants, and Population Growth Rate Using K-Means Cluster Analysis ]]>
<![CDATA[Afriyanti, Marshella]]>;
<![CDATA[Sugandha, Agus]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 2 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i2.206
<![CDATA[This research examines clustering of sub-districts in Cilacap Regency based on the number of healthcare facilities, active family planning (FP) participants, and population growth rate using K-Means Cluster analysis. The study was conducted at the Cilacap Regency Statistics Bureau using 2023 data. The purpose of this research is to identify differences in each subdistrict’s characteristics to support precise policy formulation. The analysis uses R 4.4.1 software and involves Euclidean distance measurement techniques and standard deviation to determine data similarity between sub-districts. Based on the results, five clusters with unique characteristics were identified. Clusters 1 and 2 have moderate levels of healthcare facilities and FP participants, while Cluster 3 represents sub-districts with the highest number of healthcare facilities and FP participants. Cluster 4 has a low population growth rate, while Cluster 5 includes sub-districts with the highest growth rate. This clustering provides critical information for the allocation of health resources and effective implementation of FP programs. Recommendations for further research include adding related factors to refine clustering results and provide deeper insights for decision-making in the regional health sector.]]>
<![CDATA[The Implementation the Pegel’s Classification to Forecast Rice Prices Based on Quality at The Milling Level: Forecast Rice Prices ]]>
<![CDATA[Ramadhani, Sausan]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.207
<![CDATA[Rice is the primary food of the Indonesian people and plays an important role in various aspects. In fact, the increase rice prices in Indonesia has occurred from 2014 until now. The burden on society continues to occur when the discourse of increasing PPN issue by 12% in 2025 is delivered by the government. Business actors, especially food and consumers, are very influenced by this policy. Against this background, this study analyzes the average prediction of rice prices at the milling level according to quality. The Pegel’s Classification is the technique employed in this research. The data used in this study from Badan Pusat Statistik (BPS). The predicted price of premium or medium rice less than IDR 14,000 with a difference of no more than IDR 700 for each quality. February 2025 is the predicted highest average price for rice for each quality, with a decrease in March. Pegel’s B-2 model is an best effective model because it has a Mean Absolute Percentage Error (MAPE) value of 2.02%.]]>
<![CDATA[Prediction of The Electricity Capacity Ready to Sell in DKI Jakarta Using Holt's Linear Exponential Smoothing and Arima Methods ]]>
<![CDATA[Agatha, Dhela Asafiani]]>;
<![CDATA[Wiyanti, Wiwik]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.209
<![CDATA[The point of this study is to look at how well Holt's Linear Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) can predict time series data that have trend and non-seasonal characteristics. The information on the power capacity available for sale (kWh) at DKI Jakarta serves as the case study. It is anticipated that this study will serve as a guide for choosing efficient techniques for data types with trend and non-seasonal characteristics. This study uses a quantitative methodology with the application of Holt's Linear Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA). A total of 36 data points—monthly data from January 2020 to December 2022—were used in this study. From the analysis results, the error accuracy level was obtained based on the MAPE calculation, namely 3.18% for Holt's Linear Exponential Smoothing. Meanwhile, the best model with the ARIMA method is ARIMA(3,1,1) with a MAPE value of 3.124%. Based on the forecast results from January to March 2023, the predictions with the best model, namely ARIMA(3,1,1), are 3,140,106,571 kWh, 3,149,746,276 kWh and 3,154,664,915 kWh.]]>
