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INDONESIA
JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Published by Universitas Hasanuddin
ISSN : 18581382     EISSN : 26148811     DOI : -
Core Subject : Education,
Mathematics
Jurnal ini mempublikasikan paper-paper original hasil-hasil penelitian dibidang Matematika, Statistika dan Komputasi Matematika.
Arjuna Subject : -
Articles 496 Documents
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Covid-19 Vaccination Impact on Four Asean Countries’ Stock With Spatial Dependency: A Comparison of Panel and Geographically Weighted Regression Marizsa Herlina; Shafira Rizq; Eti Kurniati; Nabila Zahratu Fuadi
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36177

Abstract

Research about various policies and responses toward COVID-19 cases and its impact on stocks has grown recently. It shows that spatial influence is one of the keys in this research. The pandemic is not free from spatial dependence regarding how it indirectly impacts a country’s economy. Each country has different policies to handle COVID-19, such as lockdowns and vaccination. WHO stated that all countries require vaccination to build human immunity against COVID-19 in the future. Naturally, ASEAN implemented this policy; thus, it is crucial to see the extent of the impact of vaccination on the ASEAN economy. However, the residuals have heterogeneity problems when using the panel regression model. One of the reasons is that there is spatial dependence, especially when modeling the COVID-19 pandemic. Therefore, comparing panel regression with a geographically weighted regression panel (GWR-Panel) is substantial when exploring the reaction of stock returns to vaccination and positive cases of COVID-19 in Indonesia, Malaysia, Singapore, and Thailand
Classification of Unisba Students' Graduation Time using Support Vector Machine Optimized with Grid Search Algorithm Ilham Faishal Mahdy; Muthia Nadhira Faladiba; Nur Azizah Komara Rifai; Indah Siti Rahmawati; Andhika Sidiq Firmansyah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36257

Abstract

Support Vector Machine is a classification method that finds the optimal hyperplane to separate two data classes. SVM has much better generalization performance than other methods. However, SVM needs to improve in determining hyperparameter values. Therefore, parameter optimization is necessary to determine the optimal hyperparameter value. Grid search is one of the parameter optimization methods that can improve the quality of SVM models. This study aims to assess the level of accuracy in predicting student graduation times by using five features that affect it. This study shows that the resulting SVM model optimized with the Grid Search Algorithm is quite consistent and prevents overfitting. By utilizing the results of SVM modelling, UNISBA is expected to improve the quality of graduates. The risk of delays in graduation can be considered early by paying attention to the background and achievements of students
Penjadwalan Dokter dan Perawat IGD Menggunakan Algoritma Kunang-Kunang Hulliyatul Khoiriyyah; Khusnul Novianingsih; Al Azhary Masta Masta
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36294

Abstract

The Emergency Room (ER) is a part of the hospital responsible for providing initial treatment to patients with life-threatening conditions. The operational hours of the ER follow the schedule set by the hospital. ER must be ready to serve emergency patients 24 hours a day and 7 days a week. Therefore, the scheduling of doctors and nurses in the ER needs to be well-managed to enhance the efficiency of doctors and nurses in responding emergency patients quickly and effectively. In this study, the problem of doctors and nurses scheduling in the ER is solved using the Firefly Algorithm, in which doctors and nurses represented as fireflies. This algorithm is chosen since its ability to find optimal solutions for complex optimization problems. In this research, doctors and nurses can submit schedule requests to improve job satisfaction. The optimization model is constructed by a number of constraints including the availability of doctors and nurses, schedule requests, and the operational needs of the ER. The Firefly Algorithm is applied to find the optimal solution for the model. Simulation results show that this algorithm can produce an optimal schedule, in which 70.6% of doctors' schedule requests and 98.2% of nurses' schedule requests are being fulfilled.              
Optimasi Portofolio Saham Indeks Bisnis 27 Menggunakan Model Black Litterman Disertai Perhitungan Value At Risk: Bahasa Indonesia Melasarah Deswita Rahmadi; Lailany Yahya; Agusyarif Rezka Nuha
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36306

Abstract

An investment can provide a profit with a certain level of risk for an investor both now and in the future. This indicates that investments are important in both financial and asset management. Finance investments can be made on several stocks or portfolios. To profit from an investment, you need a tool to optimize profit and risk, which is a portfolio. This research uses the Black Litterman Model in portfolio optimization along with Value at Risk (VaR) calculations to determine the risk of each stock. The data used is close price data on the Business Index 27 for the period January-December 2023. Next, selected 9 shares in the formation of an optimal portfolio namely, AKRA, AMRT, ASII, BBCA, BBNI, BBRI, INKP, KLBF and TLKM. Based on the calculations, the rate of profit achieved on the portfolio is 5.48% with a risk of 0.40%. Then use the Historical Method and the Monte Carlo Simulation Method to calculate the VaR using nine optimal stocks, with a 95% confidence rate. In the Monte Carlo simulation, 300 repetitions of VaR calculations are performed. Different results on both methods are due to different approaches to risk calculation
Dimensi Metrik Graf Hasil Kali Graf Lengkap Orde Dua terhadap Graf Sarang Lebah Saskia Nurul Jannah; Hasmawati Basir; Naimah Aris
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36329

Abstract

Metric dimension is a concept in graph theory that has been developed in terms of the concept and its application. Let G be a connected graph and S be a vertex subset on connected graph G. The set S is called a resolving set for G if every vertex on graph G has a distinct representation of one to each other of S. A resolving set containing a minimum cardinality is called basis. The metric dimension on graph G is cardinality of basis on graph G, notated with dim (G). In this case, the cross-product graph will be used for the research. The aim of this research is to determine the metric dimension of the second order complete graph (K2) with honeycomb networks (HC(n)) cross-operation product. Utilizing mathematical induction, we generated dim(K2×HC(n)) = 3.
The Comparison of Inverse Gaussian and Gamma Regression: Application on Stunting Data in Jepara Eva Khoirun Nisa; Riska Maulina
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36351

Abstract

Many research data have distributions other than the normal distribution, called exponential family distributions. The exponential family of distributions includes the inverse Gaussian and Gamma distributions. There are parallels between these two distributions in terms of the kind of random variable and how well they work. Finding the optimal model using inverse Gaussian and Gamma regression on stunting data in Jepara is the goal of this study. Maximum Likelihood Estimation is used for parameter estimation, Maximum Likelihood Ratio Test is used for simultaneous parameter testing, and Wald testing is used for partial parameter testing. For this case, the best model is inverse Gaussian regression. Exclusive breastfeeding, low birth weight babies, clean drinking water facilities, and the number of Integrated Service Post (Posyandu) influence the percentage of stunting in Jepara..
Metode Machine Learning-Based Univariate Time Series Imputation Method untuk Estimasi Nilai Hilang pada Data Non-Stasioner Dini Ramadhani; Agus Mohamad Soleh; Erfiani Erfiani
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36468

Abstract

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.
Partition Dimension of the Sum Product of Complete Graph K_1 and Saw Graph GR_n Jusmawati Massalesse; Dermawan Saputra; Naimah Aris
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.32376

Abstract

Let   and  let denote the distance between dan . The distance of  to a subset  is denote by  where  Furthermore, suppose   is an ordered partition with  for  then the representation of a vertex  with respect to  is the ordered k-tuple dinoted by . The partition  is called a distinguishing partition of  if  for every .   A distinguishing partition of  with the smallest cardinality is called the minimum distinguishing partition of , and its cardinality is called the partition dimension of . The purpose of this study is to determine the partition dimension of the join graph  and . By applying the concepts of equivalent vertices and vertices of the same level, it is shown that the partition dimension of the graph  is  where  is a natural number.
Determining Factors that Influence Unmet Need For Family Planning Using Geographically Weighted Logistic Regression With LASSO: Dian Ayu Permata Sari Rusdy; Sri Astuti Thamrin; Anna Islamiyati
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.35081

Abstract

Binary logistic regression is a regression used for categorical response variables with two possibilities: success or failure. This regression is a global model, making it inappropriate for spatial data. Binary logistic regression was then developed into geographically weighted logistic regression (GWLR). GWLR considers location factors into the model through a weight function. Nevertheless, GWLR is unable to overcome multicollinearity issue. Multicollinearity can cause the estimated parameters to be insignificant, thus it needs to be solved. A method to deal with multicollinearity is least absolute shrinkage and selection operator (LASSO). LASSO is applicable to various areas, including health, namely in the case of unmet need for family planning (FP). Unmet need for FP refers to productive-age women who do not wish to have more children or wish to postpone having children without using contraceptive methods. This study aims to obtain GWLR model with LASSO and influential factors, and acquire the performance of GWLR model with LASSO on unmet need for FP in South Sulawesi. The AIC value of the GWLR with LASSO model, which is 31,918, is less than the AIC value of the GWLR without LASSO, which is 38,879. This implies that GWLR with LASSO method is able to model unmet need for FP better than GWLR model. In addition, it was obtained that the status of unmet need for FP in 22 districts/cities was affected by the percentage of women with junior high school education or equivalent or lower, number of high-fertility women, percentage of husbands/families who refuse family planning, and number of KB staffs, while there were 2 districts/cities where the status of unmet need for KB was determined by the number of high-fertility women, percentage of husbands/families who refuse family planning, and number of FP staffs.
Application of Autoregressive Integrated Moving Average (ARIMA) for Forecasting Inflation Rate in Indonesia Jova Edri Saputra; Werry Febrianti
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.36609

Abstract

Inflation is one of the indicators to maintain economic stability. Controlling inflation reflects the success of economic growth, while very high or volatile inflation can lead to economic instability. The purpose of this research is to forecast the time series data of inflation rate in Indonesia until the end of 2024 using ARIMA method. The data used in this study are secondary data of monthly inflation rates in Indonesia from January 2003 to May 2024 obtained from the Bank Indonesia website. Based on the research results, the optimal model for forecasting the inflation rate in Indonesia until the end of 2024 is ARIMA (1,0,1) with a MAPE of 6.91%. The forecasting results show a stable and not too significant increase and are still within the target range set by Bank Indonesia and the Government, which is between 1,5% and 3,5% for 2024.  

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