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<![CDATA[Estimasi Return Level pada Pemodelan Spatial Extreme Value Kecepatan Arus Laut Bali dengan Pendekatan Max-Stable Process Model Smith dan Brown-Resnick ]]>
<![CDATA[Nyoman Gede Trisna Sanjaya]]>;
<![CDATA[Pratnya Paramitha Oktaviana]]>;
<![CDATA[Galuh Oktavia Siswono]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27436
<![CDATA[Bali is the world's second most popular tourist destination in 2023. One of the best tourisms is the beauty of its coasts. Even though it is the best tourism destination, it is not uncommon for disasters to occur in the coastal areas of Bali. One important factor in the occurrence of coastal disasters from waters such as tidal flooding and abrasion is ocean currents. Spatial analysis of sea currents velocity was carried out using the Smith and Brown-Resnick Max-Stable Process Approach. The purpose of this study was to determine parameter estimation and comparison of the results of Spatial Extreme Value modeling with the Smith and Brown-Resnick Max-Stable Process approach, and to determine the Return Level of Bali Sea current velocity for the same period after data testing with the best model. The data used is daily data for the period March 2, 2017 to December 30, 2020. Extreme data selection with Block Maxima uses 14 daily blocks, so there are 100 blocks for each water location. The proportion of training and testing data is 80:20. The training data follows the Generalized Extreme Value distribution and has no pattern trend (stationary). The results of the extremal coefficient measurements ranged from 1.18604 to 1.59485 indicating a fairly strong dependency between locations. The best trend surface model is a model that only has longitude coordinates on the location parameter and latitude on the scale parameter. The estimated value of the spatial parameters of the Smith model tends to be greater than that of the Brown-Resnick model. The Root Mean Square Error and Mean Absolute Percentage Error for the Smith model are 0.15503 and 7.75076%. Meanwhile, the Brown-Resnick model is 0.29576 and 14.12131%. Return Level values for the same period after data testing are classified as strong currents and are respectively 1.20586 m/s, 1.63592 m/s, 1.51322 m/s and 2.13233 m/s for Serangan, Gianyar, Nusa Dua, and Nusa Lembongan Waters. Information on estimated Return Levels is expected to be a consideration that can be used by related agencies such as the Coastal and Marine Resources Management Agency (BPSPL) and the Bali Province Regional Disaster Management Agency (BPBD) as a coastal disaster mitigation effort to make it more effective, efficient and on target. ]]>
<![CDATA[Analisis Risiko Kebakaran Hutan Dan Lahan Daerah Kalimantan Barat Menggunakan Metode Regresi Logistik Dengan Pendekatan Generalized Extreme Value ]]>
<![CDATA[Radit Candra Nugroho]]>;
<![CDATA[Pratnya Paramitha Oktaviana]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27474
<![CDATA[Forests in Indonesia have been reduced by half due to fires. Forest and land fires often occur during the long dry season in places such as the island of Borneo. West Kalimantan is an area passed by the equator which is directly above the Pontianak area. The main effect is to make West Kalimantan a tropical area with high air temperatures so that forest and land fires often occur. This study aims to obtain the results of the probability of land and forest fires in each district in West Kalimantan. The method used is binary logistic regression analysis with response variables in the form of data categories based on spatial data and analysis of extreme values with Generalized Extreme Value (GEV). Spatial analysis uses the help of ARCGIS software in processing raster data (grid cells). The data used is data on maximum temperature and maximum wind speed taken from October 7, 2021 to October 31, 2022 from the official NASA website. The spatial data used in this study is forest and land fire vulnerability data taken from the BNPB website in the form of raster data. The results of logistic regression analysis found that the maximum temperature variable has a negative relationship with the response variable, while the maximum speed of wind variable has a positive relationship with the response variable. The temporal probability of the resulting GEV is getting higher with a longer period of years ahead. The probability of forest and land fires is obtained by multiplying the log probability by the GEV temporal probability. In this study, it was found that the highest chance of forest and land fires occurring in Sanggau Regency was suspected to occur due to an increase in temperature every year. ]]>
<![CDATA[Estimasi Parameter Model Regresi Data Panel Menggunakan Metode Least Square Dummy Variable ]]>
<![CDATA[NUR AMINAH AHMAD]]>;
<![CDATA[Raupong Raupong]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27530
<![CDATA[Panel data regression is a set of techniques for modeling the effect of independent variable on the dependent variable of panel data. The parameter estimation in the panel data regression model used the least squares method, but the difference between the intercept and the slope could not be known between time and between cross-section. One of the methods used is the Least Square Dummy Variable method (LSDV). The LSDV method is a method that has the same stages as the least squares method, but uses dummy variable to get different intercept score. This research uses the LSDV method to explain the differences in intercept between cross-sections using balanced panel data, namely the Human Development Index (HDI) data in South Sulawesi 2011-2017 to get fixed effect panel data regression model parameters on that data and the regencies with Average Length of School (ALS) and Life Expectancy (LE) variable that has the most influence on HDI based on the coefficient of determination criteria. According to the results of this research, the score of the coefficient of determination in the panel data regression model using the fixed effect model in each cross-section (regency), there are also three regencies with the highest coefficient of determination, respectively, Gowa, Pare-pare and Bantaeng regency that ALS and LE are able to explain the HDI variables 98.942%, 98.089% and 97.444%.]]>
<![CDATA[Perbandingan Estimasi Cadangan Klaim dengan Metode Classical Chain Ladder dan Bornhuetter-Double Chain Ladder ]]>
<![CDATA[Krisdiantha Krisdiantha]]>;
<![CDATA[M. Jibril Khalifatullah]]>;
<![CDATA[Tione Daffaxa Dumamika]]>;
<![CDATA[Lienda Noviyanti]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27673
<![CDATA[In the world of insurance, insurance companies need to back up claims to ensure that the company can cover expenses resulting from filing claims from policyholders. Claim reserves represent the estimated value of claim payments in the future, where there are differences in the estimated and actual value of claim payments. Errors in predicting claim reserves will result in inaccuracies and disrupt the insurance company's financial stability. There are several ways to estimate claim reserves, one of the most common methods is using a Chain Ladder. However, the Chain Ladder method is very susceptible to outliers, so another method is needed to estimate claims reserves that are more accurate. This study discusses the comparison between the Chain Ladder method and one of the development methods, namely Bornhuetter-Double Chain Ladder in estimating claim reserves. The Bornhuetter-Double Chain Ladder method uses data on claims that have occurred as a whole, the amount of claims that have been paid, and the number of claims that have occurred. Based on the research results, it can be concluded that the Bornhuetter-Double Chain Ladder method is capable of producing more stable and accurate claim reserves compared to the Chain Ladder method. ]]>
<![CDATA[Klasifikasi Financial Distress Menggunakan Feedforward Neural Network Berdasarkan Rasio Keuangan Altman dan Ohlson ]]>
<![CDATA[Annisa Salsabila Pratiwi]]>;
<![CDATA[Galuh Oktavia Siswono]]>;
<![CDATA[Prilyandari Dina Saputri]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27742
<![CDATA[The ever-changing economy requires companies to anticipate future conditions in order to avoid financial distress, a continuous decline in financial conditions. The research focused on comparing Altman and Ohlson’s financial ratio in classifying financial distress on Property and Real Estate companies using the Feedforward Neural Network. The data used is the financial report data of 19 Property and Real Estate companies listed on the Indonesian Stock Exchange in 2016-2022, with the initial status of financial conditions based on earnings per share. (EPS). The study also used the Synthetic Minority Oversampling Technique (SMOTE) method to address class imbalances. The best financial ratio is selected based on accuracy values and Area Under Curve (AUC). Altman’s financial ratio with the FFNN model architecture (5-2-1) with a balance of 60:40 yields an accuracy of 84.62% and an AUC of 0.8325. The Ohlson Financial ratio with the 60:40 data balancing process and the FFNN model architecture (9-4-1) yields an accuracy of 93.27% and an AUC of 0.9045. Thus, in predicting financial distress in companies in the Property and Real Estate sector, Ohlson’s financial ratio with the predictor variables Corporate Size (SIZE), Total Liabilities to Total Assets (TLTA), Working Capital to Total Acts (WCTA), Current Liability to Current Asset (CLCA), OENEG, Net Income to total assets (NITA), Cash Flows Operating to Total Responsibilities (CFOTL), Net Revenue (INTWO), and Net Incoming Change (CHIN) yielded the best results. This best ratio can be used as a consideration in using alternative financial ratio to classify financial distress.]]>
<![CDATA[Analisis Kestabilan Model SIR-SI untuk Transmisi Penyakit Demam Berdarah Dengue ]]>
<![CDATA[Joko Harianto]]>;
<![CDATA[Katarina Lodia Tuturop]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27746
<![CDATA[The SIR-SI mathematical model for the problem of dengue virus spread which has been discussed in previous studies has not involved the saturated birth rate of mosquito. This discussion aims to construct and analyze the SIR-SI model which involves competition factors in mosquito population growth so that the model used to predict the number of dengue virus infections becomes more realistic. In addition, sensitivity analysis and numerical simulations of the models that have been constructed are also discussed. The method used is a literature study using theories derived from reputable articles. The results of this discussion show that the existence of an equilibrium point and its stability depends on the basic reproduction number. If the basic reproduction number is less than one, the number of cases of dengue fever infection will decrease. However, if the basic reproduction number is more than one, the number of cases of dengue infection will not decrease and even tend to be constant at a certain number. The average parameter of bites carried out by one mosquito in all humans () is the most dominant in increasing the spread of dengue disease in humans. On the other hand, mosquitoes' natural death rate parameter () is the most dominant in reducing the spread of dengue fever in humans. This information provides input and evaluation to decision-makers in solving the problem of the spread of dengue fever.]]>
<![CDATA[Stability Analysis and Numerical Simulation of the COVID-19 SISiR Model : Bahasa Indonesia ]]>
<![CDATA[Arival Rince Putri]]>;
<![CDATA[Berliani Nasran]]>;
<![CDATA[Budi Rudianto]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27747
<![CDATA[This research discusses the SISiR model (Susceptible Infected Sick Recovered) considering individual immune parameters and lockdown parameters. The consideration of these parameters aims to determine whether immunity and lockdown have an impact on the spread of COVID-19. The model's stability is analyzed around the equilibrium point to understand the dynamics of COVID-19 spread in a population. Furthermore, the parameter R0 is determined to indicate whether COVID-19 disappears or remains in the population. From numerical simulations with spesific parameter values, it is concluded that COVID-19 continues to spread in the population with an R0 = of 4.4486. The addition and reduction of immune and lockdown parameters affect the spread of COVID-19.]]>
<![CDATA[Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping ]]>
<![CDATA[Mella Anugrahayu]]>; Ulil Azmi
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27755
<![CDATA[The tendency of investors to choose investments with maximum return and minimal risk causes the need for diversification in a portfolio to form an optimal portfolio. A lot of research on stock portfolio optimization has been conducted extensively, but not many have tried to apply machine learning concepts such as clustering analysis to accelerate the establishment of a model that can have a positive effect on the time and cost efficiency of portfolio management. However, clustering is only limited to determining the optimal stock candidate, so it is necessary to add another optimization model to calculate the portfolio weight. Based on these problems, this study carried out portfolio optimization using Mean-Variance (MV) and Mean Absolute Deviation (MAD) model based on K-Medoids Clustering by Dynamic Time Warping approach using Monte Carlo-Expected Tail Loss for risk analysis. Based on the analysis results, the MAD portfolio is more optimal than the MV portfolio by the MAD portfolio consists of five stocks, namely BMRI shares with a weight of 0.06243, UNTR shares of 0.08658, BBRI shares of 0.10285, BBCA of 0.53623, and KLBF shares of 0.21191 are the best optimal portfolios. The optimal portfolio of the MAD model has a rate of return of 87.836% in May 2017 - December 2022 with a portfolio performance of 0.03704, while the resulting risk level based on Carlo-Expected Tail Loss is 2.2416%. ]]>
<![CDATA[Pemodelan Persentase Penduduk Miskin di Pulau Jawa dengan Pendekatan Geographically Weighted Regression (GWR) ]]>
<![CDATA[Muhammad Rafi Ikhsanudin]]>;
<![CDATA[Ernawati Pasaribu]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.27804
<![CDATA[Poverty is a multidimensional problem faced by all countries in the world. Poverty is the inability of individual or group to meet their basic needs in terms of expenditure. In poverty problem, there is a tendency that the poor will group in locations with certain characteristics. This spatial clustering indicates spatial diversity that making global regression analysis inappropriate for application. Therefore, the purpose of this research is to model the percentage of poor population in 119 districts on Java Island in 2021 using the Geographically Weighted Regression (GWR) method. The analysis results state that the GWR model with Kernel Fixed Bisquare provides superior results compared to the global regression model and able to overcome spatial heterogeneity problem. The model is able to provide a fairly high coefficient of determination, which is 70,73 percent. The GWR model identifies ten groups of districts based on the significance of the independent variables, with the majority of them (61 districts) having a significant RLS variable. This indicates that education is an important aspect that needs to be considered by local governments to alleviate poverty.]]>
<![CDATA[Kajian Regularized Generalized Structured Component Analysis untuk Mengatasi Multikolinearitas pada SEM Berbasis Komponen ]]>
<![CDATA[Fitri Amanah]]>;
<![CDATA[Fitri Rahmawati]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/j.v20i1.28069
<![CDATA[Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of and indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.]]>
