https://journal.unhas.ac.id/index.php/jmsk/issue/feed Jurnal Matematika, Statistika dan Komputasi 2024-02-21T23:26:45+00:00 Budi Nurwahyu budinurwahyu@unhas.ac.id Open Journal Systems <table style="border-collapse: collapse; width: 693px;"> <tbody> <tr> <td style="width: 40%;"><img src="https://journal.unhas.ac.id/public/site/images/budin258/jurnal.jpg" alt="" width="770" height="956" /></td> <td style="width: 2%;"> </td> <td style="width: 58%;"> <p style="text-align: justify;"><strong>e-ISSN: <a href="https://issn.brin.go.id/terbit/detail/1517041991" target="_blank" rel="noopener">2614-8811</a>, p-ISSN:<a href="https://issn.brin.go.id/terbit/detail/1180427019" target="_blank" rel="noopener">1858-1382</a></strong></p> <p style="text-align: justify;"><strong><span style="font-weight: normal;">Welcome to Jurnal Matematika, Statistika dan Komputasi (Supported by The Indonesian Mathematician Society -IndoMS). Jurnal Matematika, Statistika dan Komputasi is published on</span></strong> <strong><span style="font-weight: normal;">January, May and September by Department of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia.<br /></span></strong><strong><span style="font-weight: normal;">JMSK welcomes original papers in Indonesia Language (Bahasa) or in English for scope:</span></strong> <strong><span style="font-weight: normal;">Mathematics for the development of mathematical sciences, statistics, computation, or mathematics Education. </span></strong></p> </td> </tr> </tbody> </table> <p style="text-align: justify;"><strong>ACCREDITED BY SINTA 3</strong></p> <table style="border-collapse: collapse; width: 550px;"> <tbody> <tr> <td style="width: 95.9219px;"><strong>INDEXED BY:</strong></td> <td style="width: 146.688px;"><a href="http://id.portalgaruda.org/index.php?ref=browse&amp;mod=viewjournal&amp;journal=2164" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_IPI.png" alt="" width="94" height="76" /></a></td> <td style="width: 9.98438px;"> </td> <td style="width: 159.656px;"><a title="DOI Crossreff" href="http://dx.doi.org/10.20956" target="_blank" rel="noopener"><strong><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_copernicus2.jpg" alt="" width="185" height="45" /></strong></a></td> <td style="width: 10.9688px;"><strong> </strong></td> <td style="width: 115.75px;"><strong><a title="DOI Crossreff" href="http://dx.doi.org/10.20956" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/Logo_Crossref1.PNG" alt="" width="65" height="48" /></a></strong></td> <td style="width: 10.0312px;"><strong> </strong></td> </tr> <tr> <td style="width: 95.9219px;"><strong> </strong></td> <td style="width: 146.688px;"><strong><a title="INDEX IOS" href="http://onesearch.id/Search/Results?lookfor=jmsk&amp;type=AllFields&amp;filter%5B%5D=institution%3A%22Universitas+Hasanuddin%22&amp;filter%5B%5D=collection%3A%22JURNAL+MATEMATIKA+STATISTIKA+DAN+KOMPUTASI%22" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_IOS4.jpg" alt="" width="192" height="63" /></a></strong></td> <td style="width: 9.98438px;"> </td> <td style="width: 159.656px;"><strong><a title="INDEX ROAD" href="http://road.issn.org/issn/2614-8811#.WrRkeH--mpp" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_Road3.jpg" alt="" width="206" height="68" /></a></strong></td> <td style="width: 10.9688px;"><strong> </strong></td> <td style="width: 115.75px;"><strong><a title="INDEX GOOGLE SCHOLAR" href="https://scholar.google.co.id/citations?user=s2e2GIgAAAAJ&amp;hl=en" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/lOGO_GOOGLE_SCHOLAR.jpg" alt="" width="147" height="72" /></a></strong></td> <td style="width: 10.0312px;"><strong> </strong></td> </tr> </tbody> </table> https://journal.unhas.ac.id/index.php/jmsk/article/view/33137 Autokorelasi Spasial Prevalensi Bayi Berat Badan Lahir Rendah di Provinsi Jawa Tengah dan Jawa Timur Tahun 2022 2024-02-18T06:11:12+00:00 Dwi Ristiani Hariastuti - haristi101@gmail.com <p>Latar Belakang : Berat badan lahir rendah (BBLR) menjadi masalah kesehatan masyarakat yang signifikan secara global. Prevalensi BBLR di Indonesia, pada tahun 2022 sebesar 3,3%, dimana prevalensi BBLR Provinsi Jawa Tengah dan Jawa Timur melebihi rata-rata yaitu sebesar 5,1 % (Jawa Tengah) dan 4,1% (Jawa Timur).</p> <p>Tujuan : Penelitian ini bertujuan untuk mengetahui keterkaitan antar wilayah di Provinsi Jawa Tengah dan Jawa Timur berdasarkan prevalensi BBLR Tahun 2022</p> <p>Metode : Penelitian ini menggunakan pendekatan analisis spasial dengan jenis penelitian observasional, dimana unit analisis adalah kabupaten dan kota Provinsi Jawa Tengah dan Jawa Timur dengan menggunakan data sekunder.</p> <p>Hasil : Penelitian menunjukkan bahwa secara global terdapat autokorelasi spasial positif dengan pola sebaran spasial mengelompok berdasarkan prevalensi BBLR (I = 0.224) dan secara lokal terdapat autokorelasi antar wilayah kabupaten/kota di Jawa Tengah dan Jawa Timur berdasarkan prevalensi BBLR (E(I) = - 0.0139)</p> <p>Kesimpulan : Program intervensi untuk menurunkan prevalensi BBLR menyasar di wilayah <em>hotspot</em> yaitu Kabupaten Banjarnegara, Kebumen, Purbalingga dan Wonosobo (Jawa Tengah) dan Kabupaten Probolinggo (Jawa Timur). Namun juga perlu melakukan intervensi pada wilayah yang berada disekitarnya (tetangganya).</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33125 Clustering of Flood Area Points in Central Java Province Using Spatio Temporal - Density Based Spatial Clustering Application with Noise (ST-DBSCAN) Algorithm 2024-02-16T04:14:38+00:00 Indah Manfaati Nur indahmnur@unimus.ac.id <p>Flooding is a problem that until now needs special handling from various parties. In the last 10 years, flooding has consistently been the most common disaster event. One of them is in Central Java Province, which experienced 203 flooding areas and made it the province most frequently affected by flooding in Indonesia in 2022. Flood cases have regional characteristics and occur repeatedly with different times so that there are spatial aspects and temporal aspects. Thus, there is a need for a method to group the points of the flood area. One algorithm that can group these two aspects is Spatio Temporal - Density Based Spatial Clustering Applications with Noise (ST-DBSCAN). This study aims to obtain the results of clustering flood area points in Central Java. Based on the analysis results, the best parameters are obtained from the validation of the temporal silhouette cluster 0.48291 and temporal DBI 0.4742315. The data forms 3 large clusters with a track pattern.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33121 Comparison of Generalized Poisson and Quasi-Likelihood Methods in Overdispersion Problem in Poisson Regression 2024-02-15T12:37:05+00:00 Cintia Septemberini cintiaseptemberini@apps.ipb.ac.id <p>Regression analysis is a statistical procedure used to estimate the relationship between various variables and observe the relationship pattern between one dependent variable and with at least one independent variable. When the dependent variable is discrete, one regression analysis that can be used is the Poisson regression method. In Poisson regression analysis, one of the assumptions that must be met is equidispersion. That is, the mean and variance of the dependent variable have the same value. However, in practice in the field, there is often a violation of this assumption called overdispersion, the value of the data variance exceeds the mean value. This study overcomes the overdispersion problem by applying the Generalized Poisson Regression (GPR) method, a method that considers the data distribution (parametric) and Quasi-Likelihood, used when the data distribution is unclear (non-parametric). These two methods are applied to data on the number of maternal death cases in Indonesia in 2021. This study aims to determine the best method based on the smallest AIC value to analyze the factors that affect the number of maternal death cases in Indonesia. The results showed that the GPR method was superior to Poisson and Quasi-Likelihood regression. This can be seen from the deviance value in the GPR method, which indicates that the overdispersion problem has been resolved. Factors that are significant in influencing the number of maternal death cases include the percentage of health services for pregnant women K4, the number of mothers who experience bleeding, and the number of health centers.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33084 Penerapan Model ARIMAX dalam Melihat Pengaruh Indeks Harga Saham Global dalam Meramalkan IHSG Indonesia 2024-02-09T16:09:11+00:00 Marzuki Marzuki marzuki@usk.ac.id Muhammad Rizwanda mriswanda58@gmail.com Nurhasanah Nurhasanah nurhasanah@usk.ac.id Asep Rusyana asep.rusyana@usk.ac.id <p>Time series analysis can be classified into two parts when viewed based on the analysis data variables, namely univariate and multivariate time series analysis. The ARIMAX model is the development of the ARIMA model. The ARIMAX model is a multivariate time series analysis method consisting of exogenous and endogenous variables. This study aims to forecast the Composite Stock Price Index (IHSG) using the ARIMAX model, by looking at the influence of global stock price indices, namely the American stock price index (DJIA), Japanese stock price index (N225), and Chinese stock price index (SSEC). The results of the study show that the model used to forecast the 2019 JCI is the ARIMAX model (4,1,4). The results of the 2019 JCI forecast produce fluctuating data. The highest JCI share price of 6,958.419 occurred in November, while the lowest share price of 6.591.566 occurred in January. Forecast accuracy measured using RMSE and MAPE obtained results of 144.5387 and 2.5121 respectively.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33083 Penerapan Model ARIMAX dalam Melihat Pengaruh Indeks Harga Saham Global dalam Meramalkan IHSG Indonesia 2024-02-09T15:38:36+00:00 Marzuki Marzuki marzuki@usk.ac.id <p>Time series analysis can be classified into two parts when viewed based on the analysis data variables, namely univariate and multivariate time series analysis. The ARIMAX model is the development of the ARIMA model. The ARIMAX model is a multivariate time series analysis method consisting of exogenous and endogenous variables. This study aims to forecast the Composite Stock Price Index (IHSG) using the ARIMAX model, by looking at the influence of global stock price indices, namely the American stock price index (DJIA), Japanese stock price index (N225), and Chinese stock price index (SSEC). The results of the study show that the model used to forecast the 2019 JCI is the ARIMAX model (4,1,4). The results of the 2019 JCI forecast produce fluctuating data. The highest JCI share price of 6,958.419 occurred in November, while the lowest share price of 6.591.566 occurred in January. Forecast accuracy measured using RMSE and MAPE obtained results of 144.5387 and 2.5121 respectively.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33076 The Use of Individual Level Premium Method with Vasicek Stochastic Interest Rate in the Calculation of Civil Servant Pension Funds 2024-02-08T22:03:52+00:00 Rahmat Hermawan hermawanr20h@student.unhas.ac.id Nurhaliza Rais nurhalizaraiss@gmail.com <p>A pension fund is a sum of money given to someone who has retired, either because of age or because they are no longer able to work. Companies that organize pension funds need to conduct actuarial valuations to estimate the amount of funds needed for the obligation to pay participants' retirement benefits until the participant dies. One of the important pension funds to be valued is the pension fund for civil servants. Generally, pension funding calculations still use constant interest rates, while in reality interest rates in Indonesia tend to change. Therefore, stochastic interest rates are needed to estimate interest rates by considering fluctuations in interest rates. One of the effective stochastic interest rate models is the Vasicek model. This study aims to apply the Vasicek stochastic interest rate model in the calculation of Normal Cost and Actuarial Liability using the Individual Level Premium method for Civil Servant pension funding. Based on the calculation results, the Vasicek model parameter value is obtained in approximating the BI rate using the Ordinary least Square method, namely , , . The simulation results of the vasicek interest rate using the Milstein numerical scheme get a MAPE of 18.17%. The results of the calculation of pension funding for civil servants with vasicek stochastic interest rates using the Individual Level Premium method concluded that Normal Cost and Actuarial Liability increased every year.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33070 A Application of Weibull Regression to Recurrence Data Kidney Infection Patients 2024-02-21T23:26:45+00:00 Andrea Tri Dani andrikadoko@gmail.com <p>The kidneys are the main organ for removing metabolic waste products that are not needed by the body. Kidney infection or pyelonephritis is an infection in the bladder tract that attacks the kidneys and enters through the lower or external urinary tract, spreads to the bladder, to the ureters (upper urinary tract), then finally to the kidneys. This disease is often not detected so it can cause complications of kidney failure. This research discusses survival analysis in kidney infection patients using Weibull survival regression by carrying out descriptive statistical analysis, estimating Weibull regression model parameters, testing hypothesis of regression model parameters simultaneously and partially. The aim of this study was to determine the Weibull survival regression model and determine the factors that influence the length of time for kidney infection patients to relapse. The results of this research obtained the best model which is , from which it can be seen that frailty, gender, and the patient's medical history are factors that significantly influence the recovery rate of kidney infection patients.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33045 Analysis on dengue fever in Bandar Lampung using spatial autoregressive 2024-02-06T13:59:27+00:00 Nurhayatin Nissa Nissa Nurhayatinnisa45@gmail.com <p><em>This research explores the impact of climate change in Indonesia, focusing on the rising cases of Dengue Hemorrhagic Fever (DHF), population mobility, and density in the city of Bandar Lampung on the spread of DHF. Utilizing the Spatial Autoregressive (SAR) model, the study aims to identify spatial patterns and factors influencing the disease's spread in the region. However, the findings indicate a lack of significant spatial dependence patterns, attributed to limited geographical coverage and the dominance of variables over DHF cases. Regression analysis using SAR reveals the non-significance of the Spatial Autoregressive parameter (Rho), suggesting a weak influence of neighboring locations. Consequently, the Spatial Autoregressive model in the DHF analysis for Bandar Lampung is considered non-significant, providing insights into the complex dynamics of DHF spread and questioning key determinants in that geographical context.</em></p> <p><strong>Keywords:</strong> <em>spatial autoregressive; dengue hemorrhagic fever</em></p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33042 modelling, binary logistic regre Binary Logistic Regression Modeling in Hypertension Cases at Kebun Jahe Health Center 2024-02-06T10:59:06+00:00 Laras Fabyanti fabyanti15@gmail.com <p>This study aims to analyze the factors that can influence the occurrence of hypertension cases in patients at the Kebun Jahe Health Center and present a binary logistic regression model on factors that significantly affect the occurrence of hypertension cases at the Kebun Jahe Health Center. This study is a quantitative study using primary data obtained from interviews with patients at the Kebun Jahe Health Center. The data analysis used is descriptive and then uses binary logistic regression analysis. Binary logistic regression is used to help identify risk factors that contribute to the incidence of hypertension. From the analysis of factors that cause hypertension, it was found that the variables of age, sex, physical activity, and smoking had a significant effect on the occurrence of hypertension cases in patients at the Kebun Jahe Health Center.</p> Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/33034 Demographic Employment Status Modeling Analysis in Blitar City Using C4.5 Algorithm and Ordinal Logistic Regression 2024-02-05T06:03:22+00:00 Tianta Limara tiantalimara04@gmail.com <p>Indonesia is a developing country that still experiences socioeconomic problems in the form of unemployment. This unemployment phenomenon actually increased after the Covid-19 pandemic that occurred in Indonesia because many workers were laid off due to the country's slowing economic growth. The government is trying to deal with the unemployment problem due to layoffs by establishing a Jaminan Kehilangan Pekerjaan (JKP) program managed by BPJS Ketenagakerjaan as an Unemployment Insurance product in Indonesia. The development and evaluation of this insurance product requires an actuarial assessment that includes demographic modeling as a review in describing regional employment risks to ensure fair treatment for all individuals. Demographic data in an area that includes the composition of the workforce can be the basis for demographic modeling, especially in the context of the Unemployment Insurance program. Almost every city or regency in Indonesia, including in Blitar City, is still experiencing unemployment problems so it is necessary to analyze the classification of the working status of residents in Blitar City which is able to provide an overview of employment conditions in the Blitar City area. The study conducted a work status classification analysis with a machine learning model on population data in Blitar City which was grouped into three categories, namely full employed, under employed, and unemployed. This analysis will use machine learning models with the Decision Tree Algorithm C4.5 method and Ordinal Logistic Regression to determine the best employment demographic classification model through a comparison of the accuracy level of the model formed. In predicting the category of work status, the model formed from the Decision Tree method of the C4.5 algorithm produces an accuracy value of 78.18%. On the other hand, the model formed from the Ordinal Logistic Regression method produced an accuracy value of 79.49%. Based on these results, the Ordinal Logistic Regression method is a method that produces a better model than the C4.5 Decision Tree Algorithm in predicting the classification of work status categories in Blitar City.</p> Copyright (c)