Jurnal Matematika, Statistika dan Komputasi 2024-05-15T02:20:40+00:00 Budi Nurwahyu Open Journal Systems <table style="border-collapse: collapse; width: 693px;"> <tbody> <tr> <td style="width: 40%;"><img src="" 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="" target="_blank" rel="noopener">2614-8811</a>, p-ISSN:<a href="" 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=";mod=viewjournal&amp;journal=2164" target="_blank" rel="noopener"><img src="" alt="" width="94" height="76" /></a></td> <td style="width: 9.98438px;"> </td> <td style="width: 159.656px;"><a title="DOI Crossreff" href="" target="_blank" rel="noopener"><strong><img src="" 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="" target="_blank" rel="noopener"><img src="" 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=";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="" 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="" target="_blank" rel="noopener"><img src="" 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=";hl=en" target="_blank" rel="noopener"><img src="" alt="" width="147" height="72" /></a></strong></td> <td style="width: 10.0312px;"><strong> </strong></td> </tr> </tbody> </table> APPLICATION OF SUPPORT VECTOR MACHINE METHOD BY COMPARING SEVERAL KERNEL FUNCTIONS TO RAINFALL CLASSIFICATION IN BONE BOLANGO REGENCY 2024-05-15T02:20:40+00:00 Sri Mujirah Adam Ismail Djakaria Nisky Imansyah Yahya La Ode Nashar <p style="font-weight: 400;">In general, it is common to find data classifications with target attributes that have two classes (binary classification). But as it develops in the real world, the existing data does not only consist of two classes but develops into problems that have more than two classes (multiclass classification). One method that can handle multi-class problems is the support vector machine (SVM). SVM solves the multi-class classification problem using one approach, namely OneVsRest (OVR), which solves the multi-class problem by building a number of k classes into a binary SVM model. The next classification uses daily climate data obtained from the NASA POWER website with target attributes, namely rainfall that has a cloudy, light rain, or moderate rain category (multiclass classification). The purpose of this study is to classify rainfall data using SVM with linear, RBF, polynomial, and sigmoid kernel functions and then compare them based on accuracy values to find out the best kernel function for classifying data. The results showed that the accuracy of the kernel function in classifying successive data, namely the linear kernel function, was 75.36%, the RBF kernel function was 93.44%, the polynomial kernel function was 80.34%, and the sigmoid kernel function was 66.75%. In order to obtain the best kernel function in classifying data, namely the RBF kernel function, which has the greatest accuracy value among the other three kernel functions.</p> Copyright (c) Comparison of Fuzzy Time Series Lee, Chen, and Singh on Forecasting Foreign Tourist Arrivals to Indonesia in 2023 2024-05-14T02:48:33+00:00 Ade Setyani Nurmara Sari <p>Tourism in Indonesia is one of the most reliable sectors for national development, it is because tourism can increase foreign exchange and can increase national and regional income. The purpose of this research is to compare the Lee, Chen, and Singh fuzzy time series methods in forecasting foreign tourist visits to Indonesia. The data used in this study are monthly data on the number of foreign tourist visits to Indonesia from July 2014 to December 2023. The methods used for forecasting are Lee's fuzzy time series method, Chen's fuzzy time series, and Singh's fuzzy time series. The results of this study obtained MAPE values for in-sample data of foreign tourist visits to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 9.81%, 10.35%, and 2.77%, respectively. The MAPE values for out-sample data of foreign tourist arrivals to Indonesia using the Lee, Chen, and Singh fuzzy time series methods are 12.99%, 13.35%, 0.80%, respectively. From the MAPE value of in-sample data and out-sample data, it can be concluded that Singh's fuzzy time series has the smallest error value, so Singh's fuzzy time series is better and more accurate in forecasting foreign tourist visits to Indonesia.</p> Copyright (c) Modeling the Risk of Developing Heart Disease Using a Multivariate Approach Adaptive Regression Spline (MARS) and Structural Equation Modeling (SEM) 2024-05-13T06:02:49+00:00 Abyan Daffa Akbar <p>Heart disease is a disease that affects the structure of the heart and how it works. One of the common heart diseases suffered by people is coronary heart disease. Coronary heart disease is a disease when the arteries cannot distribute blood that carries oxygen to the heart. Most heart diseases can be prevented by surmount the risk factors that take effect and detecting heart disease as early as possible so that treatment and medication can begin. In this article, we will discuss modeling heart disease using the Multivariate Adaptive Regression Spline (MARS) and Structural Equation Modeling (SEM) approaches and analyzing the factors that surmount heart disease. The data used in this research is heart disease data obtained from Dian Husada General Hospital, totaling 385 MARS data and 193 SEM data. Based on the research results, in MARS modeling the surmounting factors are gender, cholesterol, diabetes, smoking status, body mass index, and alcohol consumption with MSE values of , GCV , and &nbsp;. In SEM modeling, the surmounting factors are body mass index, blood pressure, cholesterol, chest pain, stress, smoking habits, and sleep time.</p> Copyright (c) A District/City Profiling Based on Poverty Indicators in East Nusa Tenggara Using the Centroid Linkage Algorithm 2024-05-11T03:42:27+00:00 Andrea Tri Dani <p>Poverty is a complex multidimensional phenomenon that significantly impacts human life. Poverty has always been a problem that the government has discussed regionally, centrally, and internationally. The issue of poverty is interesting to approach and analyze using a statistical approach, namely cluster analysis. Cluster analysis is used to group objects based on their level of similarity. In this research, the algorithm used is the Centroid Linkage Algorithm. The Centroid Linkage algorithm was chosen based on its advantages in the grouping process. Distance similarity measurement uses Squared Euclidean. The data used are district/city poverty indicators in East Nusa Tenggara Province. The analysis results show that two optimal clusters were obtained with their distinguishing characteristics. Hopefully, the results of this analysis can be used as a reference in formulating policies for alleviating poverty.</p> Copyright (c) Bahasa Indonesia Optimal Control Strategy to Increase Live Offspring in the Bali Starling (Leucopsar Rothschildi) Breeding System in the West Bali National Park (WBNP), Indonesia 2024-05-07T01:12:15+00:00 Gandhiadi G K <p>In this research, the breeding system model was developed only for the breeding model in the Animal Sanctuary Unit (ASU) in the Tegal Bunder WBNP area. Starling eggs in captivity that hatch (offspring) have not been included in the sub-population. After they can be weaned and given bracelets, they will be included in the group of sub-population. Offspring that have not yet entered the breeding subpopulation often cause food shortages in the breeding pen. This is what is trying to be overcome by providing additional feed control input to the chicks in the breeding cage. The dynamic model that is built will be analyzed for system stability around the critical point using the linearization method and the Routh-Hurwitz stability criterion. Then an optimal control design was carried out using the Pontryagin Principle by providing control, namely additional feed to the unweaned offspring. The model is studied precisely and numerically, and numerical simulations are carried out to illustrate the analytical findings. Analysis revealed that providing additional control feed to the unweaned offspring succeeded in minimizing total feed costs and increasing the number of subpopulations in the captive system. The dynamic model and optimal control design studied are expected to support the sustainability of the Bali Starling breeding system in the ASU of Tegal Bunder. Apart from that, it is hoped that this study can also optimize the carrying capacity and benefits in maintaining the sustainability of the Bali Starling population in WBNP</p> Copyright (c) Clustering Provinces in Indonesia Based on Educational Facilities and Infrastructure Aspects Using K-Means and Fuzzy C Means 2024-04-23T05:51:45+00:00 Sinta Septi Pangastuti <p>Education is one of the sectors that has an important role in the development of a country. Indonesia itself is a country that is very concerned about the implementation of education. According to the results of a survey on the world's education system in 2018 issued by PISA (Program for International Student Assessment) in 2019, Indonesia occupies a low position, namely 74th out of 79 participating countries. In order to improve the state of education in Indonesia as an effort to achieve the Sustainable Development Goals (SDGs) "Quality Education", it is necessary to review the state of education in each province. This study aims to identify groups of provinces that have similar characteristics in terms of educational facilities and infrastructure using the clustering analysis method. The methods of analysis that are used in this study are K-Means Clustering and Fuzzy C Means Clustering. Based on the results, it can be concluded that the K-means Clustering method is more suitable to be used in this analysis. K-means Clustering forms 4 groups that explain the different characteristics of basic education facilities and infrastructure from each province in Indonesia.</p> Copyright (c) Factors Affecting The Number Of Domestic Flights In Indonesia During Covid-19 Pandemic Using SARIMAX Method 2024-04-22T07:02:35+00:00 Abdullah Ahmad Dzikrullah <p>Indonesia, which consists of thousands of large and small islands, relies heavily on-air transportation to support mobility between regions. As many as 80% of Indonesia's total air transportation passengers are domestic flight passengers. This shows how vital domestic flights are in Indonesia's air transportation system. However, in 2020, the COVID-19 pandemic had an impact that resulted in a decrease in the number of domestic flights in Indonesia. Therefore, an analysis is needed to determine the factors that affect the number of domestic flights in Indonesia. This study uses the SARIMAX method with several exogenous variables, including the number of operating civil aviation airports, positive daily cases of COVID-19, calendar effects during Eid al-Fitr and New Year's Day, and social restriction policies. The results showed that the number of operating airports one week before Eid al-Fitr, one week during Eid al-Fitr, one week before New Year, and Emergency PPKM significantly influenced the number of domestic flights. The best SARIMAX model obtained is SARIMAX(1,1,1)(4,1,1)<sup>7</sup> with a MAPE value of 5.35% and a coefficient of determination of 97%.</p> Copyright (c) Development of Simple Statistical Software: Linear Regression Series 2024-04-21T12:24:20+00:00 Hari Purnomo Susanto Nely Indra Meifiani Mega Isvanidiana Purnamasari Mobinta Kusuma Ika Noviantari sumin sumin Tika Dedy Prasetyo <p><em>Regression analysis is a statistical analysis method that is often used in various fields. SPSS is a software that is often used in Indonesia for regression analysis. anxiety will arise in users who do not have a strong foundation in regression and rarely use SPSS. Anxiety will be exacerbated by how to interpret the analysis results. The aim of this study is to develop simple statistical software for regression analysis that is easy to use with few settings and is able to provide interpretation of analysis results. The development model used is System Development Life Cycle (SCLD). There are five stages in this model including, Planning, Analysis, Design, Implementation, and System. The software developed is named Simple Statistical Software series Regression Analysis or abbreviated as 3S-AR. The development results obtained were the 3S-AR application which has functionality for regression analysis. Validation of development results was carried out by comparing the results of 3S-AR analysis with SPSS software. 3S-AR has several advantages. First, it's easy to use with little setup. With just one analysis you can display the results of the regression analysis along with all the assumptions. Second, being able to provide an interpretation of the analysis results. Third, if there is an analysis that is not fulfilled, it is able to provide suggestions regarding what the user should do. Fourth, it's free. The development results can contribute to users in carrying out regression analysis easily. Especially for users who do not have a strong statistical foundation and experience in using software for regression analysis.</em></p> Copyright (c) Indonesia's Image Analysis: International Media and Tourism Recovery 2024-04-20T11:39:31+00:00 Sita Rutba <p>The international world's perception of a country is essential. In 2022, Indonesia attracted <br>international media attention, which can form the nation’s image of Indonesia in the <br>international public perception. Therefore, this study analyses international media, online <br>news, and social media Twitter perceptions towards Indonesia. For the news, aspect-based <br>sentiment analysis is carried out, and for Twitter opinion, sentiment classification and the <br>grouping of topics related to these sentiments are carried out. This research can classify <br>news sentiment based on aspects of forming the country's image, such as tourism, exports, <br>diplomacy, government policies, and people's behavior. The model can classify each <br>aspect into positive, negative, neutral, and none sentiment. On Twitter opinion, there are <br>groups of topics related to positive and negative sentiments about Indonesia. Then, it was <br>found that Twitter's positive sentiment about Indonesia is associated with tourism <br>recovery. The results are summarised in a visualization dashboard.</p> Copyright (c) On Rainbow Antimagic Coloring of Corona Product of Cycle Graph (C_m) and Star Graph (S_n) 2024-04-18T13:31:56+00:00 sri agista kaya Nisky Imansyah Yahya Djihad Wungguli <p>Let G&nbsp; be a simple and finite graph. Rainbow coloring c defined&nbsp; c : G ➡ {1,2,...,k} where k&nbsp; is the minimum color used in a graph&nbsp; G. Rainbow connection number (rc)&nbsp; are defined as determining patterns by giving different colors to all edges which are elements of graph G and vertex u - v with u,v ∈ V(G)&nbsp; there is a rainbow path. The rainbow antimagic coloring is a bijective function f : V ➡ (1,2,...,|V(G)| , for every two distinct vertex there is at least one rainbow path. This article discusses about the rainbow connection number (rc)&nbsp; and the rainbow antimagic connection number (rac) on a new graph which is the result of corona operation on a cycle graph and a star graph&nbsp; The results obtained in determining the rainbow connection numbers from the graph&nbsp; C<sub>n</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> are rc(C<sub>n</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub>) = [3n/2]&nbsp; with n ≥ 3, and the rainbow antimagic connection numbers from the graph C<sub>m</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> are n + 3 ≤ rac(C<sub>m</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> ) ≤ n + 5&nbsp; with m = 3 and n ≥ 3.</p> Copyright (c)