Jurnal Matematika, Statistika dan Komputasi https://journal.unhas.ac.id/index.php/jmsk <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> Department of Mathematics, Hasanuddin University en-US Jurnal Matematika, Statistika dan Komputasi 1858-1382 <p><a href="http://creativecommons.org/licenses/by/4.0/" rel="license"><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="Creative Commons License" /></a><br /><span>This work is licensed under a </span><a href="http://creativecommons.org/licenses/by/4.0/" rel="license">Creative Commons Attribution 4.0 International License</a><span>.</span></p><p><strong>Jurnal Matematika, Statistika dan Komputasi</strong> is an Open Access journal, all articles are distributed under the terms of the <strong><a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</a></strong>, allowing third parties to copy and redistribute the material in any medium or format, transform, and build upon the material, provided the original work is properly cited and states its license.<strong> </strong> This license allows authors and readers to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference.</p> Classification of Water Level for Flood Disaster Status Detection Using Artificial Intelligence https://journal.unhas.ac.id/index.php/jmsk/article/view/32095 <p>Flooding occurs when the water surface elevation exceeds the normal level, resulting in river water overflowing and creating inundation in low-lying areas. The importance of early warning for potential floods is significant in reducing losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The results of the study indicate that the SVM algorithm outperforms the KNN algorithm. In the scenarios conducted, with the SVM algorithm using the parameter C ranging from 1 to 10 and the rbf kernel, it achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.</p> Jiwa Akbar Copyright (c) 20 1 Prediction of Clean Water Supply Using the Fuzzy Time Series Cheng Method at PDAM Tirta Silau Piasa https://journal.unhas.ac.id/index.php/jmsk/article/view/32071 <p>This research aims to determine predictions of clean water supply at PDAM Tirta Silau Piasa in 2023 using the Fuzzy Time Series Cheng method. This type of research is quantitative research using data sources, namely secondary data. This research data was taken from clean water supply data at PDAM Tirta Silau Piasa, namely data on the volume of clean water for the period January 2021 to May 2023. From the calculation results of the prediction analysis of clean water supply at PDAM Tirta Silau Piasa using the Fuzzy Time Series Cheng method, for the amount of water supply clean water in June 2023 is 443,620, with a total predicted clean water supply from 2021 to June 2023 of 12,031,703. With a MAPE value of 3%, if we look at the MAPE which is less than 10%, the results of predicting clean water supply using the Fuzzy Time Series Cheng method produce the best prediction value.</p> <p>&nbsp;</p> Deva Rezky Ramadhani Copyright (c) 20 1 Eligibility Classification Of Aid Recipients Hope Family Program In Cinta Rakyat Village Using The Method Weighted Naive Bayes With Laplace Smoothing https://journal.unhas.ac.id/index.php/jmsk/article/view/32069 <p>The Indonesian government sometimes faces difficulties in dealing with poverty problems. The Indonesian government utilizes a number of programs and stimulants to overcome the problem of poverty. The government's PKH program offers conditional assistance to low-income families who have been designated as PKH recipient households. PKH provision is still below optimal standards, this may be because the data used is not updated frequently. To assist village officials in determining which residents are eligible to receive PKH assistance, this research tries to classify the eligibility of recipient residents in Cinta Rakyat Village. With the Weighted Naive Bayes method, classification calculations are not only based on probability distributions but also by adding weights to each attribute to the class. Assisted with Laplace Smoothing to avoid a probability value of 0. As a result, there are eight factors that determine a person's eligibility to receive PKH assistance, including age, occupation, income, number of family members, number of dependent school children, quality of house, type of floor, and type of walls. As well as classification into eligible and non-eligible groups. And obtained test results using the Confusion Matrix with an accuracy value of 95.65%, error rate of 4.34%, sensitivity of 100% and specificity of 94.74%. To identify village communities who deserve PKH assistance, Cinta Rakyat Village administrators can use the findings of this research.</p> <p>&nbsp;</p> Nurjannah Copyright (c) 20 1 Analysis of Online Transportation Marketing Strategies Using Game Theory https://journal.unhas.ac.id/index.php/jmsk/article/view/32064 <p>Advances in information and communication technology that are growing rapidly also bring influence in the field of transportation. It can be seen with the emergence of online transportation applications that help people so that it can be easier to order fast and efficient transportation in reaching various places. Along with the many online transportation applications that appear, this makes competition between online transportation increase. For this reason, online transportation companies need the right and best strategy to reach consumer profits and interests. The solution that can be used for this problem is to use the Game Theory method. Game Theory method is an approach or technique used to analyze the situation of interaction between two or more people or entities that have conflicting interests in choosing the action to be performed. The results of the research obtained show competition between Gojek and Grab, Gojek's optimal strategy is Security and Promo while Grab is Service and Promo. In the competition of Gojek and InDrive, Gojek's optimal strategy is promo, while InDrive is price. In the Gojek and Maxim competition, the same optimal strategy is Promo. On the competition of Grab and InDrive, Grab's optimal strategy is Security, while InDrive is price. In the competition of Grab and Maxim, Grab's optimal strategy is Promo while Maxim is price. In InDrive and Maxim competition, InDrive's optimal strategy is Price, while Maxim is Promo.</p> Indah Hatika Lubis Copyright (c) 20 1 The Cox Proportional Hazard Model on Life Insurance Premium Payments Analysis https://journal.unhas.ac.id/index.php/jmsk/article/view/32046 <p><em>Insurance premiums are a sum of money that must be paid by participants of life insurance programs to insurance companies to compensate for losses suffered by participants. The amount of the premium must be in accordance with the sum insured to be received, so that the insurance company has enough money to replace the losses suffered by its customers. In determining the premiums also should not be too large, because it can burden the insurance program customers. Therefore it is necessary to do an analysis to find out the factors (gender, age, amount of coverage, occupation, method of payment of premiums, amount of premium, and type of insurance product) that affect the term of payment ability by the customer. The analysis conducted is using the Cox Proportional Hazard Model. The results obtained in this study are factors that have a significant effect on the period of ability to pay premiums, namely the amount of </em><em>sum insured</em><em>, </em><em>profession</em><em> and types of insurance products.</em></p> Nor Amalliyah Copyright (c) 20 1 Analisis Cluster Agglomeratif Nesting dalam Pemetaan Sarana Kesehatan Kabupaten/Kota di Provinsi Jawa Barat https://journal.unhas.ac.id/index.php/jmsk/article/view/32043 <p>The use of Hierarchical Clustering is used to group districts or cities in West Java according to the number of health facilities, distance to health facilities and population density using Agglomerative Nesting (AGNES). Clustering in this study utilizes complete linkage clustering. The elbow method produces two optimal clusters which are then validated with the sillhoute coefficient and Calinski-Harabasz. In this study, there are 27 variables in the form of health facilities spread across 27 regencies/cities in West Java in 2021. The results of the cluster analysis formed in this study are 18 districts/cities in cluster&nbsp; one and 9 districts/cities in cluster two.</p> Nadira Nisa Alwani Copyright (c) 20 1 Faktor-Faktor yang Mempengaruhi Tingkat Pendapatan Individu di Desa Cikanyere Menggunakan Model Regresi Logistik Ordinal https://journal.unhas.ac.id/index.php/jmsk/article/view/31964 <p>Income is an element in the economic development process that functions as an indicator of the standard of living for individuals, families, or communities. Cikanyere Village is located in Cianjur Regency, West Java, which still experiences low economic growth. Economic growth can be observed through the income levels of the community. Given this issue, the objective of this research is to identify the variables that influence individual income levels in Cikanyere Village. One type of regression analysis, namely ordinal logistic regression, is utilized to examine the relationship between independent and dependent variables that are polytomous, meaning they have two or more categories and are in ordinal scale. Ordinal logistic regression is chosen because the dependent variable, income level, and the independent variables, namely education level, age, marital status, number of dependents, and gender, are in ordinal scale. In this study, the influence of each component on income level is measured partially using the Pearson Chi-Square test. The results indicate that age, education level, and the number of dependents are components that affect the income level of Cikanyere Village. Gender and marital status do not influence income levels. The obtained ordinal logistic regression model shows the probability values of individual income improvement with changes in age, education level, and the number of dependents.</p> Hagni Wijayanti Copyright (c) 20 1 Penerapan Matriks Leslie pada Angka Kelahiran dan Harapan Hidup Wanita di Provinsi Papua Barat https://journal.unhas.ac.id/index.php/jmsk/article/view/31830 <p>The purpose of this study is to determine the number of female population in West Papua Province with birth rate and life expectancy using eigenvalues and eigenvectors, and to determine the limiting age distribution using the Leslie matrix model. The eigenvector is used to determine the number of female population from each age interval, while the eigenvalue is used to determine the population growth rate. The research method used is to collect research data and analyze the data, then draw conclusions.&nbsp; The data used was obtained from BPS West Papua Province, namely, Population Projections by age group in 2010-2020. The results of this study are the Leslie matrix model for the female population in West Papua Province is a discrete model divided into fourteen age interval classes constructed using fertility rates and life expectancy. The conclusion obtained is that the total female population in West Papua Province tends to increase with a positive eigenvalue greater than one or the female growth rate of West Papua Province tends to be positive.</p> Junianto Sesa Copyright (c) 20 1 KAJIAN GRUP FUZZY DALAM GRUP Z_p-{0 ̅ } https://journal.unhas.ac.id/index.php/jmsk/article/view/31827 <p><em>Group theory is a field of abstract algebra that studies the structure of sets. A non-empty set </em><em>, with a binary operation (</em><em>) that satisfies certain axioms, namely being closed, associative, containing identity and containing the inverse of each element is called a group. According to Yasir et al. (2016), a subgroup is a non-empty subset of a group G and is a group with the same operations as group G. Some concepts that are developments of group theory are fuzzy subgroups. Suppose that G is a group, a fuzzy subset μ of G is called a fuzzy subgroup of G if it satisfies </em><em>&nbsp;and </em><em>&nbsp;for each </em><em>. However, not all groups have fuzzy subgroups. The aim of writing this thesis is to show that </em><em>&nbsp;is a classical group with multiplication operations in the group and determine fuzzy subgroups in the group </em><em>From the research results, it is found that the subset </em><em>&nbsp;</em><em>with prime modulo integers </em><em>&nbsp;and </em><em>is a classical group with group multiplication operations and the fuzzy subset in the group </em><em>is a fuzzy subgroup and in general The properties of the classical group apply to the fuzzy subgroup, namely the singularity of the identity and the singularity of the inverse. However, there is a property in the properties of classical groups that does not generally apply to fuzzy subgroups, namely the law of elimination. The law of cancellation does not apply in general because there are integers modulo prime p that apply, namely at </em><em>.</em></p> Junianto Sesa Copyright (c) 20 1 Optimization of Regularization Methods in Addressing Multicollinearity Issues in Infant Mortality Data https://journal.unhas.ac.id/index.php/jmsk/article/view/31632 <p>The infant mortality rate is a crucial indicator for assessing the health and infant care quality in a region. In an effort to reduce infant mortality rates, regression analysis serves as a tool to identify influential factors. However, regression analysis often encounters the challenge of multicollinearity, which involves high correlation among predictor variables. To address this issue, various regularization techniques can be applied, such as ridge regression, least absolute shrinkage and selection operator (Lasso), and elastic net. Ridge regression aims to control coefficient variance, while Lasso directs some coefficients to zero, functioning as variable selection. Elastic net combines the strengths of both methods by merging L1 and L2 regularization. The objective of this research is to evaluate the performance of ridge regression, elastic net, and lasso methods in handling multicollinearity issues, utilizing infant mortality rate data in South Sulawesi Province. The results indicate that the elastic net method outperforms both the Ridge and Lasso methods. The best-performing model is obtained through the elastic net with a coefficient of determination value of 34.40%, whereas ridge and lasso methods yield coefficient of determination values of 29.95% and 24.52%, respectively. This demonstrates that the application of the elastic net method is capable of producing more accurate results in modeling the variables within the analysis of infant mortality rate data compared to other methods.</p> Arief Rahman Nur Andi Kresna Jaya SISWANTO Copyright (c) 20 1