BAREKENG: Jurnal Ilmu Matematika dan Terapan
BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure Mathematics (analysis, algebra & number theory), - Applied Mathematics (Fuzzy, Artificial Neural Network, Mathematics Modeling & Simulation, Control & Optimization, Ethno-mathematics, etc.), - Statistics, - Actuarial Science, - Logic, - Geometry & Topology, - Numerical Analysis, - Mathematic Computation and - Mathematics Education. The meaning word of "BAREKENG" is one of the words from Moluccas language which means "Counting" or "Calculating". Counting is one of the main and fundamental activities in the field of Mathematics. Therefore we tried to promote the word "Barekeng" as the name of our scientific journal also to promote the culture of the Maluku Area. BAREKENG: Jurnal ilmu Matematika dan Terapan is published four (4) times a year in March, June, September and December, since 2020 and each issue consists of 15 articles. The first published since 2007 in printed version (p-ISSN: 1978-7227) and then in 2018 BAREKENG journal has published in online version (e-ISSN: 2615-3017) on website: (https://ojs3.unpatti.ac.id/index.php/barekeng/). This journal system is currently using OJS3.1.1.4 from PKP. BAREKENG: Jurnal ilmu Matematika dan Terapan has been nationally accredited at Level 3 (SINTA 3) since December 2018, based on the Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia, with Decree No. : 34 / E / KPT / 2018. In 2019, BAREKENG: Jurnal ilmu Matematika dan Terapan has been re-accredited by Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia and accredited in level 3 (SINTA 3), with Decree No.: 29 / E / KPT / 2019. BAREKENG: Jurnal ilmu Matematika dan Terapan was published by: Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura Website: http://matematika.fmipa.unpatti.ac.id
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<![CDATA[PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST ]]>
<![CDATA[Taufiq Akbar]]>;
<![CDATA[Georgina Maria Tinungki]]>;
<![CDATA[Siswanto Siswanto]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1605-1616
<![CDATA[Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the grouping results, so methods that are robust to outliers are used, such as K-Medoids and Density-Based Spatial Clustering of Applications with Noise. The purpose of this study is to compare the results and performance of the two methods using the silhouette coefficient test on data on human development indicators in South Sulawesi Province in 2021. The results of the analysis show that K-Medoids produced 2 groups, namely the districts/cities group which has indicators of human development that consist of 21 districts/cities, and the high group, which consists of 3 districts/cities, while Density-Based Spatial Clustering of Application with Noise produces 1 group that has the same characteristics, which consists of 19 districts/cities, and the remaining 5 districts/cities are identified as noise. Based on the silhouette coefficient test, K-Medoids have a greater value than Density-Based Spatial Clustering of Application with Noise, namely 0,635 and 0,544, respectively, so that K-Medoids have better performance.]]>
<![CDATA[CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS ]]>
<![CDATA[Ermawati Ermawati]]>;
<![CDATA[Idhia Sriliana]]>;
<![CDATA[Riry Sriningsih]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1617-1630
<![CDATA[Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.]]>
<![CDATA[SUGAR DEMAND FORECASTING IN PT XYZ WITH WINQSB SOFTWARE ]]>
<![CDATA[Astrid Wahyu Adventri Wibowo]]>;
<![CDATA[Fitri Maimunah]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1631-1640
<![CDATA[PT XYZ is a manufacturing company engaged in the production of sugar and its by-products. Currently, the determination of the amount of production at PT XYZ has not been adjusted to meet customer demand, which may continue to decrease or increase for each period. If there is a condition that the amount of production is greater than demand, it will increase the cost of storage due to accumulation. Meanwhile, if the amount of production is smaller than demand, there will be an out-of-stock condition that can reduce consumer confidence. These problems can be solved by forecasting Tambora Sugar demand at PT XYZ to meet consumer demand using the forecasting method (forecasting) with the help of WinQsb software with input, namely sugar demand data from 2021 PT SMS. The request data will later be analyzed from the request using a scatter diagram. Furthermore, after the pattern is known, the appropriate forecasting method will be determined and inputted into the WinQsb software. Based on the calculation results, it is known that the demand pattern from last year tends to trend down so the chosen method is the Double Exponential Smoothing (DES), Single Exponential Smoothing (SES), and Linear Regression (LR) method, with the best method being Linear Regression which produces the smallest error. The output is in the form of a Master Production Schedule (MPS), namely in the 13th to 18th periods, respectively 2142; 1757; 1373; 989; 604; 220 sacks.]]>
<![CDATA[SEIR MODEL SIMULATION WITH PART OF INFECTED MOSQUITO EGGS ]]>
<![CDATA[James Uriel Livingstone Mangobi]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1641-1652
<![CDATA[Dengue hemorrhagic fever (DHF) is an acute febrile disease caused by the dengue virus, which is transmitted by various species of Aedes mosquitoes. The SEIR model is a mathematical model for studying the spread of dengue fever. In this model, it is assumed that some mosquito eggs have been infected because infected mosquitoes can transmit the virus to their eggs. The main vector of this disease is the Aedes albopictus mosquito. Analysis was carried out to assess the stability of the equilibrium point, and numerical simulations were carried out to see changes in population numbers due to changes in parameter values. A disease-free equilibrium (DFE) point, which is stable given the basic reproductive number . An endemic point whose stability is guaranteed if the value . The numerical simulations show that an increasing mosquito mortality rate decreases the number of exposed, susceptible humans. Furthermore, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible humans. For the mosquito population, increasing mosquitoes’ mortality rate will decrease the number of exposed, susceptible mosquitoes. Finally, an increase in the average bite of an infected mosquito will increase the number of exposed, susceptible mosquitoes.]]>
<![CDATA[MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA ]]>
<![CDATA[Eni Meylisah]]>;
<![CDATA[Dyah Setyo Rini]]>;
<![CDATA[Herlin Fransiska]]>;
<![CDATA[Winalia Agwil]]>;
<![CDATA[Bagus Sartono]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1653-1662
<![CDATA[When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users , the number of small and micro industries and the number of tourist villages n cluster 2, the amount of village tours . In cluster 3, the percentage of users of electricity and the percentage of villages that have mining and quarrying .]]>
<![CDATA[NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA ]]>
<![CDATA[Ani Budi Astuti]]>;
<![CDATA[Abdi Negara Guci]]>;
<![CDATA[Viky Iqbal Azizul Alim]]>;
<![CDATA[Laila Nur Azizah]]>;
<![CDATA[Meirida Karisma Putri]]>;
<![CDATA[Wigbertus Ngabu]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1663-1672
<![CDATA[The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.]]>
<![CDATA[DYNAMICS OF A SIRV MODEL FOR THE SPREAD OF COVID-19 IN MALUKU PROVINCE ]]>
<![CDATA[Nona Tjie Sapulette]]>;
<![CDATA[Yopi Andry Lesnussa]]>;
<![CDATA[Monalissa E Rijoly]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1673-1684
<![CDATA[COVID-19 (Coronavirus Disease 2019) is caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) coronavirus spreading around the world. In this study, the SIRV model was used, which is an epidemic model carried out by grouping the population into four subpopulations, namely the subpopulation of susceptible individuals who can be infected (Susceptible), the subpopulation of infected individuals (Infected), the subpopulation of individuals who recover from illness (Recovered), and the subpopulation of individuals who have been vaccinated (Vaccination). Based on the dynamic system analysis conducted, two equilibrium points were obtained, namely the disease-free equilibrium point and the endemic equilibrium point. In addition, based on data processing and model simulation results obtained, was obtained so that it can be concluded that the higher the number of vaccinated populations, the lower the level of Covid-19 spread, which means that vaccines can suppress cases of Covid-19 spread in Maluku Province]]>
<![CDATA[A TWO-STEP CLUSTER FOR CLASSIFYING PROVINCES IN INDONESIA BASED ON ENVIRONMENTAL QUALITY ]]>
<![CDATA[Umi Mahmudah]]>;
<![CDATA[Muhamad Safiih Lola]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1685-1694
<![CDATA[The main objective of this study was to conduct a cluster analysis of the environmental health index in Indonesia for all the provinces. Clustering the environmental health index was important to reveal regional disparities, target and intervention policies, monitor progress over time, and allocate resources more effectively for improved environmental health outcomes. In this study, a sample of 34 units was utilized, encompassing all provinces in Indonesia. The environmental health index was clustered based on five indicators, namely Water Quality Index, Air Quality Index, Soil Quality Index, Marine Quality Index, and Land Cover Quality Index. This research used the two-stage clustering method, which was unique in combining both hierarchical and non-hierarchical clustering methods to produce a more accurate and reliable solution. Four clusters were determined to group provinces in Indonesia based on the environmental health index. The analysis found that the quality of clustering was in the fair but close to good category. The clustering results showed that 32% of the provinces were in cluster 4 and 26.5% of the provinces were in cluster 1. Then, 23.5% and 17.6% of the provinces were in clusters 2 and 3, respectively. In addition, two indicators were found to be the most predictive of the overall clustering solution, namely the Soil Quality Index and the Land Cover Quality Index. The results also implied that provinces in cluster 3 had the lowest environmental quality so they must improve it by looking at provinces in cluster 4, which was the group with the best environmental quality index.]]>
<![CDATA[ON THE GIRTH, INDEPENDENCE NUMBER, AND WIENER INDEX OF COPRIME GRAPH OF DIHEDRAL GROUP ]]>
<![CDATA[Agista Surya Bawana]]>;
<![CDATA[Aluysius Sutjijana]]>;
<![CDATA[Yeni Susanti]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1695-1702
<![CDATA[The coprime graph of a finite group , denoted by , is a graph with vertex set such that two distinct vertices and are adjacent if and only if their orders are coprime, i.e., where |x| is the order of x. In this paper, we complete the form of the coprime graph of a dihedral group that was given by previous research and it has been proved that if , for some and if . Moreover, we prove that if is even, then the independence number of is , where is the greatest odd divisor of and if is odd, then the independence number of is . Furthermore, the Wiener index of coprime graph of dihedral group has been stated here.]]>
<![CDATA[DEVELOPMENT OF EXPECTED MONETARY VALUE USING BINOMIAL STATE PRICE IN DETERMINING STOCK INVESTMENT DECISIONS ]]>
<![CDATA[Giovanny Theotista]]>;
<![CDATA[Margareta Febe]]>;
<![CDATA[Yvone Marshelly]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol17iss3pp1703-1712
<![CDATA[Stock investment is an investment opportunity. This stock investment carries relatively high risk and therefore requires additional analysis to minimize losses and maximize profits. Expected Monetary Value (EMV) is a simple modeling method for estimating the value of an investment that will provide the greatest future return. The expected monetary value (EMV) method involves multiplying the total value of each scenario by the probability of that scenario occurring. However this method has weaknesses in terms of how many cases occur what is the value of each case and what is the probability of each case occurring. Binomial State Price is a method commonly used to calculate stock options and real options but includes the step of modeling the value of an investment in many situations and opportunities that arise in the future. In this paper, our objective is to develop the EMV method with the binomial state pricing model to determine the investment that offers the most favorable payoff. In short, we can develop the expected monetary value (EMV) method and the binomial state pricing model. It was found that this model always recommends stocks which have high dividens.]]>
