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BAREKENG: Jurnal Ilmu Matematika dan Terapan
Published by Universitas Pattimura
ISSN : 19787227     EISSN : 26153017     DOI : https://search.crossref.org/?q=barekeng
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation
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
Arjuna Subject : Matematika - Matematika Terapan
Articles 60 Documents
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Search results for , issue "Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications" : 60 Documents clear
ANALYSIS OF COMMUNITY COMPLIANCE WITH THE COVID-19 HEALTH PROTOCOLS: A QUANTITATIVE ANALYTICAL APPROACH I Komang Gde Sukarsa; GK Gandhiadi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1193-1202

Abstract

This study identifies the factors used in factor analysis that influence the compliance of the people of Bali Province to the COVID-19 health protocol. Based on the factor analysis results, the elimination of four variables from the initial 20 variables in the study because the low communality value was below 0.5. Thus, reducing the remaining 16 variables to six factors with a total variance of 74.8%. Six factors influenced Community compliance in Bali Province, namely someone who knows about the importance of health protocols (19,1%), trust in protocols (15,9%), someone infected with COVID-19 (12,2%), a sense of concern for themselves and their families (10,1%), government supervision and sanctions (9,3%), and the affordable price of masks (8,2%). Observing this study, the first factor that played a role in increasing adherence was knowledge about the importance of health care. There was a sense of trust in health protocols, and a less influential factor was the affordability of masks
POVERTY PANEL DATA MODELING IN SOUTH SUMATERA Nurul Hidayati; Herlin Fransiska; Winalia Agwil
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1203-1214

Abstract

Poverty is still a major problem on Sumatra's island, despite abundant natural resources potential, such as mining and plantation products. Sumatra Island consists of 10 provinces divided into three regions: Northern Sumatra, Central Sumatra, and Southern Sumatra. The island of Sumatra has the highest number of poor people in the Southern Sumatra region, which reaches 2.88 million people. Poverty is an integrated concept with five dimensions: poverty, powerlessness, vulnerability to emergencies, dependency, and alienation, both geographically and sociologically. One method that can be used to analyze poverty data problems is panel data regression analysis, which combines two data, namely cross-sectional data and time series data. It is expected to produce more in-depth and comprehensive information, both the interrelationships between the variables and their development within a certain period. The panel data was related to poverty and included 60 districts in the southern Sumatra region from 2018 to 2020. This study aimed to model poverty panel data in Southern Sumatra. Three estimation methods were used in the panel data regression, including the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM. The results of the model specification test show that the best model for estimating the percentage of poor people in the Southern Sumatra region is the Fixed Effect Model (FEM), with a value of R2 = 75.57%. The results of the significance test show that the variables that significantly influence the percentage of poverty in the Southern Sumatra region using the FEM model are the open unemployment rate (), life expectancy (), and the average length of schooling ().
OPTIMIZATION OF RICE INVENTORY USING FUZZY INVENTORY MODEL AND LAGRANGE INTERPOLATION METHOD Eka Susanti; Fitri Maya Puspita; Evi Yuliza; Siti Suzlin Supadi; Oki Dwipurwani; Novi Rustiana Dewi; Ahmad Farhan Ramadhan; Ahmad Rindarto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1215-1220

Abstract

Interpolation is a method to determine the value that is between two values and is known from the data. In some cases, the data obtained is incomplete due to limitations in data collection. Interpolation techniques can be used to obtain approximate data. In this study, the Lagrange interpolation method of degree 2 and degree 3 is used to interpolate the data on rice demand. A trapezoidal fuzzy number expresses the demand data obtained from the interpolation. The other parameters are obtained from company data related to rice supplies and are expressed as trapezoidal fuzzy numbers. The interpolation accuracy rate is calculated using Mean Error Percentage (MAPE). The second-degree interpolation method produces a MAPE value of 30.76 percent, while the third-degree interpolation has a MAPE of 32.92 percent. The quantity of order respectively 202677 kg, 384610 kg, 1012357 kg, 1447963 kg, and a Total inventory cost of Rp. 129231797951.
MODELLING EARTHQUAKE DISASTER DAMAGE DUE DEPTH OF EPICENTER AND MAGNITUDE USING SPATIAL REGRESSION Dhea Laksmita Arsya Primananda; Muhammad Muhajir
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1221-1234

Abstract

East Java Province is geographically close to the Eurasian and Indo-Australian Plate subduction zones, resulting in frequent earthquakes East Java Province has a high population density, so it is very risky if disaster occurs. One preventive solution to reduce this impact is estimating damage when an earthquake occurs. The purpose of this study was to determine the best modeling of damages due to earthquakes in East Java Province, using the amount of house damage as a response variable, while depth of the epicenter and the strength of the earthquake as predictor variables. It is suspected that there is a spatial dependency effect in this case, so the solution is to use regression with an area approach, namely the Spatial Durbin Model (SDM). The amount of house damage is collected from BNPB, the epicenter and the magnitude of earthquake collected from BMKG in 2021. The result shows that SDM is good at explaining the dependency relationship between response and predictor variables. The significant predictor variables are the depth of epicenter and the strength of the earthquake. It is meaning that the magnitude and the depth of the epicenter of the earthquake in an area have an impact on other adjacent areas. There is a relationship between the amount of house damage in one area and other adjacent areas. The Regency will have a high number of damaged houses if it is adjacent to a Regency that has a high number of damaged houses
ANALYSIS OF SOCIO-ECONOMIC IMPACTS OF THE COVID-19 PANDEMIC USING FACTOR ANALYSIS Made Susilawati; I Wayan Sumarjaya; IGAM Srinadi; DPE Nilakusmawati; NLP Suciptawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1235-1244

Abstract

The purpose of this study is to identify the factors that influence the socio-economic impact of the Covid-19 pandemic. This study uses explanatory factor analysis that is an analysis that forms new random factors in which the later formed factors or constructs can be interpreted. The case study was conducted in Sawan Village, Sawan District, Buleleng Bali, with six variables explaining the economic impact, and 16 variables explaining the social impact. The results of the study show that there are three factors that explain the economic impact due to Covid-19. They are the income factor, the purchase of quotas and gadgets, and the expenditure factor with the total variance described being 82,178 percent. Meanwhile, the social impact due to the Covid-19 pandemic is explained by three factors, namely the fear of interacting in public places, the fear factor of doing activities outside the home, and the fear of using public facilities with a total variance that can be explained is 73,609 percent.
FUZZY TIME SERIES BASED ON THE HYBRID OF FCM WITH CMBO OPTIMIZATION TECHNIQUE FOR HIGH WATER PREDICTION Nursyiva Irsalinda; Dera Kurnia Laely; Sugiyarto Surono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1245-1256

Abstract

Time series data represents measurements taken over a specific period and is often employed for forecasting purposes. The typical approach in forecasting involves the analysis of relationships among estimated variables.In this study, we apply Fuzzy Time Series (FTS) to water level data collected every 10 minutes at the Irish Achill Island Observation Station. The FTS, which is based on Fuzzy C-Means (FCM), is hybridized with the Cat and Mouse Based Optimizer (CMBO). This hybridization of FCM with the CMBO optimizer aims to address weaknesses inherent in FTS, particularly concerning the determination of interval lengths, with the ultimate goal of enhancing prediction accuracy.Before conducting forecasts, we execute the FCM-CMBO process to determine the optimal centroid used for defining interval lengths within the FTS framework. Our study utilizes a dataset comprising 52,562 data points, obtained from the official Kaggle website. Subsequently, we assess forecasting accuracy using the Mean Absolute Percent Error (MAPE), where a smaller percentage indicates superior performance. Our proposed methodology effectively mitigates the limitations associated with interval length determination and significantly improves forecasting accuracy. Specifically, the MAPE percentage for FTS-FCM before optimization is 20.180%, while that of FCM-CMBO is notably lower at 18.265%. These results highlight the superior performance of the FCM-CMBO hybrid approach, which achieves a forecasting accuracy of 81.735% when compared to actual data.
CLUSTERIZATION OF REGION IN SOUTH SUMATERA BASED ON COVID-19 CASE DATA Anita Saragih; Dian Cahyawati Sukanda; Ning Eliyati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1257-1264

Abstract

Based on Covid-19 case data as of July 2022, South Sumatra Province has the 15th highest rank out of 34 provinces in Indonesia, with confirmed cases totalling 82,407. This showed that the spread of Covid-19 in South Sumatra was still high. This study aimed to determine the cluster of regions in South Sumatra based on Covid-19 case data. Clustering regions used agglomerative hierarchical method. The process began with standardizing the data, calculating the similarity distance between objects, determining the optimal number of clusters using the Silhouette method, and the last was clustering analysis. This study found that the optimal number of clusters consisted of two clusters. The clustering process starts with objects 2 and objects 4 because these two objects have the closest similarity distance. In conclusion, objects with the closest similarity distance (in one cluster) have the same data movement (fluctuation).
MODEL OF TRANSMISSION COVID-19 USING SIQRD MODEL WITH THE EFFECT OF VACCINATION IN MATARAM Annisa Zaen Febryantika; Marwan Marwan; Lailia Awalushaumi; Bulqis Nebulla Syechah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1265-1276

Abstract

Mathematical modeling is considered an effective tool for analyzing real-life problems. In this research, we analyze the dynamics of the COVID-19 spread in Mataram city using the SIQRD model with influence of the vaccination. The analyze based on varying some parameter values of the model i.e the transmission rate (β), the recovery rate for COVID-19 (γ), and the death rate (δ), before and after vaccination respectively. Our chosen methodology involves parameter estimation using the Euler method. The result shows that the model has an endemic equilibrium point which remains stable before and after vaccination. Furthermore, the basic reproduction number (R0) which states the number of secondary cases that occur if there are infected people in a population, has the value more than 1 before the vaccination, but equal to 1 after the vaccination. This suggests that prior to COVID-19 vaccination, infected individuals could potentially infect more than one person, but after vaccination, each infected person tends to only infect one other individual. This shift is attributed to the subsidence of COVID-19 symptoms following vaccination
JCI MODELING IN INDONESIA BASED ON INDUSTRIAL PRODUCTION INDEX WITH LOCAL POLYNOMIAL ESTIMATOR APPROACH Rizky Ismaul Uyun Hidayat; Juan Krisfigo Prasetyo; Berliani Larasati; Mutiara Aisharezka; Nur Chamidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1277-1286

Abstract

The industrial sector is the leading sector that contributes the most to Indonesia's economic growth. Industry can be caused by various factors, one of which is the Jakarta Composite Index (JCI). Indonesian stock prices have a high variance that requires proper modeling. Therefore, this study uses a local polynomial nonparametric regression approach. This study aims to estimate and obtain the best JCI model based on the production index of large and medium industries using a local polynomial estimator and also knowing the accuracy of the JCI model based on the production index of large and medium industries. The data used in this study is secondary data using production index data for medium-large industries and data on the composite stock index in Indonesia in the form of Time series which were obtained through the Central Statistics Agency Publication website on the page www.bps.go.id. JCI modeling in Indonesia based on the production index of large and medium industries is most effective on local polynomials with polynomial degree two which obtains an optimal bandwidth of 7,8795 with a minimum Cross-Validation (CV) value of 163170,3 and a Mean Absolute Percentage Error (MAPE) value of 9,1%. From the MAPE value it is said that the model is good for making future predictions.
CATEGORICAL ANALYSIS TO PERCEPTIONS OF GOVERNMENT POLICY IN ELECTRICITY FUEL MANAGEMENT AS ALTERNATIVE TO SUBSTITUTE OIL FUEL USING CHI-SQUARE TEST Nur Chamidah; Naufal Ramadhan Al Akhwal Siregar; Muhammad Fikry Al Farizi; Bagas Shata Pratama; Atikah Faiza; Muhammad Hilmi Fibryan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1287-1300

Abstract

The scarcity and increase in world oil prices is a tough dilemma that must be responded to by the Indonesian government. In order to prevent fuel consumption from swelling, the government plans to reduce fuel subsidies. The plan certainly has many positive impacts, including savings on government finances so that they can be diverted to fund other programs that are more effective and on target. These savings are also useful in reducing the budget deficit, controlling the consumption of fuel oil, and saving non-renewable natural resources. It is appropriate for the state to think hard about switching energy to New and Renewable Energy (EBT) so that people's dependence on fossil energy consumption can be shifted. Therefore, this study aims to determine the current public perception of government policies in the management of fossil fuel energy so that they can be considered by the government in making comprehensive policy decisions. The data used in this study is in the form of primary data obtained from respondents with a population of Indonesian people and collected online through a questionnaire. The data analysis method in this study used the independence test with the chi-square test on categorical data. The results of this study indicate that there is a relationship between the level of public perception of the basic policy of managing electric fuel with the last level of education, type of work, and the area of the population.

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