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Redaksi BAREKENG: Jurnal ilmu matematika dan terapan, Ex. UT Building, 2nd Floor, Mathematic Department, Faculty of Mathematics and Natural Sciences, University of Pattimura Jln. Ir. M. Putuhena, Kampus Unpatti, Poka - Ambon 97233, Provinsi Maluku, Indonesia Website: https://ojs3.unpatti.ac.id/index.php/barekeng/ Contact us : +62 85243358669 (Yopi) e-mail: barekeng.math@yahoo.com

BAREKENG: Jurnal Ilmu Matematika dan Terapan

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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 1 (2023): BAREKENG: Journal of Mathematics and Its Applications" : 60 Documents clear

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY Sitti Sahriman; Andi Sri Yulianti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

APPLICATION OF MAMDANI FUZZY METHOD TO PREDICT THE AMOUNT OF PINE RESIN PRODUCTION Fairus Fairus; Sonia Anisa Putri; Fitra Muliani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

PT. Inhutani IV Aceh District needs to plan the right production in order to achieve maximum profit. Therefore, the company needs to develop a system that can predict the amount of pine resin that can be produced. This study uses data from PT. Inhutani IV Aceh District which is engaged in the production of raw pine resin. This study uses the mamdani fuzzy method to predict the amount of latex production based on demand data, supply data and latex production data per month in 2019-2020. Based on the results of calculations that have been carried out, with the input variable demand in January 2021 of 91,404 kg and supply in December 2020 of 71,466 kg, with the fuzzy mamdani method, the prediction results of the pine resin that the company can produce is 191,763 kg in January 2021 and based on the results of calculations using the MAPE accuracy measure, the fuzzy mamdani method has a MAPE value of 45.69 % so it can be concluded that the mamdani fuzzy method is pretty good for predicting the production of pine resin at PT. Inhutani IV Aceh District.

OVERDISPERSION HANDLING IN POISSON REGRESSION MODEL BY APPLYING NEGATIVE BINOMIAL REGRESSION Yesan Tiara; Muhammad Nur Aidi; Erfiani Erfiani; Rika Rachmawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Statistical analysis that can be used if the response variable is quantified data is Poisson regression, assuming that the assumption must be met equidispersion, where the average response variable is the same as the standard deviation value. A negative binomial regression can overcome an unfulfilled equidispersion assumption where the mean is greater than the standard deviation value (overdispersion). This method is more flexible because it does not require that the variance be equal to the mean. The case studies used in this research are cases of anemia in women of childbearing age (WCA) in 33 provinces of Indonesia. This study aims to apply the Poisson regression method and negative binomial in the case data of anemia in WCA to prove the model's goodness and find the factors that influence anemia in WCA. This data was obtained from biomedical sample data for Riset Kesehatan Dasar (Riskesdas) and data obtained from the website of the Badan Pusat Statistik (BPS) in 2013. By applying these two methods, the result is that negative binomial regression is the best model in modeling WCA cases with anemia in Indonesia because it has the smallest AIC value of 221.72; however, the difference is not too far from the AIC in the Poisson regression model, which is 221.83. It can also be supported that Poisson regression is unsuitable for the analysis because of the case of overdispersion. With a significance level of 10%, the number of WCA affected by malaria per 100 population influences cases of WCA anemia. At the same time, other independent variables have no effect.

FUZZY LOGISTIC REGRESSION APPLICATION ON PREDICTIONS CORONARY HEART DISEASE Vera Febriani; Dian Lestari; Sri Mardiyati; Oktavia Lilyasari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

According to the World Health Organization (WHO) in 2015, 70% of cardiac deaths were caused by coronary heart disease (CHD). Based on WHO data in 2017, 17.5 million deaths were recorded, equivalent to 30% of the total deaths in the world caused by coronary heart disease. Coronary heart disease is a disorder of heart function caused by plaque that accumulates in arterial blood vessels so that it interferes with the supply of oxygen to the heart tissue. This causes reduced blood flow to the heart muscle and oxygen deficiency occurs. In more serious circumstances, it can result in a heart attack. Risk factors for coronary heart disease include age, gender, hypertension, cholesterol, heredity, diabetes mellitus, obesity, dyslipidemia, smoking and lack of physical activity. If a person's chances of suffering from coronary heart disease can be predicted early based on the existing risk factors, then the mortality rate of coronary heart disease can be suppressed. The objective of this study is to build a model that can predict the possibility of a patient suffering from coronary heart disease. The study used the Fuzzy Logistic Regression model. This model was used to maximize prediction results in which data size was limited. The least square method was used to estimate the value of the parameter. We obtained from National Cardiovascular Center Harapan Kita, Jakarta. Evaluation with the Mean Degree of Membership method showed that the model built was feasible and good enough to predict coronary heart disease. By using the confusion matrix, the accuracy of the prediction model is 80.00%, with a specificity of 42.85% and a sensitivity of 100%.

GENERALIZED ORLICZ SEQUENCE SPACES Cece Kustiawan; Al Azhary Masta; Dasep Dasep; Encum Sumiaty; Siti Fatimah; Sofihara Al Hazmy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Orlicz spaces were first introduced by Z. W. Birnbaum and W. Orlicz as an extension of Labesgue space in 1931. There are two types of Orlicz spaces, namely continuous Orlicz spaces and Orlicz sequence spaces. Some of the properties that apply to continuous Orlicz spaces are known, as are Orlicz sequence spaces. This study aims to construct new Orlicz sequence spaces by replacing a function in the Orlicz sequence spaces with a wider function. In addition, this study also aims to show that the properties of the Orlicz sequence spaces still apply to the new Orlicz sequence spaces under different conditions. The method in this research uses definitions and properties that apply to the Orlicz sequence spaces in the previous study and uses the -Young function in these new Orlicz sequence spaces. Furthermore, the results of the study show that the new Orlicz sequence spaces are an extension of the Orlicz sequence spaces in the previous study. And with the characteristics of the -Young function, it shows that the properties of the Orlicz sequence spaces still apply.

A STUDY OF SMALL AREA ESTIMATION TO MEASURE MULTIDIMENSIONAL POVERTY WITH MIXED MODEL POISSON, ZIP, AND ZINB Satria June Adwendi; Asep Saefuddin; Budi Susetyo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The research began with calculating the value of multidimensional poverty at the district level in West Java Province from SUSENAS 2021. The calculation of multidimensional poverty was based on individuals in each district or city household. The dimensional weights are weighed the same, and the indicators in the dimensions are also weighed the same. Furthermore, the simulation study used the Poisson, ZIP, and ZINB mixed models to examine the model's performance on data with cases of excess zero values and overdispersion. The simulation was by generating data without overdispersion and with overdispersion. Overdispersion data was generated with parameters of ω (0.1, 0.3, 0.5, and 0.7), and the model was evaluated from the AIC value. The best method in the simulation study was used to estimate multidimensional poverty in sub-districts in West Java Province using PODES 2021. Simulation studies on data without overdispersion showed no difference in the model's goodness. Overdispersion data shows Mixed Model ZIP and ZINB are better than Mixed Model Poisson. The percentage of the multidimensional poverty population at the sub-district level in West Java Province is quite diverse, from 0.04% to 75.54%.

APPLICATION OF THE BACKPROPAGATION METHOD TO PREDICT RAINFALL IN NORTH SUMATRA PROVINCE Rinjani Cyra Nabila; Arnita Arnita; Amanda Fitria; Nita Suryani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Natural disasters are to blame for the high level of community loss. This is due to the community's lack of information about potential disasters around them. As a result, public understanding of disaster response is extremely low. As a result, weather information is critical for the smooth operation of human activities and activities, such as determining the amount of rainfall. The goal of this research is to identify the best model for predicting rainfall in North Sumatra Province and to forecast rainfall trends for the coming year. The rainfall time series data used in this study were collected from six stations in North Sumatra Province over the last ten years, including the Sibolga Meteorological Station, Aek Godang Meteorological Station, and Silangit Meteorological Station. Backpropagation is used in this study. Backpropagation is one of the methods used in artificial neural networks, which are usually divided into three layers: an input layer, a hidden layer, and an output layer connected by weights. During the training stage, the learning rate, iteration, and number of nodes in the hidden layer were all tested. Following the training process, the best model will be used for testing. The best model was obtained using rainfall data from North Sumatra Province, with an optimal iteration of 1000 iterations, an optimal learning rate of 0.1 in the learning rate trial, and the best number of hidden 5 nodes. During the testing, the MSE values were 0.047 and 0.022, respectively, and the MSE squared value was 0.0022 and 0.00049.

CERTAINTY FACTOR METHODS IN IDENTIFYING INTERESTS AND TALENTS OF ELEMENTARY SCHOOL CHILDREN AL IKHLAS TAQWA Amanda Fitria; Said Iskandar Al Idrus; Asiah Asiah; Nita Suryani; Rinjani Cyra Nabila
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Psychologists and child education experts always remind the importance of knowing interests and talents from an early age to provide a stimulus to children from an early age, because the provision of the stimulus affects the future of children. This study aims to (1) To make calculations in mathematical models to calculate and analyze the interests of children's talents using applications. (2) To create a web-based information system that can facilitate teachers and parents in determining the interests of children's talents at the Al-Ikhlas Taqwa Plus Elementary School using the Certainty Factor method. The method used in this research is the research and development (R&D) method using the certainty factor. The population in this study were all students of SD Plus Al Ikhlas Taqwa Medan T.P 2021/2022 starting from grades 3-6. Sampling was done by purposive sampling technique. Data collection was carried out by interviews, material expert test questionnaires, and media experts, and the results of the children's talent interest questionnaire were processed using the Certainty Factor. The results of this study are the results of interest and talent analysis based on 7 intelligence criteria and also the highest summary results from several criteria with one of the tests yielding a percentage of 93.58% in the field of linguistics.

PREDICTION OF THE POOR RATE K-MEANS AND GENERALIZED REGRESSION NEURAL NETWORK ALGORITHMS (CASE STUDY: NORTH SUMATRA PROVINCE) Nita Suryani; Arnita Arnita; Rinjani Cyra Nabila; Amanda Fitria
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Poverty reduction is a crucial issue and the primary The North Sumatra Provincial government's main concern is lowering the poverty rate, which is a crucial issue. The Province of North Sumatra in Indonesia, one of many nations affected by the Covid-19 pandemic, is particularly troubled economically. In this study, poverty levels were mapped using the K-Means algorithm, and GRNN was then utilized for modeling and prediction. The data source used is time series data from 2010 to 2020 from the Central Statistics Agency (BPS), which includes variables X covering population, health, education, unemployment, and asset ownership and variable Y representing poverty level. The goal of this study is to choose the best model for estimating poverty levels in North Sumatra Province. The districts and cities of Deli Serdang and Medan have the greatest rates of poverty, according to the K-means algorithm's mapping of poverty levels. Additionally, the results of the predicting produced MSE values of 0.004659 and RMSE values of 0.00002108. The value of the smoothness parameter is 0.01.

ALTERNATIVE PROOF OF THE INFINITUDE PRIMES AND PRIME PROPERTIES Dinni Rahma Oktaviani; Muhammad Habiburrohman; Fiki Syaban Nugroho
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Prime numbers is one of kind number that have many uses, one of which is cryptography. The uniqueness of prime numbers in their divisors and distributions causes prime numbers to be widely used in digital security systems. In number theory, one of famous theorem is Euclid theorem. Euclid theorem says about infinitely of prime numbers. Many alternative proof has been given by mathematician to find new theory or approximation of prime properties. The construction of proof give new idea about properties of prime number. So, in this study, we will give an alternative proof of Euclid theorem and investigate the properties of prime in distribution.

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Yopi Andry Lesnussa, S.Si., M.Si

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Kota ambon,

Maluku

INDONESIA

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

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Contact Name Yopi Andry Lesnussa, S.Si., M.Si

Contact Email yopi_a_lesnussa@yahoo.com

Phone +6285243358669

Journal Mail Official barekeng.math@yahoo.com

Location Kota ambon, Maluku INDONESIA

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Contact Name
Yopi Andry Lesnussa, S.Si., M.Si

Contact Email
yopi_a_lesnussa@yahoo.com

Phone
+6285243358669

Journal Mail Official
barekeng.math@yahoo.com

Location
Kota ambon,

Maluku

INDONESIA