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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

COMPARISON OF ANN METHOD AND LOGISTIC REGRESSION METHOD ON SINGLE NUCLEOTIDE POLYMORPHISM GENETIC DATA Adi Setiawan; Rachel Wulan Nirmalasari Wijaya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study aims to determine the goodness of classification using the ANN method on Asthma genetic data in the R program package, namely SNPassoc. SNP genetic data was transformed using codominant genetic traits, namely for genetic data AA, AC, CC were given a score of 0, 0.5 and 1, respectively, while CC, CT and TT were scored 0, 0.5 and 1, respectively. The scoring is based on the smallest alphabetical order given a low score. The average accuracy, precision, recall and F1 score were determined using the neural network method if the genetic code was used with variations in the proportion of test data 10%, 20%, 30% and 40% and repeated B = 1000 times. The results obtained were compared with the logistic regression method. If 20% test data is used and the ANN method is used, the accuracy, precision, recall and F1 scores are 0.7756, 0.7844, 0.9844 and 0.8728, respectively. When all information from various countries is used in the Asthma genetic data, the logistic regression method gives higher average accuracy, precision and F1 scores than the ANN method, but the average recall is the opposite. When a separate analysis is performed for each country, the logistic regression method gives higher accuracy, precision, recall and F1 scores in the ANN method compared to the logistic regression method.

INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK Happy Alyzhya Haay; Suryasatriya Trihandaru; Bambang Susanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.

CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING Giyanti Giyanti; Rina Oktaviyanthi; Usep Sholahudin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Mathematical decision-making ability is a complex cognitive process of finding problems solutions which is continuously explored and optimized for undergraduate students. The current research only focus on categorization on class score average into high, medium and low abilities. As a result, the lecturers do not have any standard categories to classify students’ abilities as a reference in planning supplementary learning that could optimize undergraduate students’ abilities. Therefore, the purpose of this study is to determine a classification model for undergraduate students' mathematical decision-making abilities that require supplementary learning based on the identification of the shortest distance of a new data from an existing data directory. The research method involved data mining techniques with the KNN classification model through the Knowledge Discovery in Database (KDD) process starting from data selection, pre-processing, transformation, data mining and interpretation/evaluation [1]. A total of 100 data were used as research samples which were divided into training data and testing data. Based on the test results, it is obtained that the accuracy of the classification model is 95% for the parameter value k = 15, meaning that each predicted testing data for the classification class is close to the actual condition with the number of neighbors 15 data from the training data.

ANALYSIS OF ROBUST CHAIN LADDER METHOD IN ESTIMATING AUSTRALIAN MOTOR INSURANCE RESERVES WITH OUTLYING DATASET Jonathan Prasetyo Johan; Felivia Kusnadi; Benny Yong
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Reserves are one of the most crucial components for an insurance company to make sure it has enough money to pay off all the incurred claims. The presence of outliers in the incurred claims data harbors risk on inaccurately predicting reserves to cover claim amounts, usually achieved by the standard chain ladder reserving method. To remedy the effect of the outliers, the robust chain ladder reserving method is used by setting the median value to predict estimated reserve. On this research, we utilized both methods on various datasets. The purpose of this paper is to determine the best method that can be utilized by insurance company in various scenario to obtain the most optimized reserved estimate that can minimize the risk of being unable to pay the insurance claim or even the risk of over allocating reserves that could pose profitability issue. The primary data used are the Australian domestic motor insurance claims from 2012 to 2017, obtained from Australian Prudential Regulation Authority (APRA). The dataset is then manipulated to have outliers. After calculating the estimation, the result is compared to assess the strength of the methods using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) calculation. In conclusion, we found that the robust chain ladder reserving method works better in an outlying dataset. We also identify cases in which robust chain ladder are not appropriately used.

POISSON REGRESSION MODELING GENERALIZED IN MATERNAL MORTALITY CASES IN ACEH TAMIANG REGENCY Riska Novita Sari; Ulya Nabilla; Riezky Purnama Sari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Maternal Mortality Rate (MMR) is the number of maternal deaths due to the process of pregnancy, childbirth, and postpartum which is used as an indicator of women's health degrees. The number of maternal deaths in Aceh Tamiang Regency in 2021 is a discrete random variable distributed by Poisson. The purpose of this study is to find out what poisson regression model is generalized in the case of MMR in Aceh Tamiang Regency in 2021 and what factors affect the AKI in Aceh Tamiang Regency in 2021. The research data was obtained from the Aceh Tamiang District Health Office. This type of research is quantitative by using the Generalized Poisson Regression method. The data used are maternal mortality rates and data on factors affecting MMR in Aceh Tamiang Regency in 2021. Influencing factors are the percentage of visits by pregnant women in K1 , percentage of visits by pregnant women K4 , percentage of maternity assistance by health workers , TT immunization of pregnant women , pregnant women who get Fe tablets , and puerperal ministry . Based on the results of research, the factors that affect the maternal mortality rate in Aceh Tamiang Regency in 2021 are TT immunizations for pregnant women (X4) with a p-value of 0.009 which states that for every additional TT immunization of pregnant women by 1%, the average maternal mortality rate also decreases by . The form of the generalized poisson regression model obtained is .

THE APPLICATION OF GUMBEL COPULA TO ESTIMATE VALUE AT RISK WITH BACKTESTING IN TELECOMMUNICATION STOCK Alimatun Najiha; Erna Tri Herdiani; Georgina Maria Tinungki
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The Value at Risk (VaR) method refers to a statistical risk measurement tool used to determine the maximum loss of an investment, while the distribution that must be met is the normal distribution. This is not in line with the actual situation, because the distribution of the return value is found to be not normally distributed but depends on market conditions that occurred at that time, thus invalidating the VaR estimate and resulting in greater portfolio risk. Therefore, in this study, the estimation of risk value will be carried out using the Gumbel Copula method which can model the dependency structure between stocks and is flexible enough to model financial return data from https://finance.yahoo.com/. The parameter estimates produced by the Gumbel Copula method are then used to calculate the VaR at 90%, and 99% confidence levels. The resulting VaR values are 0,076 and 0.231. To test the feasibility of the VaR model, backtesting was carried out and concluded that the VaR value obtained was valid and suitable for use in the risk assessment of PT. XL Axiata Tbk and PT. Telkomunikasi Indonesia Tbk.

SIMULATION OF THE SARIMA MODEL WITH THREE-WAY ANOVA AND ITS APPLICATION IN FORECASTING LARGE CHILLIES PRICES IN FIVE PROVINCES ON JAVA ISLAND Ratna Nur Mustika Sanusi; Budi Susetyo; Utami Dyah Syafitri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Commodities that become potential in the Horticulture Sub-sector are large chilies, so supply and prices must be controlled. One of the efforts that can be made is to predict the price of large chili in the future. However, forecasting is sometimes constrained by several things, such as small sample sizes and outliers. The effect of several factors on the parameter estimation bias can be determined by experimental design by simulating the data obtained from the generation results with several scenarios. The results of the analysis show that all factors have a significant effect on the magnitude of the parameter bias, so that all factors can affect forecasting results. When applying forecasting methods to actual data, paying attention to these three factors is necessary. The application of actual data using the SARIMA method gives good results. It can be seen from the RMSE and MAPE values , which tend to be small. Based on the forecast results for the following 12 periods, it is estimated that the price of big chili in 2022 in five provinces will still fluctuate. The high price of chili in five provinces is predicted to reach its highest in the first three months of 2022. The highest price is predicted to occur in DIY Province in February, which is Rp. 74.230.00/kg. However, from the middle to the end of the year, prices will tend to fall and stabilize. The price will be the lowest in Middle Java Province in December, which is Rp. 20,689.00/Kg.

MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL Nur'ainul Miftahul Huda; Fransiskus Fran; Yundari Yundari; Lisa Fikadila; Fauziah Safitri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The Generalized Space-Time Autoregressive (GSTAR) model is able to utilize modeling of both space and time simultaneously. The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. In this research, a modified weight matrix is presented using the Minimum Spanning Tree approach of graph theory. Prim's algorithm is utilized for calculation here. Not only does the modified weight matrix depend distance, but also highlights the correlation. It makes the modified weight matrix unique. Before starting Prim's algorithm, the correlation is first utilized as an input in forming the initial graph. Following that, find the graph with the least of MST weight. Afterwards, the graph is described utilizing weight matrix, which is applied to the normalization process. Following this, the GSTAR(1;1) modelling process is carried out, beginning with estimating the parameters and then forecasting. The case study is Covid-19 cases that occurred on Java Island between July 2020 (when early Covid-19 entered Indonesia) and the beginning of January 2021. The aim of the research is to model the Covid-19 cases using modified weights and to predict the following five times. The outcome is a GSTAR(1;1) model with modified weights can captures both temporal and spatial patterns. The accuracy of the model is achieved for both the training data and the testing data by the MAPE computations, which yielded of 11.40% and 21.57%, respectively. Predictions are also obtained for each province in the next five times.

REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL Mohammad Fajri; Rais Rais; Lilies Handayani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions.

FORECASTING TOURISM DEMAND DURING THE COVID-19 PANDEMIC: ARIMAX AND INTERVENTION MODELLING APPROACHES Fahriza Rianda; Hardius Usman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The tourism sector in Indonesia is one of the economic sectors severely impacted by the COVID-19 epidemic and its variants. The government is attempting to revive the economy by implementing numerous recovery policies. These economic recovery policies, particularly in the tourism sector, must be backed by a policy evaluation conducted using tourism demand data, such as the number of international visitors visiting Indonesia. However, official data from the BPS-Statistics Indonesia has been released with a two-month delay and is sometimes revised the following month. Consequently, it is necessary to forecast the data for the current situation. In addition, the forecasting model must be modified to account for data conditions caused by the COVID-19 pandemic. This research proposes an ARIMAX forecasting model that utilizes Google Trends and an intervention forecasting model with data sources from BPS and Google Trends. Thus, this research aims to present an overview, develop a model and identify the best forecasting model, and calculate the influence of the intervention on the number of international visitors visiting Indonesia. Compared to the ARIMAX model, the results indicated that the intervention model provided the most accurate forecasts. Not only superior in forecasting results, but the intervention model also demonstrates the magnitude of the intervention's effect on the number of international visitors visiting Indonesia.

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Maluku

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Home Page

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Editorial Team

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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