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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

barekeng
Website

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 1,369 Documents

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THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY Amelia, Reni; Indahwati, Indahwati; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights.

IMPLEMENTATION OF MONTE CARLO MOMENT MATCHING METHOD FOR PRICING LOOKBACK FLOATING STRIKE OPTION Dewi, Komang Nonik Afsari; Lesmana, Donny Citra; Budiarti, Retno
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Monte Carlo method was a numerical method that was popular in finance. This method had disadvantages at convergences, so the moment matching was used to improve the efficiency from Monte Carlo method. The research has discussed about pricing of the lookback floating strike option using the Monte Carlo moment matching method. The monthly stock price of PT TELKOM from 2004 to 2021 that used in this research. The results obtained by adding variance reduction moment matching in Monte Carlo method, which produces a relatively had smaller error when compared to the relative error of the standard Monte Carlo method. The orders of convergence from Monte Carlo method with variance reduction moment matching for call and put option are about 1.1 and 1.4. The conclusion that addition of the moment matching can increase the efficiency of the Monte Carlo method in determining the price of the lookback floating strike option.

TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO) Rizal, Muhammad Edy; Wigena, Aji H; Afendi, Farit M
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values. While univariate imputation methods are convenient and flexible since no other variable is required, multivariate imputation methods can potentially improve imputation accuracy given that the other variables are relevant to the target variable. Many multivariate imputation methods have been proposed, one of which is Vector Autoregression Imputation Method (VAR-IM). This study aims to compare imputation results of VAR-IM-based methods and univariate imputation methods on time series data, specifically on long lag seasonal data such as daily weather data. Three modified VAR-IM methods were studied using simulations with three steps: deletion, imputation, and evaluation. The deletion step was conducted using six different schemes with six missing proportions. The simulations were conducted on secondary daily weather data collected from meteorological station of Citeko from January 1, 1991, to June 22, 2013. Nine weather variables were examined, that is the minimum, maximum, and average temperatures, average humidity, rainfall rate, duration of solar radiation, maximum and average wind speed, as well as wind direction at maximum speed. The simulation results show that the three modified VAR-IM methods can improve the accuracies in around 75% of cases. The simulation results also show that imputation results of VAR-IM-based methods tend to be more stable in accuracy as the missing proportion increase compared to the imputation results of univariate imputation methods.

MILLENNIAL FINANCIAL ATTRIBUTES: STRUCTURAL MODELLING APPROACH Hakim, Lukmanul; Syafitri, Ridha
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study aims to determine the influence of Financial Literacy on electronic financial transaction decisions, the influence of financial management behavior on electronic financial transaction decisions, the influence of financial attitude on electronic financial transaction decisions, the influence of inclusion on electronic financial transaction decisions, the influence of transaction decisions electronic finance against consumptive behavior. This type of Research is Survey Research with Quantitative Approach. The samples used in this study were 96 samples with sampling techniques using non-probability and purposive sampling techniques. The data analysis technique used is Structural Equation Modeling (SEM). The results of this study show that financial literacy on electronic financial transaction decisions, financial management behavior towards consumptive behavior, financial attitude towards electronic financial transaction decisions and electronic financial transaction decisions towards consumptive behavior have a significant effect. Meanwhile, financial literacy towards consumptive behavior, financial management behavior towards electronic financial transaction decisions, financial attitude towards consumptive behavior, inclusion of electronic financial transaction decisions and inclusion of consumptive behavior have no significant effect.

POISSON REGRESSION MODELLING OF AUTOMOBILE INSURANCE USING R Vantika, Sandy; Yudhanegara, Mokhammad Ridwan; Lestari, Karunia Eka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Automobile insurance benefits are protecting the vehicle and minimizing customer losses. Insurance companies must provide funds to pay customer claims if a claim occurs. Insurance claims can be modelled by Poisson regression. Poisson regression is used to analyze the count data with Poisson distributed data responses. this paper, the data model of sample is automobile insurance claims from the companies in one year (in 2021) of observation which contains three types of insurance products, i.e., Total Loss Only (TLO), All Risk, and Comprehensive. The results of data analysis show that the highest number of claims comes from Comprehensive insurance products, especially if the premium value gets more extensive. In contrast, the least comes from TLO insurance products.

COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA Pramoedyo, Henny; Ariyanto, Danang; Aini, Novi Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data.

MULTILEVEL REGRESSION WITH MAXIMUM LIKELIHOOD AND RESTRICTED MAXIMUM LIKELIHOOD METHOD IN ANALYZING INDONESIAN READING LITERACY SCORES Santi, Vera Maya; Kamilia, Rifa; Ladayya, Faroh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure. The problem with this data structure is that individuals in the same group tend to have the same characteristics, so the observations at lower levels are not independent. Education research often produces a hierarchical structure, one of which is PISA data, where students as level-1 nested within schools as level-2. In the PISA 2018 survey, reading literacy is the main focus. The data are sourced from the Organisation for Economic Co-operation and Development (OECD). The survey results show that the reading literacy scores of Indonesian students have decreased, thus placing Indonesia at 74th out of 79 countries. However, it is still very rare to research the reading literacy of Indonesian students' using a multilevel regression model. This study aims to apply a multilevel regression model to determine the factors influencing Indonesian reading literacy scores in PISA 2018 survey data. The results of this study indicate that the factors that influence response variable are gender, grade level, mother's education, facilities at home, age at school entry, student discipline behavior at school, and failing grade, while at the school level are the type of school and school location. The magnitude variance of student reading literacy scores can be explained by the explanatory variables the student level is 11,42% and the school level is 60,66%, while the rest is explained by another factor outside the study.

A COMPARISON OF COX PROPORTIONAL HAZARD AND RANDOM SURVIVAL FOREST MODELS IN PREDICTING CHURN OF THE TELECOMMUNICATION INDUSTRY CUSTOMER Nurhaliza, Sitti; Sadik, Kusman; Saefuddin, Asep
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The Cox Proportional hazard model is a popular method to analyze right-censored survival data. This method is efficient to use if the proportional hazard assumption is fulfilled. This method does not provide an accurate conclusion if these assumptions are not fulfilled. The new innovative method with a non-parametric approach is now developing to predict the time until an event occurs based on machine learning techniques that can solve the limitation of CPH. The method is Random Survival Forest, which analyzes right-censored survival data without regard to any assumptions. This paper aims to compare the predictive quality of the two methods using the C-index value in predicting right-censored survival data on churn data of the telecommunication industry customers with 2P packages consisting of Internet and TV, which are taken from all customer databases in the Jabodetabek area. The results show that the median value of the C-index of the RSF model is 0.769, greater than the median C-index value of the CPH model of 0.689. So the prediction quality of the RSF model is better than the CPH model in predicting the churn of the telecommunications industry customer.

AGGLOMERATIVE HIERARCHICAL CLUSTERING ANALYSIS IN PREDICTING ANTIBACTERIAL ACTIVITY OF COMPOUND BASED ON CHEMICAL STRUCTURE SIMILARITY Siswanto, Siswanto; Syahrir, Nur Hilal A
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Resistance to antibiotics is increasing to alarmingly high levels. As antibiotics are less effective, more infections are becoming more complex and often impossible to treat. Numerous antibiotics discovered in marine organisms show that the marine environment, which accounts for over half of the world's biodiversity, is a massive source for novel antibiotics and that this resource must be explored to identify next-generation antibiotics. This research aimed to predict antibacterial activity in marine compounds using a computational approach to reduce the cost and time of finding marine organisms, extracting, and testing numerous unknown marine compounds' bioactivities. We used a simple unsupervised learning approach to predict the biological activity of marine compounds using agglomerative hierarchical clustering. We mixed antibiotic drug data in DrugBank Database and chemical compound data from marine organisms in literature to compile our dataset. We applied five linkage methods in our dataset and compared the best method by assessing internal validation measurement. We found that the Ward with squared dissimilarity matrix is the best method in the dataset, and ten compounds from 73 compounds of the marine compound are determined as potential marine compounds which have antibacterial activity.

BICLUSTERING APPLICATION IN INDONESIAN ECONOMIC AND PANDEMIC VULNERABILITY Ningsih, Wiwik Andriyani Lestari; Sumertajaya, I Made; Saefuddin, Asep
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.

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

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Journal Mail Official barekeng.math@yahoo.com

Location Kota ambon, Maluku INDONESIA

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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