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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 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
STABILITY ANALYSIS OF CELLULAR OPERATING SYSTEM MARKET SHARE IN INDONESIA WITH THE COMPETITIVE LOTKA-VOLTERRA MODEL Haning Puspita, Aulia Salsabila; Sutrima, Sutrima; Setiyowati, Ririn; Wibowo, Supriyadi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0333-0340

Abstract

The current increase in smartphone users has caused various mobile device operating system companies to compete with each other to create a mobile device operating system that is suitable and acceptable to the public. Competition among mobile operating system market shares can be analyzed using the Lotka-Volterra model. This study aims to reconstruct the Lotka-Volterra model for the cellular operating system market share in Indonesia. In addition, a stability analysis was carried out, which aims to determine the stability of the competitive model for the market share of the operating system in Indonesia. The results of the study show that a competitive Lotka-Volterra model can be built on the Android and iOS operating system market share in Indonesia. In this model, there are four equilibrium points, one of which is unstable, and the other three equilibrium points are conditionally stable.
ARIMA MODEL VERIFICATION WITH OUTLIER FACTORS USING CONTROL CHART Umairah, Tarisa; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0579-058

Abstract

Control charts are often used in quality control processes, especially in the industrial sector because of their significant benefits in increasing industrial production. However, control charts can also be used throughout the field of time series modeling to evaluate measures of accuracy represented by a particular time series model. The application of control charts in this research meets the criteria for evaluating accuracy. However, it is not certain that the time series model will have a high level of accuracy. There are various factors that can influence this phenomenon, one of which is the potential for outliers. Therefore, it is very important to perform time series modeling by adding an outlier factor. The residuals of the time series model obtained are used to create a control chart for model verification. The aim of this research is to evaluate the validity of time series models by looking at the influence of outlier characteristics to improve their accuracy. This research studies the accuracy of a time series model built using Gross Domestic Product (GDP) data in Indonesia. There are two different models, namely the ARIMA model without outlier factors and the ARIMA model with outlier factors which are used for research purposes. Both models were performed using the same data set. The results of this study indicate that the ARIMA model with outlier factors has better accuracy than the ARIMA model without outlier factors. This conclusion can be drawn based on the observation that the residual value is within the predetermined control limits, thus indicating that the process is in a state of statistical control.
APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA Junita, Tarisya Permata; Kartikasari, Mujiati Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0341-0348

Abstract

Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the impact of the Russia-Ukraine conflict. It is known that the value of oil and gas exports affects the Indonesian economy. Any increase in the value of oil and gas exports will be followed by an increase in economic growth, and vice versa. However, over time, the value of oil and gas exports has decreased every year. Therefore, forecasting the value of oil and gas exports is needed so that the country's economic sector development strategy can be on target. In addition, oil and gas export forecasting is also needed to determine the distribution of goods exports that must be carried out. In this study, we forecast the value of oil and gas exports using the neural network autoregressive (NNAR) method. The choice of this method is made because there is no assumption of normality of the residuals and white noise like in autoregressive models. From the NNAR method, the best model results are obtained, namely NNAR (2,3) with a MAPE value of 11.75640%, which means that this model has very good forecasting performance.
NUMERICAL MODELING OF THE 1998 PAPUA NEW GUINEA TSUNAMI USING THE COMCOT Qonita, Zulfa; Karima, Shofia; Rusdiansyah, Alfi; Riyandari, Ritha
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0349-0360

Abstract

The Papua New Guinea tsunami of 1998 is a unique phenomenon because the source of the tsunami propagation has been speculated. There was a 7.1-magnitude earthquake on July 17, 1998, at 18:49 WIT before the tsunami hit the Aitape area. However, previous studies have shown that the leading cause of the tsunami was not the earthquake but a submarine landslide. One of the steps to simulating the event is to do tsunami modeling. A tsunami propagation simulation will be conducted using Cornell Multi-grid Coupled Tsunami (COMCOT). This simulation was carried out with three scenarios to see which had the most significant effect on the tsunami event. The first scenario uses a tsunami source from a 7.1 magnitude earthquake, the following scenario is carried out using avalanche parameters, and the last scenario is a scenario with a combined source of earthquake and avalanche. The results of this study indicate that underwater landslides are the source of a tsunami similar to the original event.
CALCULATION AND OPTIMIZATION OF FORCE AND POWER ON AUTONOMOUS SURFACE VEHICLE (ASV) AS MEANS OF MARINE ACCIDENT RESCUE Herlambang, Teguh; Nurhadi, Hendro
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0361-0372

Abstract

The strategic regional position makes Indonesia a world transportation crossing route with various modes of transportation through Indonesian territory to reach other islands, countries, or even continents. For this reason, a solution is needed to reduce the number of fatalities and injuries due to ship accidents that may occur. By reducing the evacuation time to a minimum, it will be possible to minimize the number of casualties and injuries if the ship has an accident. Considering that, in terms of usability and benefits, the Autonomous Surface Vehicle (ASV) can be an alternative as a form of Search and Rescue (SAR) ship. Based on the results of the ASV Ship design that suits these needs, a force and power analysis was carried out in accordance with the applicable theory. Of the 2 designs with a monohull shape and with different hull variations, with the main dimensions of the ASV Sang Nagari (LH=4.55 m, LWL=4.348 m, B=1.272 m, D=0.804 m T=0.45 m) and ASV Sang Nadibumi (LH=4.55 m, LWL=4.311 m, B=1.352 m, D= 0.802 m, T=0.4 m) obtained a displacement of 1.063 tons and a resistance of 1 kN for ASV Sang Nagari and 1.202 tons and a resistance of 2.6 kN for ASV Sang Nadibumi at a standard speed of 10 knots. Based on the results of the force analysis, it is concluded that the two ASVs have 2 forces in static conditions, that is, weight force and upward lift force (FBouyant). Based on the results of the efficiency of ASV Sang Nagari has a higher efficiency of 0.002% with 56.422% than ASV Sang Nadibumi with 56.420%.. The ASV linear model made from linearization has the properties of a controllable and observable, so this model can be applied to navigation and control systems.
K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM Afrianti, Yuli Sri; Pasaribu, Udjianna Sekteria; Sulaiman, Fadhil Hanif; Angelia, Grace; Wattimanela, Henry Junus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0589-0602

Abstract

Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.
NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA Hardianti, Rindu; Solimun, Solimun; Nurjannah, Nurjannah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0373-0382

Abstract

This study aims to obtain the main component score of the ability to pay latent variable, determine the strongest indicators forming the ability to pay on a mixed scale based on defined indicators, and model the ability to pay on time mediated by fear of paying using path analysis. The data used in this study is secondary data from mortgage-paying customers with a sample size of 100. The method used is nonlinear principal component analysis with path analysis modeling. The results of this study indicate that the eleven variables formed by PC1 or X1 are able to store diversity or information by 32.50%, while 67.50% of diversity or other information is not stored (wasted). The credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgages has a significant effect on payments by mediating the fear of paying late with a coefficient of determination of 80.40%.
ESTIMATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL WITH BISQUARE KERNEL WEIGHTING FUNCTION ON PERCENTAGE OF STUNTING TODDLERS IN INDONESIA Asnita, Asnita; Sifriyani, Sifriyani; Fauziyah, Meirinda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0383-0394

Abstract

Stunting is a condition of failure to thrive in children under five years old due to chronic malnutrition. Efforts that can be made to reduce the incidence of stunting in Indonesia are to identify factors that are thought to affect the incidence of stunting in Indonesia. The analysis methods used in this study are the global Fixed Effect Model (FEM) and the local Geographically Weighted Panel Regression (GWPR) model. FEM is a global regression model that assumes that each individual's model has a different intercept value. While GWPR is a local regression model from FEM that considers aspects of geographic location, by repeating data at each observation location, different times, and using spatial data. The weighting function used in this study is fixed bisquare and adaptive bisquare. This study aims to obtain a GWPR model on the percentage of stunting toddlers in Indonesia in 2019 until 2022 with independent variables, namely the percentage of children receiving exclusive breastfeeding , the percentage of households that have access to proper sanitation , the average per capita health expenditure of the population for a month , the average length of schooling for women , and the number of poor people . The variables are obtained from Statistics Indonesia (BPS) and Study of Indonesia’s Nutritional Status (SSGI). The results showed that the best weighting function, namely adaptive bisquare with a CV value of 264.80.
UNDERWATER OBJECT SHAPE DETECTION BASED ON TONAL DISTRIBUTION AND EDGE DETECTION USING DIGITAL IMAGE PROCESSING Suryowinoto, Andy; Herlambang, Teguh; Baital, Muhammad Sawal; Tomasouw, Berny Pebo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0395-0402

Abstract

Underwater exploration activities always have their own charm, many exotic objects that exist in underwater ecosystems have not been mapped properly, due to the lack of related databases of the shapes and names of these underwater objects. Another factor that affects the visibility of objects related to the quantity of light intensity that enters under water, also not as much above the surface of the abundant water, especially during the day. This also hinders the process of documenting underwater objects. The main purpose of this study was to obtain the shape of underwater objects for several conditions of light intensity under water using a low cost digital image sensor camera. The method used in this research is to combine tonal distributions with object edge detection in digital image processing. The test results show that object detection tests in clear and turbid water can detect objects even though they are using a low-cost and low-resolution camera, but with the help of adequate lighting it can be done. From that it can be concluded that the detection of underwater objects is successful.
PERFORMANCE COMPARISON OF DECISION TREE AND LOGISTIC REGRESSION METHODS FOR CLASSIFICATION OF SNP GENETIC DATA Setiawan, Adi; Setivani, Febi; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0403-0412

Abstract

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.

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