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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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COMBINATION OF KNN AND PARTICLE SWARM OPTIMIZATION (PSO) ON AIR QUALITY PREDICTION Yahdin, Sugandi; Desiani, Anita; Andhini, Shania Putri; Cahyawati, Dian; Primartha, Rifkie; Arhami, Muhammad; Arinda, Ditia Fitri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The increase in the use of energy sources causes air pollution. The Air Pollutant Index (API) is information about the air quality of a place and at a certain time. API has several parameters, namely SO2, PM10, NO2, O3, and CO. In this study, the KNN method was used to assist categorize air quality. However, all training data were used during the classification process with KNN causes a long prediction process. Another problem with KNN is difficult to determine the optimal value of the K parameter in KNN. The Particle Swarm Optimization (PSO) method can be used for problems on KNN. Therefore, the aim of this study is to predict air quality based on the API by combining the KNN-PSO method. The dataset used is the API dataset for the DKI Jakarta area 2017-2019 totaling 1075 data. The results showed the accuracy for the KNN-PSO method was 98.42% with a precision value of 97.75% and a recall value of 98.13%. To further analyze the results on the combined method, the results of this study were compared with the KNN method only. The results obtained from the KNN method are lower than the KNN-PSO method. So it can be concluded that the KNN-PSO method is great and robust in air quality classification or prediction.

SOME BASIC PROPERTIES OF THE NOISE REINFORCED BROWNIAN MOTION Suryawan, Herry Pribawanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Noise reinforced Brownian motion appears as the universal limit of the step reinforced random walk. This article aims to study some basic properties of the noise reinforced Brownian motion. As main results, we prove integral representation, series expansion, Markov property, and martingale property of the noise reinforced Brownian motion.

COMPARATIVE STUDY: THE DIFFERENCES STUDENTS’ LEARNING BASED ON GENDER Natsir, Irmawaty; Munfarikhatin, Anis; Mayasari, Dian; Suryani, Dessy R.; Pagiling, Sadrack Luden
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

This research is motivated by variuous ways in students absorb, organize and process information received, such as there are students easier to remember the material given visually than auditory or vice, there are students who difficult to write, but good telling stories, there are students who easily disturbed by noise, there are students can't sit still for a long time and there are students prefer practical activities. This study is a comparative to determine differences in learning styles between male and female students at SMP Yapis Merauke. The research sample amounted to 80 students. Data was collected from a learning style questionnaire and analyzed descriptively and inferentially. Descriptive analysis shows dominant male students had auditory and dominant female students had a visual learning style. Inferential analysis with the Anova test showed a significant value of p=0,00 (p<0,05), which means a difference between the learning styles of males and females

THE APPLICATION OF MARKOV CHAIN MODEL TO CALCULATE PREMIUM AND RESERVE OF ENDOWMENT INSURANCE Haryanto, Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The calculation of premiums and reserves are two essential parts of insurance. The calculation of premiums and reserves in life insurance involves using mortality tables. This research constructed a mortality table for 20-year endowment insurance using the Markov chain model. Two reasons make the policy inactive, namely death or withdrawal. The initial age used in this research is 30 years. Meanwhile, the maximum age to join this life insurance is 40 years. The mortality table that has been obtained is used to calculate premiums and reserves. Furthermore, from the research done, it was found that the age of entry to become a member of endowment insurance affects the number of premiums that must be paid. Meanwhile, the number of reserves required will increase with the increase of customers and the period of calculation of reserves

RAINBOW CONNECTION NUMBER AND TOTAL RAINBOW CONNECTION NUMBER OF AMALGAMATION RESULTS DIAMOND GRAPH(〖Br〗_4) AND FAN GRAPH(F_3) Ismail, Sumarno; Hasan, Isran K.; Sigar, Tesya; Nasib, Salmun K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

If be a graph and edge coloring of G is a function , rainbow connection number is the minimum-k coloration of the rainbow on the edge of graph G and denoted by rc(G). Rainbow connection numbers can be applied to the result of operations on some special graphs, such as diamond graphs and fan graphs. Graph operation is a method used to obtain a new graph by combining two graphs. This study performed amalgamation operations to obtain rainbow connection numbers and rainbow-total-connection numbers in diamond graphs ( ) and fan graphs ( ) or . Based on the research, it is obtained that the rainbow-connection number theorem on the amalgamation result of the diamond graph ( ) and fan graph ( is with . Furthermore, the theorem related to the total rainbow-connection number on the amalgamation result of the diamond graph( ) and the fan graph ( is obtained, namely with .

EXPERIENCE STUDY: EFFECT OF UNDERWRITING METHODS ON MORTALITY RATE FOR LIFE INSURANCE PRODUCT AT PT. ABC (2015-2020 PERIOD) Imani, Alvira Adya; Hikmah, Yulial
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

In creating complex mortality tables, some insurance companies do not have enough data to build credible tables based on their experiences. Therefore, insurance companies usually carry out their analysis by comparing the company's actual mortality rate with the expected mortality rate based on industry tables, which is the "A/E ratio". This study aims to determine the best estimates for the mortality rate in PT ABC's underwriting method and its effect on the mortality rate and gross premium. The method used is the actual to expected analysis (A/E Ratio) method. The results of the research and analysis conclude that the more complex the underwriting process assigned to a product, the lower the mortality rate and gross premium.

CHARACTERISTIC ANTIADJACENCY MATRIX OF GRAPH JOIN Irawan, Wahri; Sugeng, Kiki Ariyanti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Let be a simple, connected, and undirected graph. The graph can be represented as a matrix such as antiadjacency matrix. An antiadjacency matrix for an undirected graph with order is a matrix that has an order and symmetric so that the antiadjacency matrix has a determinant and characteristic polynomial. In this paper, we discuss the properties of antiadjacency matrix of a graph join, such as its determinant and characteristic polynomial. A graph join is obtained of a graph join operation obtained from joining two disjoint graphs and .

COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY Puspita, Novi; Afendi, Farit Mochamad; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The number of rainy days is a calculation of the rainy days that occur in one month. In recent years, there has been a decrease in rainy days in some parts of Indonesia. One of the areas at risk of quite a high decreasing number of rainy days is the Bengkulu City area. The decrease in the number of rainy days is one of the impacts caused by climate change. The community will feel the impact of climate change-related to the season, especially those working in the agricultural sector. In compiling the planting calendar, it is necessary to consider the seasons to estimate water availability. This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. The criteria for selecting the best model used was Mean Absolute Deviation (MAD). The MAD value in the SARIMA method was 4,16, 5,07 in the SVR model, and 3,67 in the GA-SVR model. Based on these results, it can be concluded that the GA-SVR model is the best model for forecasting the number of rainy days in Bengkulu City.

DYNAMIC ANALYSIS OF THE COVID-19 MODEL WITH ISOLATION FACTORS Dewi, Atika Ratna; Ananda, Ridho; Rifanti, Utti Marina
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Covid-19 is a type of infectious disease caused by SARS Cov-2. This virus has spread throughout the world to cause a pandemic. This study aimed to model and analyze the spread of Covid-19 with the isolation factor. The spread of Covid-19 can be made into an epidemic model by taking into account the population of susceptible humans (S), infected humans (I), isolated humans (L), and recovered humans (R). The method used in this research was to derive a non-linear system of differential equations model, complete the model qualitatively, find the primary reproduction ratio ( , see the model’s behavior by analyzing the dynamics of the equilibrium point and make model simulations. This model has a disease-free equilibrium point that is asymptotically stable, while at the equilibrium point, the endemic is unstable. The results of the model simulation and analysis of the value indicate that the chance of successful Covid-19 spread and the isolation factor is a significant controlled parameter in reducing the value.

INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS Ahsan, Muhammad; Anam, Arif Khoirul; Julian, Erdi; Jaya, Andi Indra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Network intrusion is any unauthorized activity on a computer network. Attacks on the network computer system can be devastating and affect networks and company establishments. Therefore, it is necessary to curb these attacks. Network Intrusion Detection System (NIDS) contributes to recognizing the attacks or intrusions. This paper explains the factors that influence network attacks. Some machine learning methods are used such as are logistic regression, random forest XGBoost, and CatBoost. The best model is chosen from these models based on its accuracy level. Classification modeling is divided into two types, namely using a dummy and not using dummy variables. The best method for predicting network intrusion is a random forest with a dummy variable that has an Area Under Curve (AUC) value of 92.31% and an accuracy of 90.38%.

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OAI Link

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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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