cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
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
Editorial Address
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
Location
Kota ambon,
Maluku
INDONESIA
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 1,248 Documents
 61   62   63   64   65 
COMBINATION OF SAW-TOPSIS AND BORDA COUNT METHODS IN SEQUENCING POTENTIAL CONVALESCENT PLASMA DONORS Ilmiyah, Nur Fadilatul; Al Hasani, Salma Zahrotun Nihayah; Renaningtyas, Della
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1521-1532

Abstract

Convalescent Plasma Therapy (CPT) is an additional therapy to increase the chances of recovery for patients infected with COVID-19. CPT is carried out by giving blood plasma from COVID-19 survivors to COVID-19 patients. Not all survivors of COVID-19 can become plasma donors. Several criteria must be met. Therefore, selecting and sequencing potential plasma donors can be considered an act of decision-making. This research aims to provide an overview of the application of the SAW-TOPSIS combination and the Borda Count method in selecting and ranking potential plasma donor candidates. The criteria for prospective plasma donors are limited to six aspects, namely age, weight, history of blood transfusion, gender, pregnancy status, history of being infected with COVID-19, and history of previous illnesses. Data was taken from ten COVID-19 survivors to illustrate the application of the three methods. The data is taken from a questionnaire distributed via Google Forms. This research was carried out through 3 stages: applying the SAW method, the TOPSIS method, and the Borda Count method. From the calculated results, P06 was the most potential plasma donor candidate, followed by P03, P09, P02, and P04.
COMPARISON OF FORECASTING RICE PRODUCTION IN MAGELANG CITY USING DOUBLE EXPONENTIAL SMOOTHING AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Imron, M.; Khaulasari, Hani; SNM, Diva Ayu; Inayah, Jauharotul; S, Eka Eliyana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1533-1542

Abstract

Magelang City has experienced a significant decline in the rice production sector, triggering the need for forecasting research as the next crucial step. This research aims to forecast rice production in Magelang city. By applying Double Exponential Smoothing and ARIMA methods, the most suitable forecasting model is identified. Data on rice production was obtained from the Badan Pusat Statistik (BPS) of Magelang city. The results revealed that the ARIMA (0,1,1) model with MSE of 479,259 was the best choice. This model is expressed as . Using this model, rice production was forecast from July to December 2023, the forecasting results showed that rice paddy production is expected to fluctuate in the coming months. For July 2023, production is projected to be around 65,1762 units, followed by 51,4779 units in August, 58,2432 units in September, and so on.
ON RAINBOW ANTIMAGIC COLORING OF SNAIL GRAPH(S_n ), COCONUT ROOT GRAPH (Cr_(n,m) ), FAN STALK GRAPH (Kt_n ) AND THE LOTUS GRAPH(Lo_n ) Adawiyah, R; Makhfudloh, I I; Dafik, Dafik; Prihandini, RM; Prihandoko, AC
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1543-1552

Abstract

Rainbow antimagic coloring is a combination of antimagic labeling and rainbow coloring. Antimagic labeling is labeling of each vertex of the graph with a different label, so that each the sum of the vertices in the graph has a different weight. Rainbow coloring is part of the rainbow-connected edge coloring, where each graph has a rainbow path. A rainbow path in a graph is formed if two vertices on the graph do not have the same color. If the given color on each edge is different, for example in the function it is colored with a weight , it is called rainbow antimagic coloring. Rainbow antimagic coloring has a condition that every two vertices on a graph cannot have the same rainbow path. The minimum number of colors from rainbow antimagic coloring is called the rainbow antimagic connection number, denoted by In this study, we analyze the rainbow antimagic connection number of snail graph , coconut root graph , fan stalk graph and lotus graph .
ANALYSIS OF NEW CHAOTIC MAP AND PERFORMANCE EVALUATION IN ITS APPLICATION TO DIGITAL COLOR IMAGE ENCRYPTION Solihat, Ita Mar'atu; MT, Suryadi; Satria, Yudi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1553-1564

Abstract

In this research a new chaotic map which is a modification from composition of MS map and Improved logistics map is proposed. New map’s chaotic behavior is proven by the bifurcation diagram and Lyapunov exponent. This map will be used in chaos-based cryptography as a keystream generator and then it will be processed in the encryption and decryption algorithms through XOR operations. The results of the encryption and decryption processes were evaluated by several tests such as key sensitivity analysis, histogram analysis, correlation analysis, and image quality analysis. All the tests are doing to evaluate the performance new chaotic map in encryption of digital color image. Based on the results of several tests, a conclusion can be drawn that the encryption and decryption process is successful and difficult to attack with various kinds of attacks. The key that built from new chaotic map has a good sensitivity.
TUBERCULOSIS CASE MODEL USING GCV AND UBR KNOT SELECTION METHODS IN TRUNCATED SPLINE NONPARAMETRIC REGRESSION Anggraeni, Sitti; Sifriyani, Sifriyani; A'yun, Qonita Qurrota
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1565-1574

Abstract

The nonparametric regression approach is used when the shape of the regression curve is not known. The advantage of nonparametric regression is that it has a high degree of flexibility. The truncated spline is a method in the nonparametric regression approach, which can overcome changing data patterns at certain sub-intervals with the help of knot points. The purpose of this research is to obtain the best truncated spline nonparametric regression model estimates based on the GCV and UBR knot point selection methodsThe data used in this study came from the publications of the Indonesian Ministry of Health and BPS Indonesia. The response variable used is the percentage of successful treatment of tuberculosis patients in Indonesia with predictor variables namely the percentage of people who smoke over the age of 15 years, the percentage of households that have access to proper sanitation, the percentage of poor people, the percentage of food processing establishments that meet the standard requirements , national health insurance membership coverage and percentage of accredited hospitals. The results showed that the best model came from the GCV method using three knots. This model produces an MSE value of 3.65 with value of 97.04. The value indicates that the predictor variable used in this study affects the response variable by 97.04% while the other 2.96% is influenced by other variables that are not included in this study.
CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Nur, Indah Manfaati; Nugrahanto, Rifqi; Fauzi, Fatkhurokhman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1575-1584

Abstract

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.
A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND NAIVE BAYES CLASSIFICATION USING UNBALANCED DATA HANDLING Lestari, Nila; Indahwati, Indahwati; Erfiani, Erfiani; Julianti, Elisa D
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1585-1594

Abstract

Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test data. The analysis shows that SMOTE can perform better than not handling unbalanced data. Based on the results of the study, the best method for predicting the incidence of anemia is the Naïve Bayes method, with the sensitivity value of 82%.
DETERMINATION OF BANK INDONESIA SCHOLARSHIP RECIPIENTS USING NAÏVE BAYES CLASSIFIER Febri, Fera Malianis; Sari, Devni Prima
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1595-1604

Abstract

A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.
PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST Akbar, Taufiq; Tinungki, Georgina Maria; Siswanto, Siswanto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1605-1616

Abstract

Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test. However, the presence of outliers in the data can affect the grouping results, so methods that are robust to outliers are used, such as K-Medoids and Density-Based Spatial Clustering of Applications with Noise. The purpose of this study is to compare the results and performance of the two methods using the silhouette coefficient test on data on human development indicators in South Sulawesi Province in 2021. The results of the analysis show that K-Medoids produced 2 groups, namely the districts/cities group which has indicators of human development that consist of 21 districts/cities, and the high group, which consists of 3 districts/cities, while Density-Based Spatial Clustering of Application with Noise produces 1 group that has the same characteristics, which consists of 19 districts/cities, and the remaining 5 districts/cities are identified as noise. Based on the silhouette coefficient test, K-Medoids have a greater value than Density-Based Spatial Clustering of Application with Noise, namely 0,635 and 0,544, respectively, so that K-Medoids have better performance.
CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS Ermawati, Ermawati; Sriliana, Idhia; Sriningsih, Riry
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1617-1630

Abstract

Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.

Page 63 of 125 | Total Record : 1248

 61   62   63   64   65 

Filter by Year

2007 2026


Reset
Filter By Issues
All Issue Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 4 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 13 No 3 (2019): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 13 No 2 (2019): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 13 No 1 (2019): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 12 No 2 (2018): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 12 No 1 (2018): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 11 No 2 (2017): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 11 No 1 (2017): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 10 No 2 (2016): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 10 No 1 (2016): BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 9 No 2 (2015): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 9 No 1 (2015): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 8 No 2 (2014): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 8 No 1 (2014): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 7 No 2 (2013): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 7 No 1 (2013): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 6 No 2 (2012): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 6 No 1 (2012): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 5 No 2 (2011): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 5 No 1 (2011): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 1 No 2 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan Vol 1 No 1 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan More Issue