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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
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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 1,248 Documents
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ANALYZING THE LEVEL OF CREDIT FAILURE USING THE AUTOREGRESSIVE DISTRIBUTED LAG TO MAINTAIN STABILITY OF COMMERCIAL BANKS IN MALUKU PROVINCE Lewaherilla, Norisca; Sinay, Lexy Janzen; Damamain, Ferina Lestari; Sopaheluwakan, Marsa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp843-860

Abstract

Commercial banks are banks that carry out business activities conventionally and or based on sharia principles, which in their activities provide services in payment traffic. The health level of a commercial bank is the result of an assessment of the bank's condition based on risk and bank performance. Commercial Bank performance assessment can use the proxy of asset ownership, namely Return on Assets (ROA). While the risk assessment of commercial banks can use the credit risk proxy used is the Non-Performing Loan (NPL) ratio. The purpose of this study is to examine the Health Level of Commercial Banks in Maluku Province using ROA and NPL based on bank internal factors (bank specific) and macro and micro economic conditions in Maluku Province. The data used is quarterly time series data, namely in the first quarter of 2014 - first quarter of 2022. The method used is multivariate time series data analysis, namely the Autoregressive Distributed Lag (ARDL) model. The results obtained are the Health Level of Commercial Banks in Maluku Province in the first quarter of 2014 - first quarter of 2022 is classified as healthy and stable, even though the Maluku economy is experiencing the impact of the COVID-19 Pandemic. Internal (specific) bank factors are very dominant in influencing the performance and risk of Commercial Banks in Maluku Province compared to macro and micro economic factors. This means that the policies and performance of all parties related to Maluku's economic conditions need to be improved in maintaining the stability and soundness of commercial banks. In general, the performance of all parties in maintaining the health level of Commercial Banks in Maluku Province is very good, especially during the COVID-19 Pandemic.
STABILITY ANALYSIS OF A MATHEMATICAL MODEL OF RABIES SPREAD WITH VACCINATION IN HUMAN AND DOG POPULATIONS, INCLUDING AWARE AND UNAWARE EXPOSED SUBPOPULATIONS Sahusilawane, Maria Engeline; Ilwaru, Venn Yan Ishak; Lesnussa, Yopi Andry; Beay, Lazarus Kalvein; Ojo, Mayowa Micheal; Permadi, Vynska Amalia; Peter, Olumuyiwa James
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp861-878

Abstract

Rabies is a zoonotic disease that causes progressive and fatal inflammation of the brain and spinal cord, which can be prevented by vaccination. This study aims to analyze the stability of a mathematical model of rabies disease spread with vaccination in human and dog populations in Maluku Province. The model uses a system of ordinary differential equations that separates the human population into six subpopulations (6 variables) and the dog population into three subpopulations (3 variables). The new variables are unaware subpopulations that we divide from aware subpopulations. The results showed that disease-free and endemic equilibrium points could be achieved, and the stability of these equilibrium points was analyzed using basic reproduction numbers Both disease-free and endemic equilibrium points are locally asymptotically stable. The Numerical simulations were also conducted to determine the characteristics of each subpopulation. This study was to provide better insight into controlling the spread of rabies in Maluku Province and it can be used as a reference in developing mathematical models for other infectious diseases.
POSITIVE CONFIRMED PREDICTION OF COVID-19 IN EAST JAVA USING COUNT TIME SERIES BASED DOUBLE POISSON INAR(p) PROCESS Sofro, A'yunin; Subiantoro, Framitha Septian; Khikmah, Khusnia Nurul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp879-888

Abstract

In December 2019, there was a virus outbreak caused by a virus disease with a relatively high spread in Indonesia, one of which was in East Java Province. It is proven by the number of new cases on January 15, 2021, in East Java, reaching 12818 cases. This is why researchers predict the number of positive cases of COVID-19 in East Java so that the Government can anticipate an increase in the number of COVID-19 patients. This study uses data on the addition of positive COVID-19 cases in East Java from May 16, 2020, to January 24, 2021. Because the count time series data shows overdispersion, predictions are made by modeling the COVID-19 data using the INAR( ). development model, namely Double Poisson INAR( ). Several tests were carried out with data from the Double Poisson distribution, and then the ACF and PACF plots were analyzed to find the order of INARDP. After obtaining the order, the model can be constructed and estimated using MLE. Then, the prediction of adding COVID-19 cases in East Java on January 25, 2021, obtained 949 cases with an estimated error of 13.73 percent. So, the model show that the accuracy of the forecasted value with actual value is 86.17 percent.
GOJEK DATA ANALYSIS THROUGH TEXT MINING USING SUPPORT VECTOR MACHINE (SVM) AND K-NEAREST NEIGHBOR (KNN) Hasanah, Siti Hadijah; Maulana, Muhamad Riyan; Nurdiana, Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp889-902

Abstract

The main focus of this research is to apply and test the effectiveness of SVM and KNN methods in Gojek data text analysis. This research will examine how the two methods can classify user comments and feedback and identify data sentiment analysis at the same time practically help Gojek understand user needs and improve service quality. The data obtained through scrapping is categorized into positive and negative sentiment. Data is taken from Gojek application user reviews throughout the year 2022 with a total of 1148 sentiment data with a percentage of 80% training data and 20% testing data. Evaluation of model performance using Confusion Matrix and AUC-ROC Curve shows that SVM is more effective than KNN, with accuracy on training data of 92.55% for SVM and 81.71% for KNN, as well as accuracy on testing data of 82.40% for SVM and 77,09% for KNN.
GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING OF POVERTY RATES IN TROPICAL RAINFOREST AREAS OF KALIMANTAN Mumtaz, Ghina Fadhilla; Suyitno, Suyitno; Sifriyani, Sifriyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp903-916

Abstract

When applied to spatial panel data, the Geographically Weighted Panel Regression (GWPR) model is a localized version of the linear regression model. The Fixed Effect Model (FEM) inside estimator is used as a global model in this investigation. The purpose of this research is to obtain a GWPR model and identify the variables that affect the proportion of the impoverished in 56 districts and cities located in Kalimantan's humid tropical forest region between 2019 and 2022. The Weighted Least Square (WLS) approach, which provides geographic weighting in addition to the Least Square method, is used for estimating the parameters of the GWPR model. The optimal weighting function chosen from the adaptive bisquare, adaptive tricube, and adaptive gaussian weightings is the spatial weighting function used in the GWPR model estimate in this work. For determining the ideal bandwidth, the Cross Validation (CV) criterion is applied. According to the study's findings, the optimal weighting function is adaptive gaussian, which yields the best GWPR model with a CV of 8.8740 at the lowest. The GWPR model parameters were tested, and the results showed that both local and global influences affect the percentage of the population living in poverty. The gross domestic product (GDP), the open unemployment rate, the average length of education, the number of workers, and life expectancy are local factors that affect the percentage of the poor; on the other hand, the number of workers is a global factor that affects the percentage of the poor.
THE DESIGN OF STANDARD GRAPH FOR TODDLER GROWTH USES NONPARAMETRIC PENALIZED SPLINE REGRESSION Kartini, Alif Yuanita; Budiani, Jauhara Rana; Arifat, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp917-926

Abstract

One way to carry out early detection of toddler growth is through the Healthy Way Card (KMS). The KMS used in Indonesia does not describe the growth behavior of toddlers. The KMS used is the standard from the World Health Organization (WHO). Apart from that, the growth chart for toddlers at each age will show different patterns. This pattern does not form a linear graph or a particular pattern. Therefore, the Nonparametric Regression method was used using a penalized spline estimator which produces a local Indonesian standard KMS which is used to assess the growth of toddlers. Designing KMS with a confidence interval approach to nonparametric regression values using a penalized spline estimator. Data was obtained from the results of the recapitulation of Posyandu in Bojonegoro from January to December 2023, totaling 120 data. The variables used in this research are the toddler's weight (y) as the response variable and the toddler's age (x) as the predictor variable. In nonparametric regression modeling using a penalized spline estimator with several combinations of numbers and knot point locations. Selection of optimal knot points using minimum Generalized Cross Validation (GCV). Based on the results of the analysis, it shows that there are different times of weight change for male toddlers and female toddlers in Bojonegoro. The weight of male toddlers in Bojonegoro has 3 patterns of change, namely the weight of male toddlers increases drastically until the age of 16 months, then increases slowly until the age of 55 months. Then the weight of male toddlers will increase again drastically after the age of 55 months. Meanwhile, the weight of female toddlers in Bojonegoro has three patterns of change, namely the weight of female toddlers increases drastically until the age of 5 months, then increases slowly until the age of 15 months, and again increases drastically after the age of 15 months. This can be caused by physical differences in babies based on gender. To create a standard chart for toddlers' weight growth based on age, it was analyzed by calculating the percentile values consisting of P3, P15, P50, P85, and P97 for each toddler age category.
SMALL AREA ESTIMATION OF THE PERCENTAGE HOUSEHOLDS WITH FOOD EXPENDITURE SHARE MORE THAN 65 PERCENT IN LOW EXPENDITURE GROUP Anjarwati, Niken Alfina; Ubaidillah, Azka
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp927-936

Abstract

The right to get adequate food is a human right that must be fulfilled. Food insecurity is a problem that arises from not fulfilling food needs physically or economically. Food insecurity and poverty are interrelated. The United Nations prioritizes the elimination of poverty as the first goal and achieving food security as the second goal in the Sustainable Development Goals. In 2022, East Java had the highest percentage of households with a food expenditure share more than 65 percent in Java. The availability of data by expenditure group illustrates the economic status of households will assist the government in making targeted policies. However, the calculation of direct estimates at the regency level has not shown good precision, characterized by estimates with an RSE >25 percent. Therefore, this study aims to implement SAE HB Beta to improve the precision of the direct estimator. The result shows that SAE HB Beta produces a more precise estimation.
ESTIMATING EARTHQUAKE MAGNITUDE USING SPATIAL INTERPOLATION WITH THE INVERSE DISTANCE WEIGHTING AND ORDINARY KRIGING APPROACH Amalia, Fitri; Fauzan, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp937-948

Abstract

The West Java region is known for its high disaster vulnerability, particularly to earthquakes, due to the presence of many active faults. Based on this, information is needed as an initial step for disaster mitigation to reduce disaster risk and guarantee community safety in the West Java region, one of which is by carrying out spatial interpolation. In this study, the Inverse Distance Weighted (IDW) and Ordinary Kriging (OK) methods were used. The research sample included data on earthquake magnitude in West Java within the same coordinate range, from 01 January 2022 to 31 December 2022. This research was conducted to find out a more precise spatial interpolation method in estimating the strength of the earthquake in West Java in 2022. From the OK analysis results, the best theoretical semivariogram model was obtained, namely the Exponential model with nugget, sill and range values ​​of 0.07, 0.12 and 11451 meters. From the results of the IDW analysis, the best power value parameter was obtained, namely 2. This research was conducted to develop a more precise spatial interpolation method for estimating earthquake strength in West Java in 2022. The OK method results indicated that most of the West Java region has the potential for earthquakes with a magnitude of around 1.5 to 4.0, while the IDW method suggested a potential magnitude of around 2.0 to 4.0. The potential for a high-magnitude earthquake is in Kp. Cileuley, Garumukti, Pamulihan District, Garut Regency, West Java. Based on the results of Holdout Cross Validation, the IDW method is the best for estimating earthquake magnitude in West Java, with a MAPE value of 17.8%. The IDW method's estimation of earthquake magnitude is superior to OK, with smaller MAPE and MAE values.
TIME SERIES MODEL WITH LONG SHORT-TERM MEMORY EFFECT FOR GREENHOUSE GAS ESTIMATION IN INDONESIA Saputra, Ridho; Nisa, Alvi Khairin; Ramadhani, Nia; Almuhayar, Mawanda; Devianto, Dodi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp949-960

Abstract

Climate change is one of the major challenges in the world today, characterized by changes in meteorological values, such as rainfall and temperature, caused by the concentration of greenhouse gases in the atmosphere, such as CO2, N2O, and CH4. These accumulated greenhouse gases form a layer that prevents heat radiation from escaping, causing the greenhouse effect and global warming. Addressing the effects of greenhouse gas emissions requires appropriate strategies, one of which is to predict future greenhouse gas emissions for planning appropriate actions. Time series models such as the Autoregressive Integrated Moving Average (ARIMA) model are often used but have drawbacks due to their assumption of linear relationships. On the other hand, the Long Short-Term Memory (LSTM) model, introduced by Hochreiter and Schmidhuber in 1997, can learn complex and nonlinear relationships in data. This study uses LSTM to estimate greenhouse gas emissions in Indonesia based on emitting sectors, hoping to anticipate negative impacts and reduce greenhouse gas emissions. The results show that the LSTM model has good performance with an error below 20%, and it is predicted that greenhouse gas emissions will continue to increase.
APPLICATION OF DBSCAN FOR CLUSTERING SOCIETY BASED ON WASTE MANAGEMENT BEHAVIOR Al Jauhar, Hafizh Syihabuddin; Solimun, Solimun; Fitriani, Rahma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp961-972

Abstract

This research aims to answer the challenge of identifying the characteristics of the Batu City community in waste management, where traditional clustering techniques are often suboptimal due to the presence of noise or objects that do not fit the general pattern. As a solution, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied, which allows the clustering of objects based on local density and detects the presence of noise or outliers in the data. DBSCAN is considered more flexible than other clustering methods, especially in clustering data that is not linear or has a non-uniform distribution. This study successfully identified three clusters of waste management behavior with a silhouette index of 0.875, indicating good cluster quality. The first cluster consists of communities with good environmental quality, active participation in the use of waste banks, and a deep understanding of 3R-based waste management. The second cluster has adequate infrastructure quality and high awareness of the potential economic benefits of waste, while the third cluster displays a pretty good level of understanding of the 3Rs and relatively good environmental quality. The results of this study provide important insights into the differences in waste management characteristics between clusters, with environmental quality proving to be a significant factor in cluster formation.

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