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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
 102   103   104   105   106 
OVERCOMING OVERFITTING IN MONKEY VOCALIZATION CLASSIFICATION: USING LSTM AND LOGISTIC REGRESSION Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Goni, Abdiel Wilyar
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/barekengvol19iss2pp973-986

Abstract

The problem of overfitting in a classification task involving animal vocalizations, namely squirrel monkeys, golden lion tamarins, and tailed macaques, is handled in this project. Acoustic features extracted for the audio data used in this research are MFCCs. The classification of subjects was done using the LSTM model. However, several architectures with LSTM also presented the problem of overfitting. To overcome this, a logistic regression model was used, which had a classification accuracy of 100%. These results indicate that for such a classification problem, logistic regression may be more appropriate than the complex architecture of LSTMs. Several LSTM architectures have been presented in this study to give an overall review of the observed challenges. Although the capability of LSTM in handling sequential data is very promising, sometimes simpler models might be preferred, as indicated by the results. This is a single-dataset work, and the findings may not generalize well to other domains. The work contributes much-needed insight into the choice of models for audio classification tasks and identifies the trade-off between model complexity and performance
THE COMPARISON OF EXTENDED AND ENSEMBLE KALMAN FILTERS IN MODELING ENVIRONMENTAL POLLUTION INFLUENCES ON ACUTE RESPIRATORY INFECTION DYNAMICS (ISPA) Norasia, Yolanda; Oktaviani, Dinni Rahma; Putri, Devi Marita
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/barekengvol19iss2pp987-998

Abstract

Acute Respiratory Infections (ISPA) are a significant health issue. According to the World Health Organization (WHO), ISPA is the leading cause of death among children under five worldwide. ISPA can be caused by environments with high levels of air pollution, particularly in urban areas. Predicting the spread of ISPA is a crucial step in controlling the disease. Since pollution sources are diverse, modeling and prediction can be difficult, which makes advanced methods such as the Kalman Filter (KF) desirable. This study compares two estimation methods, the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF), in predicting the spread of ISPA triggered by environmental pollution. Simulation results show that both methods can produce accurate estimations, but EnKF demonstrates superior performance in terms of RMSE compared to EKF. It predicts more accurately for susceptible (X) and infected (Y) populations with EnKF than with EKF. Based on the results of the EnKF for the X and Y populations, the RMSEs are 0.0660 and 0.1114, respectively. EnKF's advantage in handling uncertainty and non-linearity in the model makes it suitable for predicting the spread of ISPA.
SUSCEPTIBLE VACCINATED INFECTED RECOVERED MODEL WITH THE EXCLUSIVE BREASTFEEDING AND ITS APPLICATION TO PNEUMONIA DATA IN INDONESIA Widyaningsih, Purnami; Musta'in, Ghufron; Saputro, Dewi Retno Sari
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/barekengvol19iss2pp999-1008

Abstract

The spread of infectious diseases can occur directly or indirectly. Pneumonia is an infectious respiratory tract disease. Indonesia is among the top 10 countries in the world concerning deaths caused by pneumonia. The spread of infectious diseases can be prevented through vaccination and exclusive breastfeeding, which play a role in providing body immunity. This study aims to formulate an SVIR model with exclusive breastfeeding, apply it to pneumonia in Indonesia, and determine its spread pattern and interpretation regarding the target of free pneumonia by 2030. The methods used were literature and applied studies. Through literature studies, the characteristics of infectious diseases were identified, assumptions and parameters of the model were added, and relationships between variables were determined. The applied method was to estimate the parameters and initial values of the model based on annual data on pneumonia disease in Indonesia. The formulated model is a system of first-order nonlinear differential equations. The model is applied to pneumonia based on annual data from 2013 to 2022 in Indonesia, and its solution is determined using the fourth-order Runge-Kutta method. Based on the model solution and 2021-2022 data, a MAPE value of 15% is obtained, indicating that the model is sufficiently accurate in explaining the spread of pneumonia in Indonesia. The spread pattern of pneumonia in Indonesia from 2013 to 2030 indicates a downward. However, as of 2030, there are still 67,261 individuals infected, indicating that the target of pneumonia-free Indonesia has not been achieved. Simulation shows that with exclusive breastfeeding rate value = 0.438852 and Hib vaccination rate = 0.25 it is estimated that the target of free pneumonia in Indonesia in 2030 will be achieved. The free target can also be achieved by increasing the exclusive breastfeeding rate to 73.9% and the Hib vaccination rate to 0.22.
COMPARISON BETWEEN BICLUSTERING AND CLUSTER-BIPLOT RESULTS OF REGENCIES/CITIES IN JAVA BASED ON PEOPLE’S WELFARE INDICATORS Widyaningsih, Yekti; Nisa, Alfia Choirun
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/barekengvol19iss2pp1009-1022

Abstract

The success of a country's development can be known from the well-being of its people. Improving the welfare of the population is the main goal of the development activities carried out by the government. To ensure that development is effective and targeted, grouping is needed to understand the characteristics of the region. This study discusses the grouping of regencies/cities in Java Island based on the people's welfare indicators in 2022. The measured welfare is material well-being. Variables used in this study are the percentage of the poor population, GDP per capita at current prices, average length of schooling, expected length of schooling, percentage of per capita expenditure on food, open unemployment rate, population, population density, and life expectancy. There are two approaches used in grouping regencies/cities along with their variables. The first approach is to simultaneously group regencies/cities and their variables using Plaid Model biclustering. The second approach is to group regencies/cities using the Ward clustering method followed by the biplot method. This study aims to compare the results of these two approaches, namely the biclustering and cluster-biplot results, on data from 119 regencies/cities in Java Island in 2022 based on people's welfare indicators. Based on the results of this study, the number of groups from each approach is 2, with group 1 being more prosperous than group 2. Judging from the standard deviation values, the Plaid Model biclustering result groups have lower standard deviation values than the cluster-biplot result groups. Therefore, in general the first approach produces better groups as they are more homogeneous than the second approach.
FORECASTING THE NUMBER OF SEARCH AND RESCUE OPERATIONS FOR SHIP ACCIDENTS IN INDONESIA USING FOURIER SERIES ANALYSIS (FSA) Recylia, Rien; Saifudin, Toha; Chamidah, Nur
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/barekengvol19iss2pp1023-1036

Abstract

As an archipelago country, Indonesia is a national and international route. This position makes high ship mobility which also increases the risk of ship accidents. To address this issue, based on these conditions, a prediction is required to forecast ship accidents in Indonesia for the upcoming period using an effective method. Through data forecasting, we can map the readiness of Basarnas resources in conducting search and rescue operations for ship accidents. Forecasting data for search and rescue operations in ship accidents is important because it can predict the quantity of needed search and rescue operations. These can be effective measures to reduce casualties in accidents of this type. This research uses the Fourier Series Analysis (FSA) method, which doesn’t require parametric assumption. Additionally, the FSA method can be used for data with unknown patterns. The data used is divided into training data and testing data. The training data used in this research is the number of search and rescue operations from January 2021 to December 2022, while the testing data is from January 2023 to December 2023. The analysis results of this study indicate that forecasting using the FSA method has a MAPE of 25.758%, which falls into the category of reasonable forecasting accuracy and with an optimal and a GCV of 166.586. The results of future predictions are in the form of a mathematical model that can be used by entering the time variable that you want to predict. The anticipated benefits of this research are to contribute to Basarnas’s planning and execution of search and rescue operations for shipwrecks, enrich academic literature on forecasting methodologies, and enhance public awareness of search and rescue operations in Indonesia
SENTIMENT ANALYSIS OF REVIEWS ON X APPS ON GOOGLE PLAY STORE USING SUPPORT VECTOR MACHINE AND N-GRAM FEATURE SELECTION Kusumo, Fahri Aimar; Saputro, Dewi Retno Sari; Widyaningsih, Purnami
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/barekengvol19iss2pp1037-1046

Abstract

Sentiment analysis is an application of text mining that is used to find out opinions from a set of textual data about a particular event or topic. The main function of sentiment analysis is to extract information and find the meaning and opinions of a given user. Sentiment analysis requires classification algorithms, such as Support Vector Machine (SVM). SVM is a frequently used algorithm for text data classification because it can handle high-dimensional data. The concept of SVM is to determine the best hyperplane that serves as a separator of two classes in the input space. Text data with a large number of features causes data imbalance and affects the classification process so it is necessary to do feature selection. Feature selection is a technique used to reduce irrelevant attributes in the dataset. N-gram feature selection is a statistics-based approach to classifying text. N-grams are able to classify unknown text with the highest certainty. The characteristics of N-grams in sentiment analysis are that they function well despite textual errors, run efficiently, require simple storage, and fast processing time. This research aims to perform sentiment analysis on application reviews on the Google Play Store with SVM and unigram, bigram, and trigram feature selection. The methodology of this research includes conducting theoretical studies, web scraping, text preprocessing, labeling sentiments with VADER, weighting with TF-IDF, dividing data into training data (80%) and testing data (20%), training and evaluating models, classifying testing data, and interpreting results. Based on the research results, 3151 testing data were classified. SVM classification and unigram feature selection have the highest accuracy value of 90% and AUC of 0.93 (excellent). SVM classification and bigram feature selection have an accuracy value of 78% with an AUC value of 0.81 (good). SVM classification and trigram feature selection had the lowest accuracy value of 68% with an AUC value of 0.66 (poor).
GAME THEORY AND MARKOV CHAIN ANALYSIS OF THE DISPLACEMENT OF SHOPPING MALL VISITORS IN SURAKARTA CITY Rizkita, Nabiella Zahra; Sutanto, Sutanto; Kurdhi, Nughthoh Arfawi
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/barekengvol19iss2pp1047-1056

Abstract

The era of globalization has led to changes in social patterns and lifestyles. With these changes, shopping malls were built to fulfill the community’s needs. This study aims to analyze the displacement of visitors in three shopping malls in Surakarta City, namely Solo Paragon Mall, Solo Grand Mall, and Solo Square, using game theory and the Markov chain. Game theory is used to determine the optimal strategy of each shopping mall based on six indicators of visitor satisfaction, namely product diversity, presence of transportation modes, distance, price, facilities, and services. Saddle points are obtained by pure strategy. The calculation results with the game theory method resulted in three competitions. The first competition between Solo Paragon Mall and Solo Grand Mall obtained the optimal strategy of Solo Paragon Mall is product diversity. At the same time, Solo Grand Mall is the existence of transportation modes. The second competition between Solo Paragon Mall and Solo Square obtained the optimal strategy of Solo Paragon Mall, which is product diversity, while Solo Square's optimal strategy is service. Lastly, the third competition, Solo Grand Mall and Solo Square, obtained the optimal strategy of Solo Grand Mall is the presence of transportation modes and Solo Square is service. Markov chain is used to calculate the transition probability of visitors and steady state, which shows that Solo Paragon Mall is more desirable with a steady state probability of 0.4459, followed by Solo Square 0.3584 and Solo Grand Mall 0.1957. The results of this study can help shopping malls evaluate and improve their strategies to increase loyalty and attract new visitors.
INTEGRATION OF HIERARCHICAL CLUSTER, SELF-ORGANIZING MAPS, AND ENSEMBLE CLUSTER WITH NAÏVE BAYES CLASSIFIER FOR GROUPING CABBAGE PRODUCTION IN INDONESIA Maghfiro, Maulidya; Wardhani, Ni Wayan Surya; Iriany, Atiek
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/barekengvol19iss2pp1057-1070

Abstract

The purpose of this study is to evaluate and compare different clustering techniques, including hierarchical cluster analysis (using complete linkage, average linkage, and single linkage methods), Self-Organizing Maps (SOM) clustering, and ensemble clustering, within the framework of integrated cluster analysis combined with Naïve Bayes analysis, specifically applied to cabbage production in Indonesia. The data utilized in this study are on cabbage production from various districts and cities in Indonesia, obtained from the 2023 publications of the Central Statistics Agency (BPS). The variables used in this study are cabbage harvest, cabbage production, area height, and rainfall. The data size used is 157 districts/cities in Indonesia. This research is a quantitative analysis employing integrated cluster analysis combined with Naïve Bayes. Cluster analysis is used to obtain classes in each district/city. Different clustering methods, including hierarchical clustering, Self-Organizing Map (SOM), and ensemble clustering, are compared to determine the best approach for grouping districts based on cabbage production. Naïve Bayes analysis is then used to classify cabbage production in Indonesia and identify the optimal clusters. This comparison aims to find the most effective clustering method for improving grouping accuracy and understanding cabbage production patterns. The best method for classifying cabbage production in Indonesia is the ensemble clustering approach integrated with Naïve Bayes, resulting in three distinct clusters: high, medium, and low production clusters.
ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL Nugraha, Edwin Setiawan; Olivia, Agna; Sudding, Fauziah Nur Fahirah; Lestari, Karunia Eka
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/barekengvol19iss2pp1071-1082

Abstract

Investment plays a crucial role in supporting economic development by allocating funds to generate future profits. Among various investment options, stock investment is widely popular. However, investors face the challenge of developing strategies to maximize returns while minimizing risks. Effective investment requires understanding the potential maximum risk of loss, known as Value at Risk (VaR). This research focuses on estimating VaR for four top general insurance companies in Indonesia: PT Lippo General Insurance Tbk (LPGI), PT Asuransi Tugu Pratama Indonesia Tbk (TUGU), PT Victoria Insurance Tbk (VINS), and PT Asuransi Dayin Mitra Tbk (ASDM). These companies were selected due to their leading positions in the industry. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, an extension of the ARIMA method designed to handle volatility clustering, is utilized for VaR estimation. Results at confidence levels of 90%, 95%, and 99% reveal that VINS carries the highest risk, with a maximum VaR of IDR 2,848,710 at 99% confidence, while LPGI shows the lowest risk, with a maximum VaR of IDR 22,677. For TUGU, the maximum possible loss is IDR 517,589, and for ASDM, it is IDR 1,532,267. Backtesting confirms the reliability of the models, with some accepted at specific significance levels. Based on this analysis, the results can help investors make investment decisions that minimize potential losses, specifically in the four stocks analyzed.
DETERMINANTS OF INDONESIA’S CINNAMON EXPORT VOLUME TO THE UNITED STATES: AN ERROR CORRECTION MODEL APPROACH Siahaan, Elga Winner Mombun; Yuliana, Lia
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/barekengvol19iss2pp1083-1092

Abstract

Cinnamon is one of Indonesia's leading export spice commodities. The United States (US) is the strongest importer country of Indonesian cinnamon. However, since 2013 the volume of Indonesian cinnamon exports to the US has decreased. If this decline continues, it could shift Indonesia's position in the cinnamon export market. This research aims to provide an overview of and analyze the influence of export prices, GDP, production, and exchange rate on Indonesia’s cinnamon exports to the US from 1990 to 2022. The data used are from the Food and Agriculture Organization (FAO), World Bank, and Organization for Economic Co-operation and Development (OECD). This research uses descriptive analysis with graphical analysis and inference analysis with the Error Correction Model (ECM). The results showed that in the long term, decreasing export prices can increase demand for cinnamon exports from the US. In the short term, large production that does not meet the quality standards can reduce cinnamon exports. The increase in US people's income and the strengthening of Rupiah can increase the volume of Indonesian cinnamon exports to the US in both the long and short term.

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