BAREKENG: Jurnal Ilmu Matematika dan Terapan
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
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<![CDATA[CONSUMER PRICE INDEX MODELING USING A MIXED TRUNCATED SPLINE AND KERNEL SEMIPARAMETRIC REGRESSION APPROACH ]]>
<![CDATA[Hidayati, Lilik]]>;
<![CDATA[Hadijati, Mustika]]>;
<![CDATA[Purnamasari, Nur Asmita]]>;
<![CDATA[Ristiandi, Ristiandi]]>;
<![CDATA[Kartini, Ni Nyoman Dewi]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp581-594
<![CDATA[Some semiparametric regression model approaches include spline, kernel, Fourier series, and wavelet. Semiparametric regression modelling can involve more than one independent variable (multivariable), a parametric approach is usually combined with one of the nonparametric approaches, such as combining a parametric approach with a nonparametric kernel. If a consumer price index model can be built based on the variables that influence it, predictions of consumer price percentages can be made, which it is hoped will help the government determine policies to control consumer price inflation, especially in NTB Province. The data used in this research includes the consumer price index and the factors that influence it according to districts/cities in NTB Province from 2022 to April 2024. The data source was obtained from secondary data at BPS NTB Province. This research design uses a mixed semiparametric approach of truncated spline and kernel regression. Based on calculations, the predicted results of the consumer price index in NTB Province show that the predicted data graph is very close to the actual data . Modelling the consumer price index in NTB Province is a model with 2 knot points, where the model efficiency has the smallest GCV value of 0.001507. The model goodness value is 0.99, meaning that the variables used can explain 99% of the model variability.]]>
<![CDATA[APPLICATION OF CLASSIFICATION BASED ASSOCIATION (CBA) FOR MONKEYPOX DISEASE DETECTION ]]>
<![CDATA[Pratama, Qoria Yudi]]>;
<![CDATA[Irawan, Mohammad Isa]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp595-602
<![CDATA[Monkeypox is a zoonotic disease that can be transmitted from animals to humans. The monkeypox virus is the cause of monkeypox disease, which belongs to the orthopoxvirus family. Although the mortality rate from monkeypox is not as high as COVID-19, this virus can be the cause of the next global pandemic if the epidemic worsens. Therefore, it is very important to carry out proper surveillance and prevention to prevent the spread of this disease. In this study, researchers developed another method to detect monkeypox disease based on its symptoms using the classification by association (CBA) method. CBA integrates the advantages of classification and association analysis, allowing the classification process and a deeper understanding of the strength of the relationship between features in the dataset through the analysis of metrics such as support and confidence. Based on the results of the experiments in this study, an accuracy of 68.64%, a precision of 92.21%, and a sensitivity of 71.09% were obtained. In this case, the accuracy obtained is still low, but the results of other metrics show that the CBA model performs fairly well in predicting the positive class with high precision.]]>
<![CDATA[CONSTRUCTION OF BLUE ECONOMY DEVELOPMENT INDEX AT THE PROVINCIAL LEVEL IN INDONESIA USING EXPLORATORY FACTOR ANALYSIS ]]>
<![CDATA[Sari, Dewi Novita]]>;
<![CDATA[Oktora, Siskarossa Ika]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp603-616
<![CDATA[Indonesia, as an archipelagic country, holds marine resources of significant economic value in improving the welfare of its people. However, the community's use of marine resources does not pay attention to sustainability. The government then uses the Blue Economy concept to maximize the economic value obtained while maintaining the sustainability of the marine ecosystem through national policies and plans. In realizing blue economy development, enabler factors, such as technology and government governance, have an important role. This research aims to construct a Blue Economy Development Index (IPEB) at the provincial level in Indonesia in 2021, including enabler factors for blue economy development. The analytical method used is Exploratory Factor Analysis. The results show that the distribution of the minimum values for the indicators that make up the IPEB is found in the provinces of the Eastern Region of Indonesia. In contrast, the distribution of the maximum values of the indicators is found in the provinces of the Western Region of Indonesia. The province with the highest IPEB score is South Sulawesi, while the lowest is Central Sulawesi. The limitation of this study is the data derived from the Village Potential Survey (Potensi Desa) data collection, so several variables are not yet available in annual time. The results of this study are important in improving the ability to monitor implementation and assist in decision-making in increasing blue economic development, especially at the provincial level.]]>
<![CDATA[GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION AND VALUE AT RISK ANALYSIS OF PT BANK NEGARA INDONESIA STOCKS ]]>
<![CDATA[Zakiah, Ainun]]>;
<![CDATA[Sulistianingsih, Evy]]>;
<![CDATA[Satyahadewi, Neva]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp617-628
<![CDATA[Investments in stocks are made to make a profit, where the higher the expected profit, the greater the risk undertaken. The return on investing in stocks can be influenced by changes in the price of stocks that are difficult to predict, which can lead to uncertainty in the value of the return and the risk of the stock. The application of the Geometric Brownian Motion (GBM) model with Jump Diffusion is crucial for enhancing the accuracy of stock price forecasting and risk analysis by incorporating price jumps resulting from external events within complex market dynamics. The data used in this study are the closing price data of the daily stock of PT Bank Negara Indonesia for the period 1 December 2022 to 31 January 2024, where the stock return data has a kurtosis value greater than 3 (leptokurtic) so that the data indicates a jump. The GBM with Jump Diffusion model was implemented to predict the stock price with a simulation repetition of 1000 times. The analysis shows that the GBM model with Jump Diffusion has an excellent accuracy rate with the smallest MAPE value of 0.86%. The average value of the VaR with Monte Carlo simulation obtained at the reliability levels of 80%, 90%, 95%, and 99% in a row is 0.96%, 1.53, 1.97%, and 2.64%. This result shows that the higher the confidence level used, the greater the risk.]]>
<![CDATA[COMPARISON OF SURVIVAL ANALYSIS USING ACCELERATED FAILURE TIME MODEL AND COX MODEL FOR RECIDIVIST CASE ]]>
<![CDATA[Arfan, Nuraziza]]>;
<![CDATA[Irfanullah, Asrul]]>;
<![CDATA[Hamidi, Muhammad Rozzaq]]>;
<![CDATA[Mukhaiyar, Utriweni]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp629-642
<![CDATA[Recidivists, or ex-prisoners who commit crimes after serving a prior sentence, pose a critical challenge to the criminal justice system. This study examines social and economic factors that may reduce the likelihood of recidivists being re-arrested. Using survival analysis, the probability that a recidivist could survive in society without being re-arrested could be estimated. The purpose of this work is to compare the AFT and Cox models to determine which provides a better fit to identify factors affecting the likelihood of re-arrest within one year after release and to statistically assess the impact of these factors. This study utilizes a stratified Cox model to address variables that violate the proportional hazards (PH) assumption. The analysis is limited to four types of AFT models: Weibull, log-normal, log-logistic, and exponential. Results show that the stratified Cox model provides the best fit, based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). This demonstrates the Cox model's robustness in analyzing survival data, accurately approximating the distribution of survival times without restrictive assumptions, unlike AFT models. The study reveals that recidivists who received financial aid upon release have a lower risk of re-arrest compared to those who did not, and each additional prior theft arrest increased the risk of re-arrest by 1.09193 times.]]>
<![CDATA[3D MODELING COMPUTATION TO EVALUATE GROYNE STRUCTURE PERFORMANCE: CASE STUDY OF PASSO COASTAL AREA ]]>
<![CDATA[Salamena, Ganisa Elsina]]>;
<![CDATA[Salamena, Gianita Anastasia]]>;
<![CDATA[Loupatty, Grace]]>;
<![CDATA[Palembang, Citra Fathia]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp643-654
<![CDATA[Groyne is very important to protect the coastline with the concept of maintaining the balance of sediment transport. Groyne building in theory can work well if worked in groups or more than one. In this study, the Passo beach location was chosen because there is an existing groyne building that, if seen on Google Earth, has been damaged by the scattering of the constituent rocks. If the groyne cannot work to balance the sediment transport, it may occur that mass destruction to the infrastructure behind the groyne itself, such as regional roads, may occur. To find out the level of damage, an in-depth study needs to be carried out. In this paper, Delft-3D mathematical modeling was carried out to investigate groyne damage by looking at the performance of groyne in maintaining the balance of sediment transport in the Passo beach area. Hydrodynamic and coastal sediment modeling analyses were carried out in wet and dry season conditions. Modeling was carried out over one month with a morphology factor of 12 to obtain sediment transport for one year. In the existing dry season conditions, it shows that at the observation point, there is erosion of 2 meters, and in the wet season sediment transport is balanced. It is implied that the groyne structure must be replaced for being surpass the structure lifetime.]]>
<![CDATA[COMPARATIVE ANALYSIS OF TWO-STEP AND QUASI MAXIMUM LIKELIHOOD ESTIMATION IN THE DYNAMIC FACTOR MODEL FOR NOWCASTING GDP GROWTH IN INDONESIA ]]>
<![CDATA[Souisa, Gilbert Alvaro]]>;
<![CDATA[Leiwakabessy, Reyner M.]]>;
<![CDATA[Damayanti, Salma]]>;
<![CDATA[Terim, Mohammad Zanuar F]]>;
<![CDATA[Pelu, Shelma M]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp655-664
<![CDATA[Economic activity data is needed quickly to make policy decisions, but this data suffers from publication delays. Gross Domestic Product (GDP) data is released within five weeks after the end of the quarter. An effort that can be made to provide such data is through nowcasting, which is forecasting in the current period using variables that have a higher frequency. This study aims at nowcasting GDP growth. The nowcasting method used is the Dynamic Factor Model (DFM) with Two Step (TS) and Quasi Maximum Likelihood (QML) estimation. The nowcasting results show that the DFM-TS model is better than the DFM-QML because it has a larger adjusted R-squared value and has the smallest RMSE value of 1.71035 compared to the DFM-QML value, which has an RMSE value of 1.71598.]]>
<![CDATA[IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH ]]>
<![CDATA[Sumin, Sumin]]>;
<![CDATA[Prihantono, Prihantono]]>;
<![CDATA[Khairawati, Khairawati]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp665-674
<![CDATA[Manual classification of Single Tuition Fees (STF) has a high risk of misclassification due to the need for a more in-depth assessment of students' economic criteria. This research uses Artificial Neural Networks (ANN), specifically the Multilayer Perceptron (NN-MLP) model, to detect and correct errors in Single Tuition Fee (STF) classification. This study aims to apply the NN model to identify and correct classification errors in the STF clustering of State Islamic Religious Universities in Indonesia (PTKIN). This research was conducted using exploratory methods and quantitative approaches involving a population of PTKIN students throughout Indonesia. A sample of 282 respondents was selected using a simple random sampling method. The results showed that NN-MLP is an effective tool for identifying and correcting misclassification in determining PTKIN tuition fees with significantly improved classification accuracy characterized by an accuracy value of 71.28% and MSE of 0.287; this model can be used as a basis for developing information systems that are fairer and more accurate in managing tuition fees in higher education. This research also proves that the NN method is superior to traditional statistical methods and simple machine learning in handling complex and diverse data. In addition, the Random Forest model successfully identified the most influential input variables in STF classification. Father's occupation, mother's occupation, number of dependents, and utility bills such as water and electricity significantly contributed to the STF classification. In contrast, variables such as vehicle facilities showed a lower contribution.]]>
<![CDATA[IMPLEMENTATION OF FEATURE IMPORTANCE XGBOOST ALGORITHM TO DETERMINE THE ACTIVE COMPOUNDS OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) ]]>
<![CDATA[Kusnaeni, Kusnaeni]]>;
<![CDATA[Adhalia, Nurul Fuady]]>;
<![CDATA[Zulfattah, Abdul Khaliq]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp675-686
<![CDATA[Sembung is a medicinal plant native to Indonesia that grows optimally in tropical climates. The secondary metabolite compounds found in the leaves of sembung are biopharmaceutical active ingredients. Fourier Transform Infrared (FTIR) spectroscopy can identify the functional compounds in sembung leaves by analyzing unique peaks in the spectrum, which correspond to specific functional groups of the compounds. In this research, 35 observations were made with 1,866 explanatory variables (wavelengths). Data in which the number of explanatory variables surpasses the number of observations is known as high-dimensional data. One method that can handle high-dimensional problems is to select important variables that affect the objective variable. The XGBoost algorithm can calculate the feature importance score that affects the goal variable so that it does not have to include all variables in the modeling, this can overcome problems in high-dimensional data. The results of the calculation of feature importance found Lignin Skeletal Band, CH, and CH2 aliphatic Stretching Group, C=C, C=N, C–H in ring structure, DNA and RNA backbones, NH2 Aminoacidic Group, C=O Ester Fatty Acid that the active compounds contained in the leaves of sembung.]]>
<![CDATA[MODELING CUSTOMER LIFETIME VALUE WITH MARKOV CHAIN IN THE INSURANCE INDUSTRY ]]>
<![CDATA[Mahdiyasa, Adilan Widyawan]]>;
<![CDATA[Pasaribu, Udjianna Sekteria]]>;
<![CDATA[Sari, Kurnia Novita]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp687-696
<![CDATA[In the competitive insurance industry, accurately predicting Customer Lifetime Value (CLV) is vital for sustaining long-term profitability and optimizing resource allocation. Traditional static models often fail to capture the dynamic and uncertain nature of customer behavior, which is influenced by factors such as life changes, economic conditions, and evolving product offerings. To address these limitations, this paper proposes an advanced modeling approach that integrates Markov Chains with survival analysis. Markov Chains are well-suited for modeling stochastic processes, where future states depend on current conditions, while survival analysis provides insights into event timing and likelihood for estimating the insurance premium. The proposed model combines these approaches to make a more complete and accurate prediction of CLV. This helps insurers make better decisions and improves the overall performance of their business. We employ the data of customer behavior from the insurance company in Bandung, Indonesia from 1994 to 2020. We found that CLV in the insurance industry is significantly affected by customer behavior.]]>
