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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

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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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HYBRID K MEANS-MULTIVARIATE ADAPTIVE REGRESSION SPLINES FOR DISTRIBUTION OF DENGUE FEVER RISK MAPPING IN BOJONEGORO DISTRICT Kartini, Alif Yuanita; Cahyani, Nita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Dengue Hemorrhagic Fever (DHF) is a dangerous disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes’ bites. WHO data shows that almost half of the world's humans are exposed to Dengue Hemorrhagic Fever. The number of mortality caused by dengue disease is around 20,000 every year. In East Java, Bojonegoro District has the highest number of dengue hemorrhagic fever cases (416). To reduce this number, the causative factors need to be known. Additionally, it's important to pinpoint the region or cluster where the variables driving the spread are located so that prevention and treatment efforts are effective. Based on the elements contributing to the transmission of Dengue Hemorrhagic Fever, this study seeks to identify and categorize locations at risk for the spread of the illness. This study uses Hybrid K Means-Multivariate Adaptive Regression Splines (MARS) which is a combination of K-Means and MARS methods in the hope of providing better analytical results. This is because the data was divided into simpler parts by considering the Oakley distance. The results obtained from the K Means-MARS hybrid shows the relationship between response variables and predictor variables for each cluster. There are three clusters of risk for the spread of dengue hemorrhagic fever in Bojonegoro district with categories: high risk cluster, medium risk cluster and low risk cluster. The high risk cluster consists of 7 sub-districts (Baureno, Kepohbaru, Balen, Sumberrejo, Kedungadem, Bojonegoro and Dander). The variables affecting the DHF Sufferer in the high risk cluster were population density (X2), Altitude (X3) and Health Worker (X6). Meanwhile, the medium risk cluster consists of 10 sub-districts (Kalitidu, Kanor, Kapas, Ngasem, Ngraho, Padangan, Sugihwaras, Sukosewu, Tambakrejo, and Trucuk). The variables that affect the DHF Sufferer in the medium cluster are Number of Dead (X1), Population Density (X2) and Health Facility (X5). The low risk cluster consisted of 11 sub-districts (Bubulan, Gayam, Gondang, Kasiman, Kedewan, Malo, Margomulyo, Ngambon, Purwosari, Sekar, and Temayang). The variables affecting the DHF Sufferer rate in the low risk cluster were number of dead (X1) and population density (X2).

LOGISTIC MODELING TO PREDICT THE INTEREST OF THE INDONESIAN PEOPLE FOR BUYING FLOOD IMPACTED INSURANCE PRODUCTS Hikmah, Yulial
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Indonesia is a country located on the equator and in the form of an archipelago. It has a high potential for various types of hydrometeorological-related disasters, such as floods, flash floods, droughts, extreme weather, etc. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. According to BNPB (2013), flooding is a disaster that relatively causes the most losses. Losses caused by floods, especially indirect losses, may rank first or second after an earthquake or tsunami. Floods cause so many losses, and it is necessary to have disaster mitigation efforts to minimize the possibility of flood risks. One risk mitigation due to natural disasters is buying insurance products. However, not everyone buys flood-impact insurance products due to economic and social factors. This study aims to create a model with the Logistics Regression Model to determine the factors influencing Indonesian people's interest in purchasing flood-impact insurance products. The research data is from 140 households in East Jakarta, Indonesia, using a non-probability purposive sampling technique. Furthermore, with a significance level of 10%, the logistic regression model obtained 14 significant regression coefficients. In the end, the obtained model is evaluated based on its level of accuracy. The results showed that the accuracy rate was almost excellent, namely 89.3%.

ANALYSIS OF THE MAGNETOHYDRODYNAMICS NANOVISCOUS FLUID BASED ON VOLUME FRACTION AND THERMOPHYSICAL PROPERTIES Norasia, Yolanda; Tafrikan, Mohamad; Kamaluddin, Bhamakerti Hafiz
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Fluid flow control is applied in engineering and industry using computational fluid dynamics. Based on density, fluids are divided into two parts, namely non-viscous fluids and viscous fluids. Nanofluid is a fluid that has non-viscous and viscous characteristics. Nanoviscos fluid flow is interesting to study by considering the effect of volume fraction and thermophysical properties. Nanoviscous fluid flow models form dimensional equations that are then simplified into dimensionless equations. Dimensionless equations are converted into non-similar equations using flow functions and non-similar variables. Nanoviscous fluids with Cu particles and water-based fluids have higher temperatures and faster velocity. Based on the effect of volume fraction, the velocity of the nanoviscous fluid moves slower, while the temperature of the nanoviscous fluid increases.

DYNAMIC ANALYSIS OF THE MATHEMATICAL MODEL FOR THE CHOLERA DISEASE SPREAD INVOLVING MEDICATION AND ENVIROMENTAL SANITATION Resmawan, R; Yahya, Lailany; Mahmud, Sri Lestari; Nuha, Agusyarif Rezka; Laita, Nazrilla Hasan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

This study aims to analyze the mathematical model of the cholera disease spread involving medicationnd environmental sanitation. The model was analyzed by determining the equilibrium point and the basic reproduction number. The next step was to analyze the equilibrium point, sensitivity, and simulate numerically. Analysis of the stability of the disease-free and endemic equilibrium points usedhe Routh-Hurwitz criteria and the Castillo-Chaves and Song Theorem. The Analysis resultf the model produced two equilibrium points; namely the disease-freequilibrium point for local asymptotic stability and the endemic equilibrium point for local asymptotic stability if . Furthermore, the sensitivity analysis indicated the most sensitive parameters for basic reproductive number changes in succession are the parameters for natural birth rates , the transmission rate of bacteria from the environment to humans , the saturated concentration of bacteria in water , an increase in the bacterial population caused by environmental pollution rate by humans . Numerical simulations suggest an increase to give vaccine can contribute to slowing the transmission of cholera where as the reduction of a vaccine able to promote the transmission of cholera diseases.

ANALYSIS OF SPATIAL EFFECTS ON FACTORS AFFECTING RICE PRODUCTION IN CENTRAL SULAWESI USING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION Gamayanti, Nurul Fiskia; Junaidi, Junaidi; Fadjryani, Fadjryani; Nur'eni, Nur'eni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Fulfillment of rice stock in Indonesia to always be distributed based on demand in the community is certainly closely related to the results of rice production. The results of rice production in various regions of Indonesia are very different. This difference can of course be influenced by geographic location or spatial effects between regions. Central Sulawesi, which is one of the provinces with a large population compared to other provinces on the island of sulwesi, has a responsibility to meet the needs of its community, so it is necessary to take into account and increase the production of rice by relying on production in the province.Modeling of rice production that has spatial effects or heterogeneity between regions is needed as an analytical tool because if the modeling ignores spatial effects and generalizes the model, the modeling predictions will be biased. So we need an analytical model that can accommodate the problem of spatial effects using Geographically Weighted Panel Regression. The purpose of this study was to determine the factors that can affect rice production in central sulawesi. The data used comes from BPS Central Sulawesi province from 2014-2020. This study focus to the spatial effect factors that are considered to be able to affect the rice production production in Central Sulawesi. Tthe results of the study there area 8 districts/cities which are affected by land area, and 4 districts/cities are affected by land area and harvested are.

EXPERT SYSTEM DESIGN TO DIAGNOSE PESTS AND DISEASES ON LOCAL RED ONION PALU USING BAYESIAN METHOD Junaidi, Junaidi; Fadjryani, Fadjryani; Setiawan, Iman; Batara, Mohammad; Hendra, Syaiful; Ismail, Nurmasita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Bayesian is a method that can be used to overcome the uncertainty of a situation or data. The information obtained must be continuously updated so that it can foster trust as a result of the uncertainty of those conditions. In this study, the application of the Bayesian method to detect early symptoms of diseases on local red onion Palu plants based on the symptoms that appear will be carried out. Information about pests and diseases that attack local red onion Palu is needed to help farmers. As a result, they can deal with attacked diseases quickly and precisely. This is crucial conducted by considering that this plant is one of the mainstay commodities for farmers in Central Sulawesi Province whose production must continue to be increased. Pests and diseases can be diagnosed through visible symptoms.The sample is local red onion Palu that affected by pests and disesases which planted in the AIAT of Central Sulawesi by experiment. As a result, through these symptoms an expert system can then be created to do a diagnosis. An expert system is a system that seeks to adopt human knowledge to a computer that is built to solve problems like an expert. The created expert system to diagnose diseases uses the Bayesian method to calculate the probability of an event occurring based on the obtained results from observations and experts. An expert system for diagnosis of pests and diseases is built on a web-based basis. This expert system has features and functions including the diagnosis of pests and diseases of the observed plants, viewing the results of the diagnosis and printing the results of the diagnosis. In addition, users can view information on pests and other diseases that attack plants. From the results of system testing that conducted by experts, this shows that the expert system is feasible to use to diagnose local red onion Palu plants which affected by pests and diseases with an accuracy point that has the largest percentage value.

EVALUATION OF THE QUEUE SYSTEM OF A DRIVING LICENSE APPLICATION AT KEPOLISIAN SUMBAWA BESAR Hermanto, Koko; Ruskartina, Eki; Adiasa, Iksan; Hudaningsih, Nurul; P, Gipari Royen; Harizahayu, Harizahayu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

A driving License (SIM) is a card that a motorized vehicle driver must own to prove the eligibility to drive a motorized vehicle. There are still many drivers who do not have a SIM. One of the reasons drivers don't apply for a driving license is because they object to follow every procedure in making a driver's license, which takes a long time and causes long queues every day. The application for a driver's license in a city or district is only in one place, namely at the City Police. As a result, many applicants apply for a driver's license in the queue system. This problem also occurs in the SIM-making service system at the Sumbawa Police Station. Therefore, this study aims to evaluate the queuing system for SIM-making services at the Sumbawa Besar Resort Police using the queuing theory method. There are two types of submissions in applying for a SIM, namely a new application and an application for an extension of a SIM, and the queuing system model used is Single Channel-Multi Phase. There are three phases in applying for a new SIM: the registration phase, photo phase, and test phase. Meanwhile, the application for a SIM extension has two steps: registration and photos. Based on the results of the analysis, the use of one service in each phase has not been optimal in overcoming the number of queues in the new SIM application process because there is still a steady-state (ρ) greater than one and the number of applicants is still queuing in the system, namely on Monday (phase test), Wednesday (test phase) and Friday (photo phase and test phase). Therefore, it is proposed to add one more service on that day and phase so that the stadium condition (ρ) is less than one and there is only one applicant in the queue (Lq).

ROBUST STOCHASTIC PRODUCTION FRONTIER TO ESTIMATE TECHNICAL EFFICIENCY OF RICE FARMING IN SULAWESI SELATAN Pranata, Ismail; Djuraidah, Anik; Aidi, Muhammad Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The stochastic production frontier (SPF) is the stochastic frontier analysis (SFA) method used to estimate the production frontier by accounting for the existence of inefficiency. The standard SPF assumes that the noise component follows a Normal distribution and the inefficiency component follows a half-Normal distribution. The presence of outliers in the data will affect the inaccuracy in estimating the parameters and leads to an exaggerated spread of efficiency predictions. This study uses two alternative models, the first with SPF Normal-Gamma and the second with SPF Student's t-half Normal, then the results are compared with standard SPF. This study uses data from statistics Indonesia on the cost structure of paddy cultivation household survey in 2014. This study aims to examine the effect of changes in distribution assumptions on the standard SPF model in estimating parameter value and the technical efficiency score in the presence of outliers. The parameter coefficient estimates similar results that apply to three SPF models. Only the standard error value in the alternative SPF model tends to be smaller than the standard SPF model. The Normal-Gamma model performs better in assessing residual with smaller root mean square error (RMSE) than the others, but the results of the estimated technical efficiency still contain outliers. The Student's t-half Normal model estimates technical efficiency no longer contains outliers, the range is shorter than the other models, and the results of estimating technical efficiency are not monotonous in the distribution of residual tails. The SPF Student's t-half Normal model is more robust in presence outliers than SPF Normal-half Normal and SPF Normal-Gamma.

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY Sahriman, Sitti; Yulianti, Andi Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

APPLICATION OF MAMDANI FUZZY METHOD TO PREDICT THE AMOUNT OF PINE RESIN PRODUCTION Fairus, Fairus; Putri, Sonia Anisa; Muliani, Fitra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

PT. Inhutani IV Aceh District needs to plan the right production in order to achieve maximum profit. Therefore, the company needs to develop a system that can predict the amount of pine resin that can be produced. This study uses data from PT. Inhutani IV Aceh District which is engaged in the production of raw pine resin. This study uses the mamdani fuzzy method to predict the amount of latex production based on demand data, supply data and latex production data per month in 2019-2020. Based on the results of calculations that have been carried out, with the input variable demand in January 2021 of 91,404 kg and supply in December 2020 of 71,466 kg, with the fuzzy mamdani method, the prediction results of the pine resin that the company can produce is 191,763 kg in January 2021 and based on the results of calculations using the MAPE accuracy measure, the fuzzy mamdani method has a MAPE value of 45.69 % so it can be concluded that the mamdani fuzzy method is pretty good for predicting the production of pine resin at PT. Inhutani IV Aceh District.

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Contact Name Yopi Andry Lesnussa, S.Si., M.Si

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Journal Mail Official barekeng.math@yahoo.com

Location Kota ambon, Maluku INDONESIA

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Yopi Andry Lesnussa, S.Si., M.Si

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yopi_a_lesnussa@yahoo.com

Phone
+6285243358669

Journal Mail Official
barekeng.math@yahoo.com

Location
Kota ambon,

Maluku

INDONESIA