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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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OUTLIERS HANDLING ON SEASONAL ARIMA INTERVENTION MODEL (CASE: IMPACT OF MOST FAVORED NATION POLICY ON INDONESIAN HOT ROLLED COIL/PLATE) Maksum, Fadhila Annisa; Oktora, Siskarossa Ika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0603-0614

Abstract

Intervention analysis measures the impact of various external events or interventions capable of changing data patterns. This research aims to determine the outliers handling on the seasonal ARIMA intervention model using the Box-Jenkins method. The pre-intervention model formed contains seasonal and step functions, which does not fulfill the white noise of the final intervention model. Therefore, the outliers need to be detected the model meets the white noise assumption. The intervention model and outlier detection in this study are conducted to capture the impact of a tariff-setting policy of 5 and 15 percent, called the first and second intervention, on the volume of Hot Rolled Coil/Plate (HRC/P) imports. When the outlier is detected, the next step is to examine and adjust its effect on the model by adding the effect of the outlier in the model. Using the seasonal ARIMA intervention model, the results showed that the first and second interventions significantly reduced the volume of HRC/P imports. A limitation of this research is that this model cannot include other independent variables in the modeling.
MODELING FACTORS AFFECTING EDUCATED UNEMPLOYMENT ON JAVA ISLAND USING GEOGRAPHICALLY WEIGHTED POISSON REGRESSION MODEL Wicaksono, Ditto Satrio; Nuriyah, Sinta; Fajritia, Rahajeng; Yuniarti, Ni Putu Nita; Priatmadani, Priatmadani; Amelia, Laeli; Berliana, Sarni Maniar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0615-0626

Abstract

The eighth goal of the SDGs, which aim to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all, addresses the problem of unemployment. Indonesia, the fourth-largest country in the world, keeps on dealing with unemployment and its negative consequences. Three provinces on the island of Java have higher unemployment rates for educated people than any other provinces. The purpose of this study is to examine the variables affecting educated unemployment in Java. This study uses cross-sectional data published from BPS-Statistics Indonesia website and the Indonesia Investment Coordinating Board (BKPM) for 119 regencies/cities across six provinces on Java Island in 2021. The predictor variables are Gross Regional Domestic Product (GRDP), investment, labor force participation rate, mean years of schooling, regency/city minimum wage, and inflation. The number of working-age population is used as an exposure measure. Four models were used to analyze the factors affecting educated unemployment on Java Island: Poisson regression model and Geographically Weighted Poisson Regression (GWPR) model, both with and without an exposure. Based on the smallest AIC/AICc, the best model is the GWPR model with an exposure. This model creates 11 groups of locations based on the same predictor variables that significantly affect educated unemployment
A BI-OBJECTIVE COST MINIMIZATION MODEL FOR THE INSULAR TOUR ROUTE PLANNING PROBLEM Afifudin, Mohammad Thezar; Muspida, Muspida; Sahar, Dian Pratiwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0437-0448

Abstract

This article presents a study on the development of a bi-objective cost problem optimization model in planning tourist routes in the island zone. This problem is a new variant of the tour route plan problem. Bi-objective view of two cost components, namely maritime transportation costs and ground transportation costs. Two models were formulated using a mixed integer linear programming approach. The first model was designed to minimize one of the two cost components separately. The second model was bi-objective cost minimization based on the priority weights of the two costs. It was designed to determine minimum transportation costs based on priority weights. Model testing was carried out through numerical experiments on several cases that often occur in industries in Maluku, Indonesia, especially tourism and goods shipping. Each case has variations in the number of islands and nodes. As a result, the model can demonstrate its adaptability to changes in objectives and parameters. For cases that do not have a single solution, an increase in the network structure on the number of islands and nodes will increase the variety of efficient alternative solutions. The set of efficient solutions also shows an inverse relationship between MTC and GTC. The results also show that MTC minimization cannot be used as a reference for TC minimization in cases with many nodes and islands. Efforts to minimize MTC in the island zone impact reducing total costs but do not mean minimizing total costs. In addition, based on the exponential trend line of computing time, the number of nodes has a more significant influence on computing time compared to the number of islands.
THE SAMPLE SCHEDULING APPLICATION OF THE ANT COLONY OPTIMIZATION ALGORITHM IN VEHICLE ROUTING PROBLEM TO FIND THE SHORTEST ROUTE Mahfudhotin, Mahfudhotin; Palupi, Ratnaning
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0643-0656

Abstract

In this collaborative research initiative with the Research Center and Industry Standardization in Surabaya, the primary objective is to obtain Indonesian National Standard certificates through the collection of samples from companies in East Java. The focal challenge revolves around the optimization of delivery routes for vehicles with specific capacities, constituting the Vehicle Routing Problem (VRP), and is addressed through the application of the Ant Colony Optimization (ACO) algorithm. The study confronts constraints, including the limitation of time and resources during the sample collection process, and grapples with challenges associated with travel distances that impact overall efficiency. The utilization of Sample Scheduling software (Si Dull) developed in Visual Basic introduces configurational constraints for route planning with the aim of minimizing distances. The overarching aim is to implement the ACO algorithm, culminating in the development of the Si Dull application, to elevate the efficacy of the sample collection process for industrial certification in Surabaya, thereby contributing to enhanced efficiency in travel distances for sample collection endeavors
RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD Iriany, Atiek; Ngabu, Wigbertus; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0627-0636

Abstract

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.
LEADERS AND FOLLOWERS ALGORITHM FOR TRAVELING SALESMAN PROBLEM Angmalisang, Helen Yuliana; Anam, Syaiful
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0449-0456

Abstract

Leaders and Followers algorithm is a metaheuristics algorithm. In solving continuous optimization, this algorithm is proved to be better than other well-known algorithms, such as Genetic Algorithm and Particle Swarm Optimization. This paper aims to apply the Leaders and Followers algorithm for the Traveling Salesman Problem (TSP), a well-known combinatorial optimization problem to minimize distance. There are some modifications in order to fit the algorithm in TSP problems. Some most-used-problems in TSP are used to test this algorithm. The result is that the Leaders and Followers algorithm performs well, stable, and guarantees the optimality of the obtained solution in TSP with fewer than 20 cities. In TSP with a bigger number of cities, the proposed algorithm is not stable and might has difficulties in finding the optimal solutions.
RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK Permata, Regita Putri; Muhaimin, Amri; Hidayati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0457-0466

Abstract

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.
SPREADING PATTERN OF INFECTIOUS DISEASES: SUSCEPTIBLE INFECTED RECOVERED MODEL WITH VACCINATION AND DRUG-RESISTANT CASES (APPLICATION ON TB DATA IN INDONESIA) Widyaningsih, Purnami; Yumaroh, Siti Roqhilu; Saputro, Dewi Retno Sari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0467-0474

Abstract

Mycobacterium tuberculosis is the causative agent of the infectious illness tuberculosis (TB). Indonesia is the world's third-highest TB burden country. TB transmission is prevented by the BCG vaccination. A directly observed treatment, short-course (DOTS) treatment approach can cure TB illness. Recurrent TB may occur due to either relapse or reinfection with drug-resistant bacteria. The goals of this article are formulating the SVITR model with relapse and drug-resistant cases, applying the model to the TB data in Indonesia, determining the model accuracy, determining the spreading pattern and interpreting the result, and simulating the parameters. Literature study and application methods are used in this research. The SVITR model with relapse and drug-resistant cases is a first-order nonlinear differential equation system. The model is applied to TB in Indonesia based on annual data from Indonesian Health Profile, World Bank, and WHO. The model is solved by the fourth-order Runge-Kutta method. The model is accurate enough to explain the spread of TB in Indonesia with a MAPE value of 15,5%. The spreading pattern of tuberculosis infection is upward from 2010 to 2050. In 2050, there are still 8.115.976 TB cases in Indonesia. Hence in 2050, Indonesia's free of TB target has yet to be achieved. Simulation is conducted by increasing BCG vaccination to 95%, reducing contact with TB patients to 5%, increasing treatment to 95%, and lowering relapses and drug-resistant cases to 0.00005%, so the Indonesia free of TB target in 2050 can be achieved from 2042.
ETHNOMATHEMATICS OF SMALL BORDER ISLANDS: LUTUR BATU ON MOA ISLAND Sugiarto, Sigit; Rupilele, Karolina; MA, Ratnah Kurniati; Lekitoo, John Nandito; Inuhan, Michael; Dahoklory, Andy Sunder Keer
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0475-0482

Abstract

The people of Moa Island have a cultural heritage in the form of Lutur Batu. Lutur Batu is a construction of large stones arranged side by side to form a circular wall. Lutur Batu is used to close village boundaries and as a garden fence which aims to protect plants from wild animals. This research aims to determine ethnomathematics studies on Lutur Batu on Moa Island. The ethnomathematics study of Lutur Batu on Moa Island in this research is expected to provide a deeper understanding of the local wisdom of the people of Moa Island, as well as contribute to the recognition, maintenance, and preservation of cultural heritage with mathematical value in the region. Furthermore, an understanding of the geometric patterns and construction of Lutur Batu can be adopted in mathematics learning at school to increase students' interest in learning and understanding of mathematical concepts.This qualitative research focuses on an in-depth understanding of ways of thinking and using mathematics in the traditions and practices of making Lutur Batu in Moa Island. The results of the analysis on Lutur Batu and the manufacturing process showed that there are mathematical concepts such as circles, cylinders, comparisons, and statistics. Lutur Batu and the Mathematical concepts contained therein can be used in Mathematics learning at school to enrich learning materials, increase learning motivation and students' understanding of Mathematics concepts. Apart from that, it can increase students' insight and knowledge regarding the cultural heritage of Moa Island
RAINFALL FORECASTING OF SALT PRODUCING AREAS IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH LINEARIZED RIDGE REGRESSION DUMMY Sahriman, Sitti; Randa, Eunike Laurine; Surianda, Sitti Aisyah; Hisyam, M. Zaky Gozhi; Taufik, Muh. Ikbal; Putra, Guntur Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0483-0492

Abstract

Pangkep Regency is one of the regions in South Sulawesi that is the center of national salt production. Salt production in the area is still dependent on sea water evaporation so that rainfall is one of the determining factors for the success of salt productivity. Statistical downscaling is an accurate method for rainfall forecasting by linking the local scale rainfall in Pangkep Regency (response variable) with the global scale of the global circulation model/GCM output (predictor variable). However, the GCM output rainfall has a large dimension, which is an 8×8 grid (64 predictor variables), causing multicollinearity. The linearized ridge regression (LRR) method is used to overcome this problem. This method combines the performance of generalized ridge regression and Liu-type methods to reduce multicollinearity. In addition, dummy variables based on the K-means clustering technique were added to the model to overcome heteroscedasticity. The purpose of this study is to obtain the results of rainfall forecasting in Pangkep Regency using the LRR method in the statistical downscaling model. The model generated from the LRR method with dummy variables is better at explaining the variability of rainfall in Pangkep Regency. The value is higher (72%) than without dummy variables (57%). The addition of dummy variables in the LLR model has better accuracy in forecasting rainfall. The actual rainfall correlation of Pangkep Regency with has the largest correlation (0.76) with the smallest mean absolute percentage error value (0.49). The results obtained are that the months of May - November tend to have relatively low rainfall, so that salt farmers can produce salt with good quantity and quality.

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