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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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SENTIMENT ANALYSIS OF OMNIBUS LAW USING SUPPORT VECTOR MACHINE (SVM) WITH LINEAR KERNEL Makhtum, Ahmad Rohiqim; Muhajir, Muhammad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2197-2206

Abstract

The Omnibus Law is a recently enacted legislation that has been implemented within the regulatory framework of Indonesia. This legal framework, often denoted as the Universal Sweeping Law, consolidates multiple legal norms into a singular regulation. The Omnibus law encompasses a total of 11 distinct clusters, one of which pertains specifically to labor regulations. Nevertheless, the Omnibus law has elicited diverse reactions among the Indonesian populace, particularly on the Twitter platform. The researchers employed scraping techniques to extract tweets from Twitter users. A total of 3067 data points were collected during the period from March 20, 2022 to May 20, 2022. The data were subsequently categorized into positive, negative, and neutral sentiments. They were then assigned weights and classified using the Suport Vector Machine (SVM) method. The objective was to identify the public's sentiments towards the Omnibus law and evaluate the accuracy of the Support Vector Machine (SVM) method. The accuracy of the SVM algorithm with a linear kernel is found to be 97.05% based on its classification performance. There is a greater level of public concern and attention directed towards positive responses in relation to the Omnibus law, as opposed to negative responses. The positive responses encompassed the provision of favorable legislation to assist young entrepreneurs, whereas the negative responses pertained to concerns regarding persistently low wages for workers, despite the implementation of the Omnibus Law.
A DIFFERENTIABLE STRUCTURE ON A FINITE DIMENSIONAL REAL VECTOR SPACE AS A MANIFOLD Kurniadi, Edi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2207-2212

Abstract

There are three conditions for a topological space to be said a topological manifold of dimension : Hausdorff space, second-countable, and the existence of homeomorphism of a neighborhood of each point to an open subset of or -dimensional locally Euclidean. The differentiable structure is given if the intersection of two charts is an empty chart or its transition map is differentiable. In this article, we study a differentiable manifold on finite dimensional real vector spaces. The aim is to prove that any finite-dimensional vector space is a differentiable manifold. First of all, it is proved that a finite dimensional vector space is a topological manifold by constructing a norm as its topology. Given a metric which is induced by a norm. Two norms on a finite dimensional vector space are always equivalent and they are determine the same topology. Secondly, it is proved that the transition map in the finite dimensional vector space is differentiable. As conclusion, we have that any finite dimensional vector space with independent norm topology choice is a differentiable manifold. As a matter of discussion, it can be studied that the vector space of all linear operators of a finite dimensional vector space has a differentiable manifold structure as well.
SCHEDULING ANALYSIS BEDUGUL VILLA CONSTRUCTION PROJECT USING PERT AND CPM METHODS Santoso, Kiswara Agung; Yusnita, Ade Ratna; Pradjaningsih, Agustina
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/barekengvol18iss1pp0105-0116

Abstract

Scheduling in construction projects is necessary so that the planned time to complete the project can be achieved on time. the methods used in optimizing project scheduling are the Project Evaluation Review Technique (PERT) method and the Critical Path Method (CPM) method. Bedugul Villa is one of the projects that has been carried out with a work contract for 175 calendar days and the scheduling of which will be optimized in this study. The optimal duration for scheduling with the PERT method is to produce an optimal duration of 170 calendar days. The duration is 5 days faster than the existing schedule prepared by the project construction contractor, which is 175 calendar days. The probability of completion of the project is 87.7%. Calculations using the CPM method are 168 calendar days or 7 days earlier than the existing schedule made by the contractor.
A FRACTIONAL DIFFERENTIAL EQUATION MODEL FOR THE SPREAD OF POTATO LEAF ROLL VIRUS (PLRV) ON POTATOES Jasmine, Prisca; Putri, Arrival Rince; Efendi, Efendi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2213-2224

Abstract

Potatoes infected with the PLRV virus will experience a decrease in production up to 90%. In this paper, The PLRV distribution fractional differential equation model with potato and vector populations is reformulated by adding one new parameter, namely the rate of vector death due to predators. The model is divided into susceptible and infected classes. The PLRV dispersion model was developed and converted to a fractional order form for 0<σ ≤ 1. Next, the invariant region, positive solutions, basic reproduction number, equilibrium point, and stability were determined. Based on the stability analysis, it is shown that the stability of the disease-free equilibrium point is locally stable and globally stable if the basic reproduction number (R0)<1, and the stability of the endemic equilibrium point is globally stable if the basic reproduction number (R0)>1. Numerical solutions were also carried out to determine the effect of several parameters on the PLRV distribution model on potatoes. The numerical solution results show that the elimination rate of infected potatoes and the infection rate of potatoes have a significant role in controlling the spread of PLRV in potatoes.
A DYNAMIC HETEROGENEOUS NEXUS BETWEEN PADDY AND POVERTY: EVIDENCE FROM DUMITRESCU-HURLIN CAUSALITY AND PMG-ARDL Destiartono, Mohamad Egi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2225-2234

Abstract

Agriculture is supposed to have a pivotal role in assisting poverty alleviation in Indonesia. Hence, this paper empirically examines the causal link between paddy productivity and poverty rates in Sumatra, retrieving balanced panel data from ten provinces for the period 2010-2022. Dumitrescu-Hurlin (DH) causality and Pooled Mean Group (PMG) methods are applied in order to reveal the causal direction and the elasticity under heterogeneous panel models. This paper integrates slope homogeneity, panel unit root, and panel cointegration tests. The results reveal that poverty rates and paddy productivity, are integrated in mixed order, and , and they are cointegrated. The DH causality test denotes a unidirectional causality from paddy productivity toward poverty rates which implies the absence of a feedback effect. Following the PMG model, there is a positive impact of paddy productivity on poverty rates in the short run (∆β= 0.29); however, this linkage switches to become negative in the long run (β= -0.48). A 1% improvement in paddy productivity will be followed by a 0.48% reduction in poverty rates. Thus, augmenting paddy productivity has a favorable role in declining poverty rates. The estimated parameters of long-run PMG are robust, i.e., consistent with alternative methods of cointegrated regressions.
SENTIMENT ANALYSIS WITH LONG-SHORT TERM MEMORY (LSTM) AND GATED RECURRENT UNIT (GRU) ALGORITHMS Putera Khano, Muhammad Nazhif Abda; Saputro, Dewi Retno Sari; Sutanto, Sutanto; Wibowo, Antoni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2235-2242

Abstract

Sentiment analysis is a form of machine learning that functions to obtain emotional polarity values or data tendencies from data in the form of text. Sentiment analysis is needed to analyze opinions, sentiments, reviews, and criticisms from someone for a product, service, organization, topic, etc. Recurrent Neural Network (RNN) is one of the Natural Language Processing (NLP) algorithms that is used in sentiment analysis. RNN is a neural network that can use internal memory to process input. RNN itself has a weakness in Long-Term Memory (LTM). Therefore, this article examines the combination of Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. GRU is an algorithm that is used to make each recurrent unit able to record adaptively at different time scales. Meanwhile, LSTM is a network architecture with the advantage of learning long-term dependencies on data. LSTM can remember long-term memory information, learn long-sequential data, and form information relation data in LTM. The combination of LSTM and GRU aims to overcome RNN’s weakness in LTM. The LSTM-GRU is combined by adding GRU to the data generated from LSTM. The combination of LSTM and GRU creates a better performance algorithm for addressing the LTM problem.
APPLICATION OF BAGGING CART IN THE CLASSIFICATION OF ON-TIME GRADUATION OF STUDENTS IN THE STATISTICS STUDY PROGRAM OF TANJUNGPURA UNIVERSITY Imtiyaz, Widad; Satyahadewi, Neva; Perdana, Hendra
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2243-2252

Abstract

The timeliness of graduation is used as the success of students in pursuing education which can be seen from the time taken and measured by the predicate of graduation obtained. The characteristics of students who tend to graduate not or on time can be analyzed using classification techniques. Classification and Regression Tree (CART) is one of the classification tree methods. There is a weakness in CART, which is less stable in predicting a single classification tree. The weaknesses in CART can be improved by using Ensemble methods, one of which is Bootstrap Aggregating (Bagging) which can reduce classification errors and increase accuracy in a single classification model. This study aims to classify and determine the accuracy of Bagging CART in the case of the accuracy of student graduation classification. The number of samples used is 140 data on the graduation status of Untan Statistics Study Program students from Period I of the 2017/2018 academic year to Period II of the 2022/2023 academic year. The variables used are the timeliness of graduation which is categorized into two namely Not and On Time, Gender, Semester 1 GPA, Semester 2 GPA, Semester 3 GPA, Semester 4 GPA, Region of Origin Domicile, High School Accreditation, Entry Path, Scholarship, and first TUTEP. A good classification can be seen from the accuracy value. The CART method obtained an accuracy value of 70%. While using the CART Bagging method obtained an accuracy value of 85.71%. Based on the accuracy value obtained, the application of the CART Bagging method can increase accuracy and correct classification errors on a single CART classification tree by 15.71% by resampling 25 times.
APPLICATION OF FUZZY ANALYTICAL NETWORK PROCESS IN DETERMINING THE CHOICE OF AREAS OF INTEREST Tiara, Dinda; Sulistianingsih, Evy; Perdana, Hendra; Satyahadewi, Neva; Tamtama, Ray
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2253-2262

Abstract

The Untan Statistics Study Program offers students a choice of areas of interest to develop competencies, attitudes, and skills. This study aims to analyze the decision to determine the choice of field of interest according to lecturers and students using the Fuzzy Analytical Network Process (FANP) method. A combination of ANP methods and Fuzzy logic, FANP is used to model and analyze complex networks of several factors determining the choice of areas of interest. The step in this study begins with the determination of the criteria and sub-criteria used for tissue formation. Then a comparison was carried out in pairs using the Fuzzy scale, so that the calculation of the global weight value of each criterion and sub-criteria was obtained. The resulting weight can be used for decision making. Data in research affects the opinions of lecturers and students. The decision obtained using the FANP method in this study is in the opinion of lecturers and students that the fields of business and finance are priority alternatives with the highest weight of 44.5%. The second priority with a weight of 37.5%, namely social and industrial interests, and the environmental and disaster sector occupies the last priority with a weight of 18%.
INTEGRATION OF SVM AND SMOTE-NC FOR CLASSIFICATION OF HEART FAILURE PATIENTS Utari, Dina Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2263-2272

Abstract

SMOTE (Synthetic Minority Over-sampling Technique) and SMOTE-NC (SMOTE for Nominal and Continuous features) are variations of the original SMOTE algorithm designed to handle imbalanced datasets with continuous and nominal features. The primary difference lies in their ability to generate synthetic examples for the minority class when dealing with continuous and nominal features. We employed a dataset comprising continuous and nominal features from heart failure patients. The distribution of patients' statuses, either deceased or alive, exhibited an imbalance. To address this, we executed a data balancing procedure using SMOTE-NC before conducting the classification analysis with SVM. It was found that the combination of SVM and SMOTE-NC methods gave better results than the SVM method, seen from the higher level of accuracy and F1 score. F1 gives less sensitivity to class imbalance compared to accuracy. Suppose there is a significant imbalance in the number of instances between classes. In that case, the F1 score can be a more informative metric for evaluating a classifier's performance, especially when the minority class is of interest.
VECTOR AUTOREGRESSIVE WITH OUTLIER DETECTION ON RAINFALL AND WIND SPEED DATA Lestari, Lisa; Sulistianingsih, Evy; Perdana, Hendra
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/barekengvol18iss1pp0117-0128

Abstract

Vector Autoregressive (VAR) is a multivariate time series model that analyzes more than one variable where each variable in the model is endogenous. VAR is one of the models used in forecasting rainfall and wind speed. In observations of rainfall and wind speed, there are usually a series of events whose values are far from other observations or can be said to be outliers. The purpose of this study is to compare the VAR model on rainfall and wind speed data before and after outlier detection. This study uses secondary data, namely monthly data on rainfall and wind speed from 2019 to 2021. From the analysis results, the smallest AIC value obtained in the VAR model before outlier detection was 4.94, then the smallest AIC value in the VAR model after outlier detection was 0.25. Thus, it can be concluded that the best model is obtained in the VAR model after outlier detection seen from the smallest AIC value of the two VAR models.

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