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Study Program of Mathematics, Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia Kampus C UNAIR Jl. Mulyorejo Surabaya, Jawa Timur 60115
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Contemporary Mathematics and Applications (ConMathA)
Published by Universitas Airlangga
ISSN : -     EISSN : 26865564     DOI : https://doi.org/10.20473/conmatha
Core Subject : Science, Education,
Materials Science & Nanotechnology Mathematics
Contemporary Mathematics and Applications welcome research articles in the area of mathematical analysis, algebra, optimization, mathematical modeling and its applications include but are not limited to the following topics: general mathematics, mathematical physics, numerical analysis, combinatorics, optimization and control, operation research, statistical modeling, mathematical finance and computational mathematics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2022)" : 5 Documents clear
Classification of Review Text using Hybrid Convolutional Neural Network and Gated Recurrent Unit Methods Fiqih Fathor Rachim; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.38262

Abstract

Consumer reviews are opinions from buyers to sellers based on service satisfaction or product quality. The more consumer reviews cause the process of analyzing manually will be difficult. Therefore, an automated sentiment analysis system is needed. Each review will be grouped into a sentiment class which is divided into positive and negative classes. This study aims to classify review texts using the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) methods. The research stages in this study include collecting data on Tokopedia review texts, extracting hidden information from review texts using CNN, conducting learning on review texts using GRU. A total of 1000 review texts were divided into 80% training data and 20% test data. The review text is converted into matrix using One Hot Encoding algorithm and then extracted using CNN. The CNN process includes the convolution calculation, the calculation of the Rectified Linear Unit (ReLU) activation function, and the pooling stage. The extraction results in the CNN process are continued in the GRU process. The GRU process includes initializing parameters, GRU feed forward, Cross-Entropy Error calculation, GRU feedback, and updating weights and biases. The optimal weight is obtained when the error value in the training is less than the expected minimum error or the training iteration has reached the specified maximum iteration. Optimal weight is used for validation test on test data. The implementation of review text classification using the hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) method was made using the python programming language. The accuracy of the validation test is 88.5%
Designing Standard Growth Chart Based on Weight-For-Age Z-Score of Children in East Java Using Least-Square Spline Estimator Nur Chamidah; Ardi Kurniawan; Toha Saifudin
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.38917

Abstract

Children would be categorized as children who have underweight nutritional status, if according to index of anthropometric they have a lack of weight. In Indonesia, this anthropometric index is recorded on a Card Toward Health called as KMS. This card follows the WHO-2005 standard which is designed based on samples from Brazil, Ghana, India, Norway, Oman, and USA. Those samples, of course, physically are very different from Indonesian children. Therefore, in this paper we design weight-for-age Z-score standard growth charts of children by using least-square spline estimator and samples of children from East Java province, Indonesia. Next, the proposed children standard growth charts are used to assess East Java children nutritional status. The results show that the proposed standard growth charts have met the goodness of fit criteria namely the average values of coefficient determination for boy and girl are close to one, and values of mean square errors are close to zero. It means that the proposed growth charts are more suitable to be used to assess the nutritional status of East Java children, because they can better explain the real conditions of children in East Java, Indonesia than the WHO-2005 standard growth charts.
Analisis Kestabilan dan Kontrol Optimum pada Model Penyebaran Penyakit Influenza dengan Adanya Populasi Cross-Immune Bertha Aurellia Pamudya Fajar; Miswanto; Windarto
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.39168

Abstract

Influenza is a respiratory tract infection known as flu. Caused by an RNA virus from Orthomyxoviridae family. This thesis aims to analyze the stability of the equilibrium point in the mathematical model of influenza transmission with Cross-Immune population and applying optimal control variables in the form of prevention and treatment. In this mathematical model of influenza transmission with Cross-Immune population, we obtain two equilibriums namely, the non- endemic equilibrium and the endemic equilibrium. Local stability and the existence of endemic equilibrium depend on the basic reproduction number (R0). The spread of influenza does not occur in the population when R0 < 1 and the spread of influenza persist in the population when R0 > 1. Furthermore, the problem of control variables in the mathematical model of influenza transmission is determined through the Pontryagin Maximum Principle method. The numerical simulation results show that treatment efforts are more effective in suppressing the spread of influenza disease than prevention efforts. However, giving control variables in the form of prevention and treatment at the same time is very effective in minimizing the number of human populations expose to and infected with influenza.
Model Data Kepemilikan Asuransi Kesehatan di Indonesia Berdasarkan Status Pekerjaan Melalui Analisis Regresi Logistik Biner Dua Level Marsya Anggun Prisila; Anna Islamiyati; Andi Kresna Jaya
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.39354

Abstract

Regresi logistik biner dua level merupakan metode analisis regresi yang digunakan untuk menganalisis hubungan antara satu variabel respon yang berupa data kualitatif dikotomi dengan beberapa variabel prediktor, dari data yang berstruktur hirarki. Penelitian ini bertujuan untuk mendapatkan model data kepemilikan asuransi kesehatan di Indonesia berdasarkan status pekerjaan melalui analisis regresi logistik biner dua level. Metode yang digunakan adalah regresi logistik biner dua level dengan model random intercept menggunkan maximum likelihood estimation pada data kepemilikan asuransi kesehatan di Indonesia. Berdasarkan hasil taksiran model diperoleh bahwa status pekerjaan berpengaruh terhadap kepemilikan asuransi kesehatan di Indonesia dan 2.99 kali berpeluang memiliki asuransi kesehatan dibanding penduduk yang tidak memiliki pekerjaan.
Penugasan Tutor Sebaya dengan Metode Pinalti Hardy Batlajery; Venn Ilwaru
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.39524

Abstract

This research focuses on the assignment of peer tutors in the Mathematics Study Program, Mathematics Department, FMIPA Unpatti. The purpose of this study was to obtain the completion of peer tutor assignments with the penalty method. The penalty method is a method used to solve the problem of unbalanced assignments. The processed two parts, namely finding the initial solution and finding the optimal solution. The column penalty method or the row penalty method is used to get the initial solution. In this study, the row penalty method is used because the number of rows is less than the number of columns, and the optimal solution is sought. The data used are student names, courses, and final grades. The results obtained using the Penalty method are that David becomes a tutor in the Statistical Method course, Alfito becomes a tutor in the Operations Research course, Christin becomes a tutor in the Linear Program course, Gabriella becomes a tutor in the Analytical Geometry course and Navila becomes a tutor in the course Elementary Statistics.

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