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Contact Name
Windarto
Contact Email
windarto@fst.unair.ac.id
Phone
+62315936501
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conmatha@fst.unair.ac.id
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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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INDONESIA
Contemporary Mathematics and Applications (ConMathA)
Published by Universitas Airlangga
ISSN : -     EISSN : 26865564     DOI : https://doi.org/10.20473/conmatha
Core Subject : Science, Education,
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.
Articles 5 Documents
Search results for , issue "Vol. 4 No. 1 (2022)" : 5 Documents clear
Pewarnaan Titik Ketakteraturan Lokal Inklusif pada Hasil Operasi Comb Graf Bintang Arika Indah Kristiana; Surya Indriani; Ermita Rizki Albirri
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i1.33606

Abstract

Let G(V,E) is a simple graph and connected where V(G) is vertex set and E(G) is edge set. An inclusive local irregularity vertex coloring is defined by a mapping l:V(G) à {1,2,…, k} as vertex labeling and wi : V(G) à N is function of inclusive local irregularity vertex coloring, with wi(v) = l(v) + ∑u∈N(v) l(u). In other words, an inclusive local irregularity vertex coloring is to assign a color to the graph with the resulting weight value by adding up the labels of the vertices that are neighbouring to its own label. The minimum number of colors produced from inclusive local irregularity vertex coloring of graph G is called inclusive chromatic number local irregularity, denoted by Xlisi(G). In this paper, we learn about the inclusive local irregularity vertex coloring and determine the chromatic number of comb product on star graph.
Pewarnaan Titik Ketakteraturan Lokal Inklusif pada Keluarga Graf Unicyclic Arika Indah Kristiana; Muhammad Gufronil Halim; Robiatul Adawiyah
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i1.33607

Abstract

The graph in this paper is a simple and connected graph with V(G) is vertex set and  E(G) is edge set. An inklusif local irregularity vertex coloring is defined should be maping l:V(G) à {1,2,…, k} as vertex labeling and wi : V(G) à N is function of inclusive local irregularity vertex coloring, with wi(v) = l(v) + ∑u∈N(v) l(u) in other words, an inclusive local irregularity vertex coloring is to assign a color to the graph with the resulting weight value by adding up the labels of the vertices that are should be neighboring to its own label. The minimum number of colors produced from inclusive local irregularity vertex coloring of graph G is called inclusive chromatic number local irregularity, denoted by Xlisi(G). Should be in this paper, we learn about the inclusive local irregularity vertex coloring and determine the chromatic number on unicyclic graphs.
Analisis Kestabilan Model Matematika Predator-Prey pada Dinamika Sosial Laurensia Regina Bestari Gepak; Miswanto Miswanto; Cicik Alfiniyah
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i1.34147

Abstract

In social life, difference and diversity is something that cannot be denied by anyone. Starting from differences horizontally concerning ethnicity, language, customs to religion and vertically concerning the political, social, cultural to economic fields. The existence of these many differences can certainly bring positive and negative impacts in social life. With diversity, interaction in society is dynamic, but it results in the emergence of negative attitudes such as egoism and competition between groups. From the occurrence of this can trigger the problem of social inequality in the community. Social inequality can occur because of national development efforts that only focus on economic aspects and forget about social aspects. The purpose of this thesis is to discuss the stability analysis of the predator-prey mathematical model on social dynamics with the Holling type II functional response. From this model analysis, we obtained four equilibrium points, which are the equilibrium point for the extinction of all population (E0) which is unstable, then the equilibrium point for the extinction of the non-poor population and the poor (E1) and the extinction of the non-poor population (E2) which are stable with certain conditions and coexistence (E3) which is to be asymptotically stable. Also in the final section, we perform the numerical simulation to supports the analytical result.
Pembentukan Model Pohon Keputusan pada Database Car Evaluation Menggunakan Statistik Chi-Square Retno Maharesi
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i1.34393

Abstract

The study discusses problems related to the formation of a decision tree based on a collection of evaluation data records obtained from a number of car buyers. This secondary data was obtained from the UCL machine learning website. The purpose of this research is to produce a prototype algorithm for obtaining an inductive decision tree based on Chi-square statistics. An inductive decision tree formation method based on the Chi-square contingency test was compared with a decision tree obtained using a machine learning algorithm which was done using RapidMiner5 software. The work to produce an inductive decision tree was carried out by first processing data using Microsoft excel and next processed using SPSS software, on the crosstabs descriptive menu. The results of the two methods provide some kind of similar rules, in terms of the order of priority of the variables that most influencing people's decision to accept an automotive product. The formation of the decision tree uses a random sampling of size 300 data records among 1729 respondent data records in the car evaluation database. The resulting decision tree should have a minimal structure like a binary tree. This is possible because its formation is based on the statistical inferential method, so it does not require a separate pruning process as an addition step in the C4.5 algorithm, which actually this algorithm also considers aspects of the statistical significance.
Penerapan Seagulls Optimization Algorithm untuk Menyelesaikan Open Vehicle Routing Problem Laula Ika Setya Rahman; Asri Bekti Pratiwi; Herry Suprajitno
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 1 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i1.34549

Abstract

This paper aims to solve the problem of Open Vehicle Routing Problem using Seagulls Optimization Algorithm. Open Vehicle Routing Problem (OVRP) is a variation of Vehicle Routing Problem (VRP) which will not return to the depot after visiting the last customer, is different from VRP which requires the vehicle to return to the depot because the company have insufficient number of vehicles for the distribution of products to customers so they must to rent vehicles and this OVRP aims to minimize the total cost of distributing products with the shortest optimal distance to meet the demands of each customer with private vehicles and rental vehicles. Seagulls Optimization Algorithm (SOA) is the algorithm inspired by the behaviour of seagulls in migrating and ways of attacking the pray of seagulls in nature. In general, the process begins with generating the initial position, evaluating the objective function, the migration process, the attacking process to get a new position, compare the objective function for the new position and the old position, update the position and save the best seagulls in each iteration until the maximum iteration is met. The program used to complete OVRP with Seagulls Optimization Algorithm is Borland C++ and implemented using 3 case examples, small data with 18 customers, medium data 50 customers and large data 100 customers. Based on the implementation results, it can be concluded that the higher number of seagulls, iterations and the smaller the control variable value tend to effect minimum cost gained.

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