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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. 8 No. 1 (2026)" : 5 Documents clear
Nilpotent Graphs of Rings of Integer Modulo: Structural Properties and Topological Indices Deny Putra Malik; Gusti Yogananda Karang; Qurratul Aini; I Gede Adhitya Wisnu Wardhana
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.71177

Abstract

Nilpotent elements in modular rings play a fundamental role in understanding the algebraic structure of rings and their applications in various mathematical domains. Motivated by the need to explore the interplay between algebraic and combinatorial representations, this study introduces and investigates nilpotent graphs constructed from rings of integers modulo prime powers. We begin by characterizing nilpotent sets and establishing theorems that describe their distribution and algebraic behavior. Using these characterizations, we construct nilpotent graphs, where vertices represent nilpotent elements and edges reflect their interactions. The structural properties of these graphs are examined, and several well known topological indices, such as the Zagreb, Harary, Hyper Wiener, Randić, Harmonic, Sombor, and Schultz indices, are computed to quantify connectivity, complexity, and centrality. The results reveal meaningful patterns that bridge ring theory and graph theory.
Utilization of GEE for Mapping Land Cover Changes in Settlements Before and After the 2018 Lombok Earthquake in North Lombok Regency Lia Fitta Pratiwi; Luzianawati; Nuzla Af'idatur Robbaniyyah; Kurnia Ulfa; Muhammad Rijal Alfian
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.76412

Abstract

A major earthquake struck Lombok, West Nusa Tenggara, in 2018, causing significant infrastructure damage, including in North Lombok Regency. This study aims to analyze land cover changes in North Lombok Regency before and after the earthquake using Sentinel-2 Level-1C satellite imagery. Through supervised classification using the Maximum Likelihood method, changes in the area of various land cover types, such as Bare Land, Paddy Field, Dense Vegetation, Water Bodies, and Built-up Areas, were identified and analyzed temporally in 2017, 2020, and 2023. The results show that the earthquake caused drastic changes in land cover in the study area, particularly a decrease in the area of dense vegetation and an increase in the area of bare land. These changes indicate significant ecosystem disruption caused by the earthquake. Subsequently, a recovery trend was observed in the 2020-2023 period. Changes in land cover, especially in built-up areas and bare land, are consistent with the earthquake's impact and subsequent reconstruction efforts.
Cryptocurrency Price Prediction Using Long Short Term Memory Algorithm and Moving Average Convergence Divergence Abiyyu Dicky Pratama; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.76496

Abstract

Cryptocurrency is one of the digital assets that is increasingly popular for investment in Indonesia. However, the price movements of cryptocurrencies tend to be volatile, as prices can change at any time and are not easy to predict. This study aims to predict cryptocurrency price movements using the Long Short-Term Memory Algorithm (LSTM) and Moving Average Convergence Divergence (MACD). LSTM is an algorithm used to generate optimal weights and biases in modeling cryptocurrency data, while MACD is used to analyze trends and momentum in cryptocurrency prices. The data used consists of daily closing prices of Bitcoin (BTC), totaling 809 data points. The data is divided into 70% (566 data) for the training process and 30% (243 data) for the testing process. From this data, patterns are formed with five inputs and one output, resulting in 561 patterns for the training process and 238 patterns for the testing process. The LSTM and MACD processes for predicting cryptocurrency include procedures for data input, data division, parameter initialization, LSTM calculation, average error evaluation, and MACD calculation. Based on the program implementation, with several parameter values, the average error difference obtained during the training stage is 0.0695 and 0.0303 during the testing stage. Because the average error difference obtained is relatively small, this indicates that LSTM-MACD is capable of recognizing data patterns and predicting data effectively.
Super Edge-Magic Total Labelings of ?? × ?? With ?? Pendants Rica Amalia; Kholifatur Rohmah; Khairil Anam
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.85975

Abstract

A graph ? is defined as a finite nonempty set ? of objects called vertices (vertex for singular) together with a possibly empty set of ? ⊆ {{?, ?} ∣ ?, ? ∈ ?} called edges. One of interesting topic in graph theory is graph labelling. Super edge magic total labeling is a special form of total edge magic labeling, where vertex labels must come from the set {1,2,…,|?|}, while edge labels come from the remainder of the set {1,2,…,|?|+|?|}. Formally, this labeling is a bijective mapping: ? : ? ∪ ?→{1,2,…,|?|+|?|} with the following conditions: ?(?) ∈ {1,2,...,|?|} ∀? ∈ ? where there is a constant number ? such that for every edge ? = ?? ∈ ?, ?(?) + ?(?) + ?(?) = ?. The main focus of this research is to determine the existence and construction of super edge-magic total labeling on cartesian product graph ?? × ?? with additional pendants. In this study, we get that ?? × ?? with pendants are graphs with super edge-magic total labelling’s by constructing the labeling of their vertices and edges, thereby obtaining a magic constant ?.
Performance Evaluation of the Gated Recurrent Unit Model in Predicting the Closing Stock Price of PT Aneka Tambang Tbk Wahyu Erinna Ratih; Safira Fitri Anggraini; Tri Maryono Rusadi
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.86603

Abstract

Stock price prediction is a crucial task in financial market analysis due to its impact on investment decision-making. This study aims to apply the Gated Recurrent Unit (GRU) model to forecast the stock price of ANTM.JK using historical time series data. A total of 12 experimental models were developed by varying data split ratios, window sizes, epochs, and batch sizes to identify the optimal model configuration. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the GRU model is capable of predicting stock prices with high accuracy, achieving an accuracy of 98.02%. The RMSE values ranged from 57.78 to 91.09, MAE values ranged from 38.20 to 62.87, and MAPE values ranged from 1.98% to 3.22%. The best-performing model was Model 7, with a 70:30 training–testing split, a window size of 30, 50 epochs, and a batch size of 16, which produced the lowest error values among all models. These findings indicate that GRU is an effective and reliable approach for modeling nonlinear and dynamic stock price time series and has strong potential for supporting financial market analysis and investment decision-making.

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