cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Imam Mukhlash
Contact Email
imamm@matematika.its.ac.id
Phone
+6285648721814
Journal Mail Official
ijcsam.matematika@its.ac.id
Editorial Address
Departemen Matematika, Gedung F Lantai II, Kampus ITS, Keputih, Sukolilo-Surabaya 60111 Jawa Timur, Indonesia Phone: +62 31-5943354 Email:ijcsam.matematika@its.ac.id
Location
Kota surabaya,
Jawa timur
INDONESIA
International Journal of Computing Science and Applied Mathematics-IJCSAM
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 24775401     DOI : -
Core Subject : Education,
Mathematics
IJCSAM (International Journal of Computing Science and Applied Mathematics) is an open access journal publishing advanced results in the fields of computations, science and applied mathematics, as mentioned explicitly in the scope of the journal. The journal is geared towards dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry. IJCSAM (International Journal of Computing Science and Applied Mathematics) is a journal published by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Arjuna Subject : Matematika - Matematika Terapan
Articles 143 Documents
 11   12   13   14   15 
Local Stability Analysis of Mathematic Model SEIHR-VW on Dengue Haemorrhagic Fever Transmission Nolaika Arsiani Norramandhany; Widowati Widowati; Redemtus Heru Tjahjana
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.6054

Abstract

Dengue fever is caused by the dengue virus (DENV) and is mainly transmitted by mosquitoes, particularly Aedes aegypti. In this study, we develop a mathematical model to describe and analyze how dengue spreads within a population. The mathematical model is expressed as a nonlinear system of differential equations and consists of seven compartments (SEIHRVW): susceptible, exposed, infected, hospitalized, and recovered humans, along with susceptible and infected mosquitoes. The model has two possible equilibrium points: a non-endemic and endemic equilibrium point. To better understand the dynamics of the model, we calculate the basic reproduction number (R0) using the Next Generation Matrix (NGM) method, and then the Routh-Hurwitz criterion method is applied to analyze the local stability of both equilibrium points. The results indicate that the nonendemic equilibrium point is asymptotically stable when R0 < 1, while the endemic equilibrium point becomes asymptotically stable when R0 > 1. In general, our analysis concludes that the proposed dengue transmission model is asymptotically stable at the endemic equilibrium point, with R0 = 3.85011.
Partition Dimension of Bridge Graphs Between Complete and Star Graphs Amrullah Amrullah; Laila Hayati; Junaidi Junaidi
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8363

Abstract

This paper investigates the determination of the partition dimension for a \emph{bridge graph} formed by connecting a clique $K_n$ and a star $K_{1,m}$ through a single edge. Although the partition dimension has been extensively studied for various families and graph operations, the mixed dense--sparse case on $B(K_n,K_{1,m})$ remains unsettled, since the result is sensitive to the position of the bridge edge and the balance between the size parameters $n$ and $m$.We combine distance symmetry arguments, leaf-based constraints at the star center, and explicit constructions of distinguishing partitions to obtain tight values of the partition dimension. The study begins with the basic cases $K_1$ and $K_2$, and then proceeds to the general case with parameters $n\ge 2$. The main result shows that for the \emph{central bridge} ($e=v_1x$), it holds that $pd(B)=n-1$ if $m<n$, $pd(B)=n$ if $m=n$, and $pd(B)=m$ if $m>n$; for the \emph{leaf bridge} ($e=v_1u_1$), it holds that $pd(B)=n$ when $m\le n$, and$pd(B)=m-1$ when $m>n$. These results demonstrate that the location of the bridge edge, together with the size parameters $m$ and $n$ of the components, can sharpen the partition dimension value of the graph prior to the bridging operation.
Metaheuristic Search in Mixed Kernel and Spline Truncated Non-parametric Regression Mustika Hadijati; Irwansyah Irwansyah; Nurul Fitriyani; Muhammad Sopian Sauri
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8841

Abstract

Non-parametric regressions are widely used in data analysis because of their flexibility. Apart from their applicability, it is not easy to find the optimal parameters of the corresponding non-parametric models. This situation is caused by the nonexistence of a closed formula of the optimal parameters. In this paper, we propose a metaheuristic approach for optimal parameter search in mixed kernel and truncated spline and kernel regression. Moreover, we provide examples on how to implement the proposed algorithm to both real and simulated datasets. The results indicate that the algorithm yields highly accurate predictions for mixed truncated spline and kernel regression models.
A Probability Flux Approach for Binary Dynamics on Networks Mohamad Riyadi; Agus Yodi Gunawan; Dewi Handayani
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8848

Abstract

Binary-state dynamics on networks provide a powerfulframework for modeling epidemics and related spreading processes.Two main approaches are commonly used, namely exactcontinuous-time Markov chain (CTMC) formulations and meanfieldapproximations. The CTMC approach ensures stochastic accuracybut suffers from exponential state-space growth, whereasmean-field approximations lose reliability in heterogeneous orsmall networks. In this study, we formulate the master equationfor binary dynamics using a probability flux approach, yieldingan exact formulation for arbitrary networks. By integrating localtransition rules, network topology, and state-space partitioning,the framework captures microscopic dynamics while enablingmacroscopic analysis. Numerical simulations reveal that bothstate probabilities and expected infection levels are influencednot only by mean degree but also by structural heterogeneity.For instance, star and line topologies exhibit distinct behaviorsdespite having identical connectivity. Spectral analysis confirmsthe asymptotic stability of the disease-free equilibrium, whileinvariance under node relabeling emphasizes the role of graphsymmetries in reducing state-space complexity. This work extendsflux-based theory to network epidemics and provides a foundationfor future studies on adaptive or time-varying networks.Index Terms - Binary dynamics, probability flux, master equation,CTMC, ep
A Patch-Based Transformer Approach to Nonlinear Dynamics Natural Gas Price Forecasting Muhamad Syukron
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8849

Abstract

Natural gas prices are a critical economic indicator influencing various sectors of the global economy. Accurate forecasting is essential for effective policy formulation and strategic decision making. However, natural gas price movements often exhibit complex non-linear patterns that traditional statistical time series models fail to capture. Furthermore, many deep learning architectures struggle to effectively model these intricate dynamics. To address this challenge, we employ the Patch-Based Transformer (PatchTST) model for natural gas price forecasting. The comparative results reveal that PatchTST achieves substantially higher predictive accuracy than both statistical and other deep learning models. Its Transformer-based architecture, combined with patching and channel independence, enables the model to effectively capture both temporal dependencies and localized variations. The model achieved mean squared error (MSE) and mean absolute percentage error (MAPE) values of 0.1176 and 7.57%, respectively. These findings demonstrate that PatchTST provides robust and precise forecasts, offering valuable insights for decision-making in the energy market
Effectiveness of Digital Simulation-Based Learning Approach in Optimizing Students’ Understanding of Queueing Models Using Real-Life Data Arin Berliana Angrenani; Dian Kurniati; Susi Setiawani; Rafiantika Megahnia Prihandini; Ngizatul Afifah
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8850

Abstract

This study examines indications of the effectiveness of a digital simulation-based learning approach in supporting students’ understanding of queueing models using real-life data. Aquasi-experimental one-group pretest–posttest design, supported by qualitative interview data, was conducted with 31 undergraduate mathematics education students at the University of Jember. The ExtendSim software was used to create interactive queueing simulations that allowed students to explore parameters such as arrival rate, service rate, and waiting time. Validity and reliability tests were conducted using item–total (Pearson) correlations and Cronbach’s alpha, with results indicating high validity (r > 0.5, p < 0.05) and high internal consistency (a > 0.80). A paired ttest showed a statistically significant increase in scores within this sample (t = 8.89, p < 0.001). Students’ perceptions of the simulation were highly positive, with an average Likert score of 3.23 (very high). Qualitative interviews further indicated that the simulations helped students visualize queue dynamics and relate theoretical concepts to real-life contexts. There were also indications of increased motivation, engagement, and computational thinking skills; however, these findings are limited by the single-site sample and the one-group study design.
Analyzing the Influence of Gross Domestic Product on the Human Development Index Worldwide in 2021 Using a Nonparametric Regression Approach Based on Penalized Spline Estimator Dita Amelia; Azizah Atsariyyah Zhafira; Bryan Given Christiano Ginzel; Fery Yulian Putra; Yoga Setya Wibawa
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8851

Abstract

People’s welfare is a universal goal that is the main focus of all countries in the world. One of the indicators used to measure welfare is the Human Development Index (HDI), which includes education, health and per capita income. On the other hand, Gross Domestic Product (GDP) is the main measure of a region’s economic growth. This research aims to highlight how regional economic dynamics affect human welfare in the world in 2021 and the data source was obtained from OurWorldInData. This research uses nonparametric regression with a penalized spline estimator approach. Penalized Spline analysis shows that the best model for predicting HDI based on GDP per capita is to use 2 knot points, namely k1=8000 and k2=50000. This model produces a Mean Squared Error (MSE) value of 0.0018 and Generalized Cross Validation (GCV) of 0.0019. In addition, this model has the ability to explain response variability of R2=91.58%. The grouping of countries by GDP per capita reveals that economic improvement impacts human development differently across income levels. By tailoring strategies to specific income groups, policymakers can more effectively enhance human development outcomes, fostering a more equitable and prosperous society
Disturbance Estimation for Unmanned Surface Vehicles using a Nonlinear Disturbance Observer Emilta Friska Juniar; Tahiyatul Asfihani
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An Unmanned Surface Vehicle (USV) is a maritime transportation system designed to operate autonomously. However, in complex marine environments, the vessel is subjected to external disturbances such as wind, ocean currents, and waves that can affect the stability and robustness of its motion control system. This study aims to estimate these disturbances in real time using a Nonlinear Disturbance Observer (NDO) and to utilize the estimated disturbances to update the predictive model in a Disturbance Compensating–Nonlinear Model Predictive Control (DC-NMPC) framework. The estimation considers two types of disturbances: constant disturbances representing steady wind and current forces, and periodic disturbances representing wave effects modeled as sinusoidal functions. These disturbances affect the sway velocity (v) and yaw velocity (r) of the vessel. Simulation results show that the NDO is capable of reconstructing the actual disturbances with bounded and consistent estimation errors. This is indicated by RMSE values of 0.0070 for d1 and 0.0605 for d2 under the tested disturbance scenario. The estimation performance remains consistent under variations of the observer gain matrix, indicating observer stability. Increasing the gain improves the estimation response speed but slightly increases the error, revealing a trade-off between responsiveness and estimation accuracy. Furthermore, the observer is able to track time-varying sinusoidal disturbances, demonstrating robustness against dynamic environmental disturbances. These results indicate thatNDO-based disturbance estimation can enhance the robustness of USV motion control under environmental disturbances.
Heterogeneous Correlation Mapping between Rainfall Variability in Lake Toba and Indian Ocean Sea Surface Temperature Mohamad Khoirun Najib; Sri Nurdiati
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rainfall variability in the Lake Toba watershed of North Sumatra is influenced by large-scale ocean–atmosphere in-teractions, particularly those involving sea surface temperatures (SST) in the Indian Ocean. This study applies Heterogeneous Correlation Mapping (HCM) to examine the spatially varying relationship between monthly rainfall at 13 meteorological stations and SST over the Indian Ocean warm pool (5°S–10°N, 60°E–80°E) during 1981–2014. Singular Value Decomposition (SVD) is employed to extract dominant coupled modes of SST–rainfall variability. Results indicate that a six-month lag yields the strongest coupling, with the leading mode explaining 88.5% of the total variance. A clear spatial heterogeneity is observed: stations such as Lumban Julu and Silaen exhibit stronger SST–rainfall correlations, while others show weaker responses, likely due to topographic and local climatic modulation. These findings underscore the importance of accounting for spatial and temporal structures in hydroclimatic teleconnection analysis and offer insights for improving seasonal rainfall prediction in mountainous tropical regions
Quantization-Aware Training for Man-in-the-Middle Attacks Detection in IoT Application Dyah Ayu Suci Ilhami; Aji Gautama Putrada; Farah Afianti
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Issues in securing the resilience of Deep Learning models and operational problems related to model size and latency in the Internet of Things (IoT) environment, especially when applied to devices with limited resources, can be overcome by implementing a modified Quantization-Aware Training (QAT) model based on IDS and an autoencoder. This research proposes the use of QAT to achieve operational efficiency while strengthening the detection model’s resilience against data manipulation in MiTM attacks. By integrating QAT, the model not only becomes lighter for edge devices but also more capable of maintaining detection integrity even when model weights are compromised. The research was conducted using the CICIoT2023 dataset, and the output of the advanced QAT model was then applied to resource-constrained IoT devices, namely the Raspberry Pi 3. The methodology began with data collection, followed by data preprocessing, which was then normalized before being fed into the IDS and autoencoder techniques. After the autoencoder model was successfully created, the QAT model was developed so that it remained unchanged or even improved when implemented on the Raspberry Pi 3. With the application of QAT modifications, an 80.43% reduction in model size and an 11.2% increase in model inference speed were confirmed. Furthermore, when faced with QAT model weight corruption of up to 20%, the F1-score value remained stable at 100%. These results show that the QAT model is highly effective at improving model reliability and maintaining the resilience of deep learning models against MiTM attacks, even under limited conditions.

Page 14 of 15 | Total Record : 143

 11   12   13   14   15 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 1 (2026) Vol. 11 No. 2 (2025) Vol. 11 No. 1 (2025) Vol. 10 No. 2 (2024) Vol. 10 No. 1 (2024) Vol. 9 No. 2 (2023) Vol. 9 No. 1 (2023) Vol. 8 No. 2 (2022) Vol. 8 No. 1 (2022) Vol. 7 No. 2 (2021) Vol. 7 No. 1 (2021) Vol. 6 No. 2 (2020) Vol. 6 No. 1 (2020) Vol. 5 No. 2 (2019) Vol. 5 No. 1 (2019) Vol. 4 No. 2 (2018) Vol. 4 No. 1 (2018) Vol. 3 No. 2 (2017) Vol. 3 No. 1 (2017) Vol. 2 No. 3 (2016) Vol. 2 No. 2 (2016) Vol. 2 No. 1 (2016) Vol. 1 No. 1 (2015) More Issue