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 8 Documents
 1 
Search results for , issue "vol. 12 no. 2 (2026)" : 8 Documents clear
Mechanism Design: A Mathematical ProgrammingFramework for Principal-Agent Systems* I Nyoman Sutapa; Laksmi Prita Wardhani; Magdalena Wullur; Mohammad Sukri Bin Mustapa
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.8989

Abstract

Mechanism design is often introduced as a reverseform of game-theoretic analysis because the designer startsfrom a desired objective and then constructs rules under whichstrategic agents choose outcomes consistent with that objective.This paper develops a mathematical programming frameworkfor principal-agent mechanism design under asymmetric infor-mation. The framework treats allocation and transfer rules asdecision variables and imposes incentive compatibility, individ-ual rationality, and feasibility as explicit constraints, consistentwith the optimization-based view of mechanism design. Theproposed mathematical formulation is intended to be directlyimplementable as a finite-dimensional optimization model whentypes and actions are discretized. The paper makes three con-tributions. First, it clarifies the relationship between the reversegame-theoretic interpretation and classical mechanism design,and inverse game-theoretic learning. Second, it formulates adirect-mechanism optimization model and derives its finite-typeLP and MILP specialization. Third, it discusses continuoustype spaces through finite-grid approximation and provides ageneral sensitivity interpretation for finite networks. A two-agentbinary-type example is used not as a new auction result, butas a benchmark recovery test showing that the optimizationtemplate recovers a second-price auction with a reserve pricefrom incentive and participation constraints. The framework isrelevant for systems in which incentives and outcomes must bedesigned jointly
Connectivity of Power Graphs of Cyclic Groups with Specific Orders Riyanto; Arif Munandar
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9603

Abstract

The power graph is defined as a graph whose vertexset consists of the elements of a group, where two vertices arejoined by an edge if one element is a power of the other. In thisresearch we discuss the power graph of a cyclic group of order n=pq, with p and q distinct prime numbers, illustrates theconnectivity among its vertices and reveals important topologicalproperties such as the diameter and the clique number. Fur-thermore, this study also examines the degrees of the verticesand provides an analysis of the Hamiltonian, Eulerian, andplanar characteristics of the graph. These findings highlighthow algebraic structures can be effectively explored throughgraph-theoretical perspectives, offering deeper insights into theinterplay between group theory and graph topology.
Regime-Switching Value-at-Risk Using Hidden Markov Models: A Cross-Market Analysis of Emerging and Developed Equity Indices Staenly staenly; Maria Yus Trinity Irsan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9691

Abstract

Accurate risk measurement is essential for fi-nancial stability, particularly in markets where returns fre-quently transition between distinct volatility regimes. Thispaper applies a Hidden Markov Model (HMM) to dailylog returns of three equity indices, namely the JakartaComposite Index (JCI), Nikkei 225, and S&P 500, over theperiod January 2008 to December 2024, covering approx-imately 4,100–4,300 observations per index and multiplemajor stress episodes. Gaussian HMMs with two, three,and four hidden states are estimated, with the BayesianInformation Criterion selecting three states for JCI andNikkei 225, and four states for S&P 500, indicating thatoptimal regime granularity is market-specific. A notablestructural finding is that the JCI and S&P 500 transitionmatrices exhibit near-zero probabilities for direct calm-to-stress transitions, implying that crises are preceded byan intermediate regime of elevated uncertainty. Regimeprobabilities are used to construct a regime-conditionalValue-at-Risk (HMM-VaR), benchmarked against Histori-cal Simulation (HS-VaR) and Student-t GARCH (1,1) VaR(t-GARCH-VaR) using the Kupiec unconditional coveragetest and the Christoffersen conditional coverage test. Sub-period analysis at the 95% confidence level reveals thatduring crisis periods, static methods exceed the nominalviolation rate by up to 2.4 times the margin of adaptivemethods, with HS-VaR violation rates reaching 11.88%(JCI) and 13.55% (S&P 500) against a nominal 5%. During normal periods, HMM-VaR is the only methodthat consistently passes both coverage tests across all threemarkets, with violation rates closely approximating thenominal level (4.90%–5.61%). The findings demonstratethat regime-switching VaR provides superior and moreconsistent risk measurement across diverse market struc-tures, while also revealing that aggregate backtesting canbe misleading due to the cancellation of opposing errorsbetween crisis and normal sub-periods.
Early Risk Prediction of Rheumatoid ArthritisUsing a Hybrid Restricted Boltzmann Machine Riska Rahma Yanti; Siti Amiroch; Awawin Mustana Rohmah; Dicka Yale Kardono
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.9705

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmunedisease that requires early detection to prevent permanent jointdamage. The main challenge in predicting RA risk lies in thecomplexity of clinical data, which is multidimensional and oftenimbalanced. This study employs a Hybrid Restricted BoltzmannMachine (H-RBM) approach to improve the accuracy of earlyRA risk prediction. The RBM method is used as a featureextractor to map non-linear relationships within clinical data,which is then combined with three classification algorithms:Support Vector Machine (SVM), Random Forest (RF), andLogistic Regression (LR). The research data was sourced fromKaggle, comprising 4,425 samples that were balanced using theSynthetic Minority Oversampling Technique (SMOTE). Testingwas conducted with three data split ratios (70:30, 80:20, and90:10) using GridSearchCV for hyperparameter tuning. Theexperimental results show that the RBM-SVM model dominatesabsolutely with the highest performance at the 90:10 ratio,yielding an accuracy of 99.82%, an AUC score of 1.0000, and arecall of 100%. The perfect recall rate indicates that the modelis capable of identifying all positive patients without any missedcases (zero false negatives). Meanwhile, the RBM-RF and RBM-LR models achieved optimal performance at an 80:20 ratio withaccuracies of 93.95% and 83.42%, respectively. Overall, RBMintegration proved significant in extracting more informativelatent information compared to raw clinical features, therebymaking a crucial contribution to a precise RA early detectiondecision support system
The The Solution of The Richards Equation by TheDual Reciprocity Method for Analyzing Infiltrationwith The Depth Variation of Trapezoidal Channelsin Heterogeneous Soi Millatuz Zahroh; Yuniarti Permatasari; Moh. Hasan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10713

Abstract

The research discusses the effect of depth variationson the infiltration process of trapezoidal irrigation channels intwo layers of heterogeneous soils. There are five depth variationsat 2.5 cm, 7.5 cm, 12.5 cm, 17.5 cm, and 22.5 cm. The infiltrationproblem of this research is constructed in Richards equation as amathematical model. The model is transformed into the modifiedHelmholtz equation to get linear partial differential equation.Based on the model, the flux value and boundary conditions aredefined and determined based on various assumptions. The modelwas then solved numerically using the dual reciprocity method(DRM). The numerical solution obtained is the suction potentialfor each variation in channel depth. The solution indicates thatthe area below the channels has the highest suction potential. Theresult shows that the suction potential at the shallowest depthsis the lowest. This indicates that the deeper channel produces ahigher flux compared to a shallower channel. The solutions alsoimply that there are jumps in suction potential value from layer1 to layer 2
A Simulation of the Discrete Time Markov ChainSusceptible Vaccinated Exposed Infected Recovered (DTMC SVEIR) Model with Relapse Cases in theSpread of Tuberculosis* Respatiwulan Respatiwulan; Hasih Pratiwi; Sri Sulistijowati Handajani; Yuliana Susanti; Kiki Ferawati; Muhammad Bayu Nirwana; Andreas Rony Wijaya; Aprilia Saniatul Rahmawati; Embun Zahra Nirmala
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10716

Abstract

Tuberculosis (TB) is a contagious disease that pri-marily affects the lungs and spreads through the air whenan infected person coughs, sneezes, or spits. TB remains as asignificant global health issue, with millions of cases and deathsannually. There are mathematical epidemiological models thathave been developed, including SIR, SEIR, and SVEIR forexplaining the spread of disease pattern. The SVEIR modelexplains the impact of vaccination on disease transmission such asTB. In TB cases, the recovered individuals could be relapsed andbe infected again. This study discussed a Discrete Time MarkovChain (DTMC) SVEIR model and applied model simulation toTB disease. The population is divided into five groups such assusceptible, vaccinated, exposed, infected, and recovered. Thechanges of group’s state follow Markov process in discretetime observation. The model is extension from DTMC SEIRwith relapse by adding Vaccinated compartment. The modelis expressed as transition probability that explain the changesof state in each group. The simulation results indicate that thespread of the disease is significantly influenced by parametersvalue such as contact rate, vaccination rate, vaccine effectiveness,relapse rate, and recovery rate. If contact rate higher thanthe recovery rate then the number of infected individuals willincrease. The other hand, if vaccination works effectively thenthe number of susceptible individuals decrease. Lower relapserates lead to higher recovery numbers, whereas higher relapserates accelerate reinfection
On the Energy and Laplacian Energy of the Shadow,Thorn, and Middle Graphs of the Lintang Graph Fransiskus Fran; Meliana Pasaribu; Raventino Raventino; Tria Mutiara
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10744

Abstract

A graph with a transparent initial structure may exhibit markedly different spectral behavior after local or incidence-based transformations. This paper investigates the energy and Laplacian energy of the shadow, one-thorn, and middle graphs derived from the lintang graph Ln = K2 ∨Kn∼ = K2,n. The shadow graph is identified as D2(Ln)∼ = K4,2n, so its adjacency and Laplacian spectra are obtained from the standard spectra of complete bipartite graphs. The one-thorn graph T (Ln) is formed by attaching exactly one pendant vertex to each vertex of Ln, and its spectra are determined through quadratic transformations of the adjacency and Laplacian eigenvalues of the parent graph. The middle graph M (Ln) is analyzed using block matrices, the Schur complement, and rank-one determinant identities. The resulting energy formulas include E(D2(Ln)) = 4√2n and E(T (Ln)) = 2√2n + 4 + 2n, while exact expressions are also obtained for E(M (Ln)) and for the Laplacian energies of all three transformed graphs. In particular, LE(T (Ln)) = (4n/n+2) + 2√2(n−1) + √n2 + 4 + √n2 + 4n + 8. Moreover, D2(Ln) is integral precisely when n = 2k^2 for some positive integer k, whereas T (Ln) and M (Ln) are neither integral nor Laplacian integral for any n ≥ 2. These results show that neighborhood duplication, pendant extension, and incidence expansion produce distinct spectral and arithmetic effects, despite originating from the same complete bipartite base graph.
A Predictive Framework for Transition ProbabilityShifts Using Environmental Data Via Lee-CarterModel Extension Kenneth Gunawan; Nicholas Andrew Tanubrata; Rex Aaron Holindo; Ferry Vincenttius Ferdinand; Helena Margaretha
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10834

Abstract

This study addresses the challenge of incorporatingenvironmental data, particularly air pollution, into practicalactuarial models. This study extends the Lee-Carter modelusing environmental variables and employs LASSO regressionto address potential multicollinearity among pollution indicators.The results are closed-form approximations for estimating theexpected changes in transition probabilities from the previoustransition probabilities forecast within both multiple-state andmultiple-decrement models, which are calculated using currentenvironmental data. These approximations enable actuaries toquantify the expected difference in transition probabilities fromthe initial forecast by utilizing the newest environmental data thatwas previously unavailable. The framework is empirically testedusing a case study on a dementia-related multiple-state modelin the UK. Results show significant improvements in predictiveaccuracy, measured by reductions in Mean Absolute PercentageError (MAPE), reducing MAPE by over 40% in several agegroups. These results emphasize the value of environmentalintegration in actuarial modeling, particularly in the context ofhealth-related insurance products

Page 1 of 1 | Total Record : 8

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 2 (2026) 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