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All Journal International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research
Riza Adrian Ibrahim
Department of Mathematics, FMIPA, Universitas Padjadjaran Jl. Raya Bandung-Sumedang Km 21, Jatinangor 45363, Jawa Barat, Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Transportation Other

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Estimation of the Extreme Distribution Model of Economic Losses Due to Outbreaks Using the POT Method with Newton Raphson Iteration Riza Adrian Ibrahim; Sukono Sukono; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol 2, No 1 (2021)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.324 KB) | DOI: 10.46336/ijqrm.v2i1.118

Abstract

Extreme distribution is the distribution of a random variable that focuses on determining the probability of small values in the tail areaof the distribution. This distribution is widely used in various fields, one of which is reinsurance. An outbreak catastrophe is non-natural disaster that can pose an extreme risk of economic loss to a country that is exposed to it. To anticipate this risk, the government of a country can insure it to a reinsurance company which is then linkedto bonds in the capital market so that new securities are issued, namely outbreakcatastrophe bonds. In pricing, knowledge of the extreme distribution of economic losses due to outbreak catastrophe is indispensable. Therefore, this study aims to determine the extreme distribution model of economic losses due to outbreak catastrophe whose models will be determined by the approaches and methods of Extreme Value Theory and Peaks Over Threshold, respectively. The threshold value parameter of the model will be estimated by Kurtosis Method, while the other parameters will be estimated with Maximum Likelihood Estimation Method based on Newton-Raphson Iteration. The result of the research obtained is the resulting model of extreme value distribution of economic losses due to outbreak catastrophe that can be used by reinsurance companies as a tool in determining the value of risk in the outbreak catastrophe bonds.
Estimation of the Extreme Distribution Model of Economic Losses Due to Outbreaks Using the POT Method with Newton Raphson Iteration Riza Adrian Ibrahim; Sukono Sukono; Riaman Riaman
International Journal of Quantitative Research and Modeling Vol. 2 No. 1 (2021): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v2i1.118

Abstract

Extreme distribution is the distribution of a random variable that focuses on determining the probability of small values in the tail areaof the distribution. This distribution is widely used in various fields, one of which is reinsurance. An outbreak catastrophe is non-natural disaster that can pose an extreme risk of economic loss to a country that is exposed to it. To anticipate this risk, the government of a country can insure it to a reinsurance company which is then linkedto bonds in the capital market so that new securities are issued, namely outbreakcatastrophe bonds. In pricing, knowledge of the extreme distribution of economic losses due to outbreak catastrophe is indispensable. Therefore, this study aims to determine the extreme distribution model of economic losses due to outbreak catastrophe whose models will be determined by the approaches and methods of Extreme Value Theory and Peaks Over Threshold, respectively. The threshold value parameter of the model will be estimated by Kurtosis Method, while the other parameters will be estimated with Maximum Likelihood Estimation Method based on Newton-Raphson Iteration. The result of the research obtained is the resulting model of extreme value distribution of economic losses due to outbreak catastrophe that can be used by reinsurance companies as a tool in determining the value of risk in the outbreak catastrophe bonds.
Transforming Digital Pedagogy in Primary School Teacher Education (PGSD) to Enhance Learning Quality in the Post-Pandemic Era Riza Adrian Ibrahim; Alim Jaizul; Yasir Salih
International Journal of Ethno-Sciences and Education Research Vol. 6 No. 3 (2026): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v6i3.1292

Abstract

The transformation of digital pedagogy has become a crucial aspect of improving the quality of education, particularly in the post-pandemic era. This study aims to analyze the role of digital pedagogy in Primary School Teacher Education (PGSD), focusing on its potential to enhance teaching effectiveness, student engagement, and learning outcomes. The research employs a qualitative approach through a systematic literature review of scholarly articles and educational reports published between 2021 and 2026. The findings reveal that digital pedagogy supports interactive learning environments, promotes student-centered approaches, and strengthens teachers’ instructional competencies. However, challenges such as limited digital infrastructure, insufficient teacher readiness, and gaps in digital literacy persist. Furthermore, the shift toward hybrid and technology-based learning requires continuous adaptation from both educators and institutions. Therefore, integrating digital pedagogy into PGSD curricula is essential to prepare future teachers who are innovative, adaptive, and technologically competent. The study concludes that strategic policy support, infrastructure development, and ongoing professional training are key factors in optimizing digital pedagogy implementation in primary education.
Quantitative Analysis of Risk Factors Causing Delays in Road Infrastructure Projects Using the SEM (Structural Equation Modeling) Method Riza Adrian Ibrahim; Astrid Sulistya Azahra; Nurnisaa binti Abdullah Suhaimi
International Journal of Quantitative Research and Modeling Vol. 7 No. 3 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i3.1444

Abstract

Road infrastructure projects are vulnerable to schedule disruption arising from interacting managerial, resource, technical, financial, and external conditions. However, conventional delay studies often identify or rank individual factors without estimating their structural contribution. This study develops and tests an integrated model of seven risk dimensions affecting road infrastructure project delay using Structural Equation Modeling. An explanatory design was implemented through a cross-sectional survey of 260 professionals involved in road infrastructure projects. The measurement model was assessed using indicator loadings, Cronbach’s alpha, composite reliability, average variance extracted, and the Heterotrait–Monotrait criterion, while the structural model was evaluated through path coefficients, collinearity diagnostics, bootstrapping, and explanatory power. The measurement results demonstrated reliability, convergent validity, and discriminant validity across all constructs, with all 42 indicators retained. The structural model showed that Project Management Risk was the strongest predictor of Project Delay (β = 0.458, p < 0.001), followed by External Risk (β = 0.227, p = 0.0006), Human Resource Risk (β = 0.152), Financial Risk (β = 0.143), and Material Risk (β = 0.139). Technical/Design Risk and Equipment Risk did not demonstrate significant effects. Collectively, the seven risk dimensions explained 69.5% of the variance in Project Delay, with Project Management Risk contributing 43.6% of explained variance. The findings establish that road-project delays are principally associated with management and external conditions, supporting risk mitigation strategies that prioritize governance, coordination, monitoring, decision-making, and contingency planning.
Co-Authors Alim Jaizul Astrid Sulistya Azahra Nurnisaa binti Abdullah Suhaimi Riaman Riaman Sukono Sukono Yasir Salih