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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement)
Tantrawan, Made
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Decision Sciences, Operations Research & Management Mathematics

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Mathematics in Cryptography and Its Applications in Cybersecurity Isnaini, Uha; Qomariasih, Nurul; Amiruddin, Amiruddin; Tantrawan, Made; Arizal, Arizal; Ernanto, Iwan; Adiati, Nadia Paramita Retno; Indarsih, Indarsih; Yasa, Ray Novita; Wijayanti, Indah Emilia; Indarjani, Santi; Salmah, Salmah; Ulfa, Septia; Hartanto, Ari Dwi; Amelia, Fetty
Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement) Vol 11, No 4 (2025): December
Publisher : Direktorat Pengabdian kepada Masyarakat Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpkm.101525

Abstract

The growing prevalence of cyber threats and attacks poses significant risks to the security of personal data and the integrity of sensitive information worldwide. Cryptography plays a vital role in establishing strong cybersecurity defenses, and the development of robust cryptographic algorithms is essential for protecting data against cyber-attacks. This workshop aimed to enhance participants’ understanding of the mathematical foundations of cryptographic algorithms and equip them with practical skills to identify and mitigate cyber threats. It also introduced innovative educational tools, including an Augmented Reality (AR) application for teaching classical cryptography and a Game-Based URL Phishing Education application. A total of 110 participants attended and completed the pre-test. The post-test measured knowledge gained during the workshop, and an accompanying survey gathered feedback on its effectiveness and identified areas for improvement. Overall, the workshop successfully achieved its objectives by educating participants on cryptography in the Internet of Things (IoT), increasing awareness of social engineering, introducing cryptographic tools from ancient to modern times, and exploring the principles of quantum cryptography.
From Risk-Neutral to Risk-Sensitive Reinforcement Learning: Actor–Critic vs REINFORCE with Tail-Based Risk Measures Lestia, Aprida Siska; Effendie, Adhitya Ronnie; Tantrawan, Made; Azrarsyah, Muhammad Rafli
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40309

Abstract

his study investigates the application of \emph{risk-sensitive reinforcement learning} on heavy-tailed return series by comparing two primary algorithms: REINFORCE with baseline (REINFORCE-BL) and episodic batched actor--critic (A2C-B). Initial exploratory analysis reveals an asymmetric return distribution with numerous extreme \emph{outliers}, rendering variance-based risk measures inadequate and motivating the integration of tail-based risk measures—specifically Value at Risk (VaR), Conditional Value at Risk (CVaR), and Entropic Value at Risk (EVaR)—into the RL objective function. This study constructs a simple portfolio environment with discrete actions (market entry, market exit, and \emph{hold}) and trains both algorithms under four scenarios: risk-neutral, VaR, CVaR, and EVaR. Experimental results demonstrate that A2C-B consistently outperforms REINFORCE-BL across all scenarios, exhibiting higher average long-term rewards, faster convergence rates, and more stable \emph{learning curves}. While VaR and CVaR penalties significantly reduce rewards and increase learning volatility for REINFORCE-BL, A2C-B experiences only moderate reward reductions while maintaining stability. In the EVaR scenario, both algorithms yield high rewards, yet A2C-B retains a slight advantage in terms of stability. These findings indicate that in environments with heavy-tailed returns, employing coherent risk measures (particularly CVaR and EVaR) within an actor--critic framework offers a more compelling trade-off between tail risk control and average performance, serving as a viable \emph{baseline} for the development of risk-sensitive RL in finance and actuarial science.
Co-Authors Adhitya Ronnie Effendie, Adhitya Ronnie Adiati, Nadia Paramita Retno Amiruddin Amiruddin Arizal, Arizal Azrarsyah, Muhammad Rafli Fetty Amelia Hartanto, Ari Dwi Indah Emilia Wijayanti Indarjani, Santi indarsih, Indarsih Lestia, Aprida Siska Qomariasih, Nurul Ray Novita Yasa Salmah Salmah Uha Isnaini Ulfa, Septia