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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi
Safrizal Ardana Ardiyansa
Brawijaya University
Mathematics

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Adaptive Portfolio Rebalancing for NASDAQ-100 Stocks with Hippopotamus Optimization Algorithm under Transaction Costs Safrizal Ardana Ardiyansa; Mohamad Muslikh; Syaiful Anam
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.43876

Abstract

This study proposes a transaction cost-aware portfolio optimization framework for NASDAQ-100 stocks based on the Hippopotamus Optimization Algorithm (HOA). The mathematical model extends the classical mean-variance framework by incorporating transaction costs into a Net Sharpe Ratio (NSR) objective function. Daily adjusted closing prices of NASDAQ-100 constituent stocks from 2021 to 2025 are employed, with the twenty highest-ranked stocks according to the Sharpe Ratio (SR) selected as the candidate investment universe. Each optimization experiment is independently repeated 25 times to evaluate robustness and solution stability. The results indicate that the proposed HOA consistently achieves competitive optimization performance with very small variability across repeated runs throughout the investment horizon. Although several competing algorithms attain comparable or slightly better objective values during particular rebalancing periods, HOA remains among the strongest-performing methods while exhibiting stable convergence behavior. Throughout the investment horizon, the proposed algorithm produces the highest average cumulative portfolio value of \$5,307.87 \$125.71 from an initial investment of \$1,000, corresponding to an average annual return of 40.03% 0.68%, together with the highest average SR and NSR of 1.5736 0.0151, while maintaining competitive maximum drawdown and transaction costs. These findings demonstrate that incorporating transaction costs directly into the optimization objective enables HOA to achieve a favorable balance between portfolio growth, risk-adjusted performance, and trading efficiency under dynamic market conditions.
Health Insurance Claim Classification using Support Vector Machine with Velocity Pausing Particle Swarm Optimization Luh Putu Dharma Jayanti; Syaiful Anam; Safrizal Ardana Ardiyansa; Natasha Clarissa Maharani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.31914

Abstract

classification is a serious problem. Identifying claim classification is difficult. Machine Learning (ML) can predict potential claim decisions. Support Vector Machine (SVM) is a ML model that can generalize well to test data. SVM achieves an -score of 73.39% and 89.88% with a linear kernel, 73.34% and 73.34% with Radial Basis Function (RBF) kernel. Particle Swarm Optimization (PSO) improves the performance, because it can find the best parameters for SVM. However, the SVM parameters found by PSO are not guaranteed to be the global optimum. Velocity Pausing PSO (VPPSO) can address this problem. SVM-VPPSO performs better compared with SVM and SVM-PSO. SVM-VPPSO with linear kernel achieves -score of 90.17%, 90.16%, and 90.06% with 10, 20, and 30 particles respectively. The linear kernel also performs better than RBF kernel with a difference of 0.39% on the testing data. The best configuration is SVM-Linear-VPPSO with 10 particles. This configuration also achieves computation time of 46.938 seconds, which faster than SVM-Linear-VPPSO with 20 particles. The variance in computational time with 10 particles is 1.832 seconds, which better than with 20 particles with variance of 37.909 seconds.
An Explainable Deep Learning Approach for Brain Tumor Detection Using MobileNet and Grad-CAM Visualization Amalan Fadil Gaib; Safrizal Ardana Ardiyansa; Anggito Karta Wijaya; Eric Julianto; I Gusti Ngurah Bagus Ferry Mahayudha; Ando Zamhariro Royan
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35901

Abstract

Brain tumor detection remains a significant challenge due to the complex variations in tumor appearance. Although deep learning models have demonstrated high accuracy, their limited interpretability hinders clinical adoption. To address this issue, this study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) into Convolutional Neural Networks (CNNs) to enhance the visual interpretability of predictions. Grad-CAM extends Class Activation Mapping (CAM) and is applicable to a wide range of deep learning architectures. The primary contribution of this work is the demonstration that combining Grad-CAM with MobileNet architectures yields an interpretable and efficient framework for diagnosis of brain tumor, effectively balancing accuracy, computational efficiency, and clinical transparency. Using a Brain Tumor MRI dataset, MobileNetV4 achieved an accuracy of 98.29% with the shortest training time (1738.82 seconds) and an ROC accuracy of 99.96%. MobileNetV3 achieved 99.62% accuracy with an ROC accuracy of 99.92%. Grad-CAM effectively highlighted tumor regions while showing uniform attention in non-tumor cases, thereby reducing false positives. These results demonstrate that lightweight models can achieve a strong balance between predictive performance, training efficiency, and interpretability. The proposed framework thus supports the development of explainable and efficient diagnostic tools for clinical practice.
Co-Authors Amalan Fadil Gaib Anam, Syaiful Ando Zamhariro Royan Anggito Karta Wijaya Eric Julianto I Gusti Ngurah Bagus Ferry Mahayudha Luh Putu Dharma Jayanti Mohamad Muslikh Natasha Clarissa Maharani