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All Journal Operations Research: International Conference Series International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research International Journal of Mathematics, Statistics, and Computing International Journal of Humanities, Law, and Politics International Journal of Health, Medicine, and Sports International Journal of Linguistics, Communication, and Broadcasting
Pirdaus, Dede Irman
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Operations Research: International Conference Series

 1 
Health Sector Portfolio Optimization Using the Markowitz Approach with Risk Aversion and Risk Tolerance Parameters Ismail, Muhammad Iqbal Al-Banna; Pirdaus, Dede Irman
Operations Research: International Conference Series Vol. 5 No. 4 (2024): Operations Research International Conference Series (ORICS), December 2024
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v5i4.352

Abstract

This study analyzes the optimal portfolio formation of health sector stocks listed on the Indonesia Stock Exchange using the Markowitz approach with dual risk parameters. Unlike traditional mean-variance optimization, this research incorporates both risk aversion (ρ) and risk tolerance (τ) parameters to better accommodate varying investor risk preferences. Using daily closing price data from six health sector stocks during the period January 2022 to December 2023, this study employs web scraping techniques for data collection and implements portfolio optimization calculations. The results show that the dual risk parameters approach produces consistent portfolio weights across both risk measures, with SIDO.JK receiving the highest allocation (approximately 41.6%) followed by SOHO.JK (23.0%) and SILO.JK (16.9%). The efficient frontier analysis demonstrates portfolio risk ranges from 0.015 to 0.030 with returns between 0.10% to 0.45%. This study contributes to the literature by demonstrating how incorporating dual risk parameters can provide more nuanced portfolio allocations while maintaining the fundamental benefits of diversification.
Implementing EfficientNetB0 for Facial Recognition in Children with Down Syndrome Pirdaus, Dede Irman; Dwiputra, Muhammad Bintang Eighista; Saputra, Moch Panji Agung
Operations Research: International Conference Series Vol. 6 No. 2 (2025): Operations Research International Conference Series (ORICS), June 2025
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v6i2.383

Abstract

Early detection of Down Syndrome in children is crucial to provide more appropriate medical and educational interventions. This study aims to build and evaluate a deep learning-based classification model using the EfficientNetB0 architecture to distinguish facial images of children with Down Syndrome and healthy children. The dataset used consists of two classes (Down Syndrome and healthy), which have gone through an augmentation process to increase data diversity and prevent overfitting. The model was trained using the Adam algorithm with a learning rate of 0.0001 and a sparse categorical crossentropy loss function for 10 epochs. The training results showed that the model achieved a validation accuracy of 93.94%, with the lowest validation loss value of 0.2390. Further evaluation was carried out using a confusion matrix, which showed that the model was able to properly classify 312 out of 333 Down Syndrome images and 309 out of 330 healthy children images, resulting in an overall accuracy of 94%. In addition, the precision, recall, and f1-score values ​​for both classes were in the range of 0.94, indicating a balanced and strong performance. Visual analysis of the misclassified images indicates that some misclassifications occur on healthy children’s faces with certain expressions, angles, or lighting conditions that resemble Down syndrome. Conversely, some children with Down syndrome are also predicted as healthy when their facial features are not too prominent or similar to normal children under certain lighting conditions. This shows that despite the high performance of the model, sensitivity to facial feature variations remains a challenge.
Co-Authors Agung Prabowo Amal, Moh Alfi AS, Nurnisaa Azahra, Astrid Sulistya Chayandari, Rini Dwiputra, Muhammad Bintang Eighista Hidayana, Rizki Apriva Ibrahim, Riza Andrian Ismail, Muhammad Iqbal Al-Banna kalfi kalfin Kalfin Laksito, Grida Saktian Lianingsih, Nestia Rusyn, Volodymyr Salih, Yasir Saputra, Jumadil Saputra, Renda Sandi Sidiq, Fahmi Surya, Dhika Vera Yohandoko, Setyo Luthfi Okta Yuningsih, Siti Hadiaty