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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi International Journal of Quantitative Research and Modeling International Journal of Health, Medicine, and Sports
Yasmin, Arla Aglia
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Engineering Environmental Science Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health

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Determination of Dominant Factors Affecting Lung Cancer Patients Using Principal Component Analysis (PCA) Amal, Moh Alfi; Suhaimi, Nurnisaa binti Abdullah; Yasmin, Arla Aglia
International Journal of Quantitative Research and Modeling Vol 5, No 3 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i3.747

Abstract

The diagnosis of lung cancer is one of the most pressing health issues as the disease is often only detected at an advanced stage, leading to a poor prognosis for patients. Therefore, in an effort to detect, prevent, and manage the disease more effectively, this study utilized screening variables. Screening is an important endeavor in the early detection of diseases or abnormalities that are not yet clinically apparent using various tests, examinations, or procedures. The use of screening variables is very important in the early detection process because it can help in this study to understand the risk factors and causes of disease. The purpose of this study is to determine the dominant factors affecting people with lung cancer using Principal Component Analysis (PCA). Based on the results of the study, it was found that there are 20 dominant screening variables that have a considerable correlation to the formation of early detection of lung cancer with a total proportion of covariance variance of 100% when, the total variance obtained from the 20 screening variables is 100%. The final PCA results show that the factor loading values are used to determine which variables are most influential by comparing the magnitude of the correlation. As a result, the main factor causing lung cancer was Fatigue which had a factor loading of 7.87%, followed by the variables Age and Alcohol use with a factor loading of 6.02%. Other variables also showed certain factor loadings that indicated the cause of lung cancer. These findings are very important in efforts to improve early detection and management of lung cancer through more effective and targeted screening.
Exploring the Relationship between Nicotine Dependence and Triglyceride Levels among Male Smokers with Hypertension Yasmin, Arla Aglia; Pirdaus , Dede Irman; Abdul Halim , Nurfadhlina
International Journal of Health, Medicine, and Sports Vol. 2 No. 2 (2024): International Journal of Health, Medicine, and Sports
Publisher : Corespub

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhms.v2i2.100

Abstract

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with smoking identified as a significant risk factor. This study investigates the impact of nicotine dependence on triglyceride levels among male smokers diagnosed with hypertension. Blood samples and Fagerstrom Test for Nicotine Dependence (FTND) scores were collected from 31 participants aged 30-65 years at a community health center. Triglyceride levels were analyzed alongside demographic and smoking-related variables. While age, cigarettes per day (CPD), and cigarette type showed no significant association with triglyceride levels, individuals with low nicotine dependence exhibited a trend towards higher triglyceride levels. However, this association was not statistically significant. Uncontrolled confounding variables like BMI, diet, and physical activity may have influenced the results. Further research with larger sample sizes and robust controls is necessary to clarify these relationships definitively
Development and Evaluation of Effervescent Powder Formulations Combining Red Ginger Extract and Honey Azahra, Astrid Sulistya; Prabowo, Agung; Yasmin, Arla Aglia
International Journal of Health, Medicine, and Sports Vol. 2 No. 2 (2024): International Journal of Health, Medicine, and Sports
Publisher : Corespub

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijhms.v2i2.101

Abstract

Effervescent powder formulations combining red ginger (Zingiber officinale var. rubrum) extract and honey were developed and evaluated for their physical, chemical, and antibacterial properties. The formulations were prepared using wet granulation method, and their organoleptic properties, moisture content, flow characteristics, dispersibility, foam height, pH, and hedonic responses were assessed. Additionally, the antibacterial activity of the formulations against Streptococcus pyogenes was evaluated using agar diffusion method. The results showed that the formulations exhibited variations in color, odor, taste, and physical form, with formulation F3 containing 10% red ginger extract demonstrating the best sensory attributes. All formulations met the moisture content and flow time criteria, indicating good stability and handling properties. They also displayed rapid dispersibility and proper foam height upon dissolution. The pH values fell within the acceptable range for consumption. However, none of the formulations showed significant antibacterial activity against Streptococcus pyogenes. Further optimization may be required to enhance the formulations' antibacterial efficacy.
Investment Portfolio Optimization In Infrastructure Stocks Using The Mean-VaR Risk Tolerance Model Yasmin, Arla Aglia; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i1.602

Abstract

Infrastructure a crucial role in economic development and the achievement of Sustainable Development Goals (SDGs), with investment being a key activity supporting this. Investment involves the allocation of assets with the expectation of gaining profit with minimal risk, making the selection of optimal investment portfolios crucial for investors. Therefore, the aim of this research is to identify the optimal portfolio in infrastructure stocks using the Mean-VaR model. Through portfolio analysis, this study addresses two main issues: determining the optimal allocation for each infrastructure stock and formulating an optimal stock investment portfolio while minimizing risk and maximizing return. The methodology employed in this research is the Mean-VaR approach, which combines the advantages of Value at Risk (VaR) in risk measurement with consideration of return expectations. The findings indicate that eight infrastructure stocks meet the criteria for forming an optimal portfolio. The proportion of each stock in the optimal portfolio is as follows: ISAT (2.74%), TLKM (33.894%), JSMR (3.343%), BALI (0.102%), IPCC (5.044%), KEEN (14.792%), PTPW (25.863%), and AKRA (14.219%). The results of this study can serve as a foundation for better investment decision-making.
Optimization Modeling of Investment Portfolios Using The Mean-VaR Method with Target Return and ARIMA-GARCH Yasmin, Arla Aglia; Riaman, Riaman; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): 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.v10i1.30042

Abstract

This research develops a portfolio optimization model using the Mean-Value at Risk (Mean-VaR) approach with a target return constraint, addressing the gap in models that specific return objectives. The ARIMA-GARCH model is utilized to predict stock returns and volatility, offering precise inputs for optimization. By applying the Lagrange method and Kuhn-Tucker conditions, the model determines optimal portfolio weights that balance risk and return. Using data from infrastructure stocks on the Indonesia Stock Exchange (January 2019-September 2024), the model’s effectiveness is validated through numerical simulations. The results illustrate efficient frontiers for target returns of 5x10^-6, 0.001, and 0.0019, revealing that higher return targets proportionally increase risk. ARIMA-GACRH’s advantage lies in its ability to capture both mean and variance dynamics, ensuring reliable volatility estimates for informed decision-making. This study contributes to portfolio optimization literature by emphasizing target return constraints and demonstrating the practical utility of volatility modeling. The findings provide a robust framework for investors to align portfolios with financial goals and risk tolerance. Future work could explore broader market contexts or integrated additional constraints for enhanced applicability.
Co-Authors Abdul Halim , Nurfadhlina Agung Prabowo Amal, Moh Alfi Azahra, Astrid Sulistya Pirdaus , Dede Irman Riaman Riaman, Riaman Suhaimi, Nurnisaa binti Abdullah Sukono .