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All Journal PROCEEDING IC-ITECHS 2014
Din, Mazura Mat
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Computer Science & IT Education

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Heartcare: Predictive Analytics for Early Detection and Prevention Rosli, Daniyal; Din, Mazura Mat; Idrus, Zanariah
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1672

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, often due to late detection and prevention. Heartcare aims to leverage predictive analytics to facilitate early detection and prevention of heart diseases. By integrating machine learning algorithms such as Decision Trees, Random Forests, and Logistic Regression, Heartcare provides healthcare professionals with a powerful tool for patient health monitoring. This study focuses on developing a predictive model to assess heart disease risk using patient-specific data, such as age, sex, BMI, and lifestyle factors. The outcomes will enable healthcare professionals to make informed decisions, potentially saving lives and reducing healthcare costs.
Cryptocurrency Price Forecasting Using ARIMAX: Conceptual Framework Mohamad Fuad, Muhammad Fauzan; Din, Mazura Mat
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1676

Abstract

Cryptocurrency, a digital or virtual currency secured by cryptography, has become a dynamic and volatile asset class, presenting both opportunities and challenges for traders and investors. This study aims to develop a web-based application prototype for forecasting Bitcoin prices using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The ARIMAX model, known for its ability to integrate external factors into time series forecasting, is applied to historical Bitcoin price data, utilizing the Crypto Fear and Greed Index as an exogenous variable. By addressing the inherent volatility and unpredictability of Bitcoin's price movements, this study seeks to enhance the reliability of price predictions. Model performance will be evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The resulting web application will deliver real-time price forecasts and analyses, empowering users to make informed decisions and manage risks more effectively in cryptocurrency trading. This research ultimately aims to contribute to advancements in predictive modeling techniques within financial technology, providing traders and investors with a valuable tool for navigating the complex cryptocurrency market.
Automated Recognition of Medicinal Plants in the Wild: A Leaf-centric Approach Ahmad Zaki, Muhammad Ammar; Mohd Zukhi, Mohd Zhafri; Din, Mazura Mat
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1680

Abstract

This study explores the use of technology to simplify the identification of medicinal plants in the wild by focusing on leaf characteristics. Using convolutional neural networks (CNNs), the research aims to develop a mobile-friendly system tailored to Malaysia’s rich biodiversity and traditional medicine heritage. Key steps include collecting a diverse range of plant data, enhancing image quality through pre-processing, and testing various CNN models to determine the most effective one. Designed for use by both experts and non-experts, such as rural communities and herbalists, the tool integrates advanced AI with traditional knowledge to preserve cultural practices, promote safe natural remedies, and raise awareness about medicinal plants’ role in healthcare and conservation. By addressing the decline in herbal knowledge, this project aims to deliver a practical and accessible solution that supports public health and environmental sustainability.
Co-Authors Ahmad Zaki, Muhammad Ammar Idrus, Zanariah Mohamad Fuad, Muhammad Fauzan Mohd Zukhi, Mohd Zhafri Rosli, Daniyal