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All Journal PROCEEDING IC-ITECHS 2014
Mohd Zukhi, Mohd Zhafri
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Computer Science & IT Education

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Early Prediction of Mental Health Disorder Among Higher Education Students Using Machine Learning Mohd Asni, Muhammad Luqman Hakim; Mohd Zukhi, Mohd Zhafri; Mat Din, Mazura
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.v5i1.1674

Abstract

In spite of the fact that mental health illnesses are quite common among students in higher education, early detection continues to be a difficult task. This study seeks to determine the use of machine learning to forecast the occurrence of mental health issues in this group. Various machine learning methods were explored to analyze the data collected from higher education students and to identify potential risk factors associated with mental health issues. Through the development of a model that is capable of accurately predicting the risk of mental health illnesses, the project intends to facilitate early intervention and improve the overall well-being of their student population.
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 Din, Mazura Mat Mat Din, Mazura Mohd Asni, Muhammad Luqman Hakim