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Mat Din, Mazura
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Computer Science & IT Education

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QuranVision Zamzuri, Hariz; Mat Din, Mazura; Ali, Noor Rasidah
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.v5i1.1666

Abstract

Tajweed is a set of rules, which is required for every Muslim to learn in order to recite the holy Quran. These rules are needed to guide Quran’s reciters from making any errors, such as mispronouncing words which are strictly prohibited when reciting Quran. The conventional learning process on Tajweed rules, which comes in the form of face-to-face learning between instructor and the students, although considered the most effective way to learn Quran, may be tedious and time consuming due to the need for prolonged session of direct contact for learning. On that note, a mobile application which can detect and recognize the feature of Tajweed rules via image processing in al-Quran using a deep learning technology is proposed. Convolutional Neural Network or CNN is the commonly used technology when it comes to object detection and image classification. Model based on CNN architectures will be utilized in developing a real-time detection and recognition mobile application, focusing on Meem Sakinah-based Tajweed rules which include Idgham, Ikhfa’ and Izhar Shafawee. The key benefits of this application is in its ability to detect Tajweed rules in a real-time scenario and works in both black and white and color-coded Quran. This application provides an alternative way for new Quran’s learners from varying backgrounds and age to learn about Tajweed rules on their own time through visual and audio learning as well as detailed descriptions of the Tajweed rules which can aid in understanding the material more effectively.
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.
Early Detection on Company Bankruptcy: a Comparison of Neural Networks and Logistic Regression Ahmad Shukri, Muhammad Fairus; Abdul Razak, Nor Hafizah; 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.v5i1.1678

Abstract

Detecting firm insolvency at an early stage is crucial for financial analysis and risk management. This study compares the efficacy of two widely used bankruptcy prediction techniques: Neural Networks (NN) and Logistic Regression (LR). We evaluate each approach based on its accuracy, computing efficiency, and interpretability, aiming to identify a suitable predictive model that aligns with specific objectives, data characteristics, and the need for interpretability in financial decision-making. This research indicates that NN provides superior prediction accuracy but is accompanied by increased computing demands and reduced interpretability. In contrast, LR offers more speed, requires fewer processing resources, and provides explicit understanding of variable correlations; however, it may not perform well with intricate and nonlinear data. This study confirms the significance of choosing a suitable predictive model that balances competing demands of accuracy, efficiency, and interpretability in financial decision-making.
Deep Learning for Meal Recognition and Calorie Estimation Ahmad Fariz, Ahmad Nabil Bin; Abdul Razak, Nor Hafizah; 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.v5i1.1679

Abstract

Accurate calorie estimates from foods are prerequisite for diet following and health monitoring. Manual calorie estimations according to age-old methods mostly tend to be inaccurate. This paper proposes the use of convolutional neural networks (CNNs) for precise identification from food images and prediction of meal calories to solve the concern. Therefore, the objective is to create a model capable of recognizing foodstuff besides estimating their caloric content. Developing a model that could correctly identify food ingredients and calculate their energy value through training and testing was important in this project. Our aim here was to verify the accuracy of the model using systematic reviewing means as well as an interface where it can be tested. A dataset of 1,337 high-quality images divided into 12 culinary classes cake, hamburger, noodles, spaghetti, pizza, chicken curry, croissant, French fries, fried chicken, roast chicken, lobster nasi goreng, and waffle was obtained from Roboflow Universe and used for this project. The selection of technique which is YOLO (You Only Look Once) model architecture and flow design because it proved to be highly efficient for real-time object recognition.
Co-Authors Abdul Razak, Nor Hafizah Ahmad Fariz, Ahmad Nabil Bin Ahmad Shukri, Muhammad Fairus Ali, Noor Rasidah Mohd Asni, Muhammad Luqman Hakim Mohd Zukhi, Mohd Zhafri Zamzuri, Hariz