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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Advances in Applied Sciences Journal of Education and Learning (EduLearn)
Mat Diah, Norizan
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Humanities Automotive Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Sudoku solutions: a comparative analysis of breadth-first search, depth-first search, and human approaches Mat Diah, Norizan; Riza, Syahirul; Ahmad, Suzana; Musa, Norzilah; Hashim, Shakirah
Journal of Education and Learning (EduLearn) Vol 19, No 1: February 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v19i1.21214

Abstract

Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the result will always be the same. The player thought that the number of givens or clues, the initial value on the Sudoku puzzles, would significantly determine the difficulty level, which is not necessarily correct. This research uses two search algorithms, breadth-first search (BFS) and depth-first search (DFS), to solve a set of Sudoku puzzles based on the number of givens. The Sudoku puzzles are chosen based on the number of givens between 32 and 35. In cases where Sudoku puzzles are considered medium or intermediate difficulty, the solutions generated by both algorithms will be compared with the human-solving approach. The research aims to determine whether humans tend to solve Sudoku puzzles with solutions resembling those generated by BFS or DFS. Furthermore, if all three approaches-human, BFS, DFS-yield comparable solutions, the Sudoku puzzle has only one unique solution.
Betta fish species classification using light weight deep learning algorithm Muhaimin Lim, Danishah Hana Muhammad; Mat Diah, Norizan; Ibrahim, Zaidah; Kasiran, Zolidah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp28-38

Abstract

Betta fish sellers and breeders often face challenges in accurately identifying Betta fish species due to variations in colors, patterns, and shapes, leading to potential financial losses and deceptive transactions. To address this issue, we developed a mobile application that employs MobileNet, a deep learning (DL) technique, to classify Betta fish species. The dataset, acquired from online stores, comprises 400 images, with 100 images representing each of the four studied Betta fish species: comb tail, delta tail, spade tail, and veil tail. Prior to model implementation, the dataset undergoes pre-processing with data augmentation techniques, including rotation, shear, zoom-in, horizontal flip, and brightness adjustments, enhancing the model performance. Training utilizes 80% of the data, with the remaining 20% allocated for testing. Three distinct MobileNet models are developed for males, females, and both genders combined, achieving accuracies of 70, 83.75, and 65%, respectively. These trained models are the foundation for a mobile application developed for the Android platform that enables users, particularly Betta fish sellers, and breeders, to efficiently classify Betta fish species, empowering them to set accurate prices based on the identified species.
Faraid distribution calculation using AI-based Quranic chatbot Md Zin, Iman Hafizi; Mansor, Nur Farraliza; Mat Diah, Norizan; Hashim, Shakirah; Mansor, Mastura
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp393-406

Abstract

Faraid, Islamic inheritance law, refers to that aspect of Shariah law which is not properly understood and has created issues and impediments in the distribution of estates. This paper discusses the development of an AI-based Quranic chatbot to be used by the public to learn the Faraid rules and automate calculations of inheritance distribution. The chatbot has been developed using natural language processing and a rule-based algorithm, which intends to search and get an accurate interpretation from the user queries, retrieve relevant verses of the Quran, and compute the share of inheritance according to the established Islamic jurisprudence. Fuzzy match identifies and corrects variation in queries, enhancing user interaction, ensuring that it appears more intuitive and accessible. The system processes user input regarding heirs of the deceased, estate value, and debts, and applies Faraid rules to generate accurate distribution results that happen to be web-based platforms of this chatbot. It intends to link traditional Islamic knowledge with modern digital solutions, bringing Faraid calculations closer, more comfortable, faster, and transparent. Through rigorous tests and user feedback will prove above, revealing the chatbot’s potential in understanding the application of Islamic inheritance law and promoting digital engagement in all these through Quranic teachings.
Co-Authors Ahmad, Suzana Hashim, Shakirah Ibrahim, Zaidah Kasiran, Zolidah Mansor, Mastura Mansor, Nur Farraliza Md Zin, Iman Hafizi Muhaimin Lim, Danishah Hana Muhammad Musa, Norzilah Riza, Syahirul