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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Shuzlina Abdul-Rahman
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering Engineering

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Predictive analytics of university student intake using supervised methods Muhammad Yunus Iqbal Basheer; Sofianita Mutalib; Nurzeatul Hamimah Abdul Hamid; Shuzlina Abdul-Rahman; Ariff Md Ab Malik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.053 KB) | DOI: 10.11591/ijai.v8.i4.pp367-374

Abstract

Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia (SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.
Visual analytics of 3D LiDAR point clouds in robotics operating systems Alia Mohd Azri; Shuzlina Abdul-Rahman; Raseeda Hamzah; Zalilah Abd Aziz; Nordin Abu Bakar
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.326 KB) | DOI: 10.11591/eei.v9i2.2061

Abstract

This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on simultaneous localization and mapping (SLAM) using point cloud data derived from the light detection and ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as grid mapping and monte carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on real-time appearance-based (RTAB) and makes use of the red green blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the robot operating system (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.
Heuristic based model for groceries shopping navigator Muhammad Wardi bin Peeyee; Shuzlina Abdul-Rahman; Nurzeatul Hamimah Abdul Hamid; Mohd Zaki Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp932-940

Abstract

This paper presents a heuristic based model for groceries shopping navigator that attempts to improve the navigation problem that usually face by customers while doing their shopping. A system known as Shopping Navigator or shortly SHoNa was developed to give the optimal sequence of shelves to be visited by the customer and the total estimated shopping time so that the user can plan their shopping task earlier. Genetic algorithm was employed and implemented in a web-based platform that is compatible with other devices such as smartphones and tablets. SHoNA can minimize the shopping time by identifying the most optimal order of shelves inside the supermarket that needs to be visited by the customer. A series of experimental was performed in producing the optimum model. Our findings showed that the combination of order one crossover and inverse mutation produced a better optimal performance, which is the minimum total amount of groceries shopping time.  SHoNA can be further enhanced with visualization features for a better shopping experience.
Simulation of simultaneous localization and mapping using 3D point cloud data Shuzlina Abdul-Rahman; Mohamad Soffi Abd Razak; Aliya Hasanah Binti Mohd Mushin; Raseeda Hamzah; Nordin Abu Bakar; Zalilah Abd Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp941-949

Abstract

Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology.  Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.
Analytics of stock market prices based on machine learning algorithms Puteri Hasya Damia Abd Samad; Sofianita Mutalib; Shuzlina Abdul-Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp1050-1058

Abstract

This study focuses on the use of machine learning algorithms to analyse financial news on stock market prices. Stock market prediction is a challenging task because the market is known to be very volatile and dynamic. Investors face these kinds of problems as they do not properly understand which stock product to subscribe or when to sell the product with an optimum profit. Analyzing the information individually or manually is a tedious task as many aspects have to be considered. Five different companies from Bursa Malaysia namely CIMB, Sime Darby, Axiata, Maybank and Petronas were chosen in this study. Two sets of experiments were performed based on different data types. The first experiment employs textual data involving 6368 articles, extracted from financial news that have been classified into positive or negative using Support Vector Machine (SVM) algorithm. Bags of words and bags of combination words are extracted as the features for the first experiment. The second experiment employs the numeric data type extracted from historical data involving 5321 records to predict whether the stock price is going up (positive) or down (negative) using Random Forest algorithm. The Rain Forest algorithm gives better accuracy in comparison with SVM algorithm with 99% and 68% accuracy respectively. The results demonstrate the complexities of the textual-based data and demand better feature extraction technique.
A review on object detection for autonomous mobile robot Syamimi Abdul-Khalil; Shuzlina Abdul-Rahman; Sofianita Mutalib; Saidatul Izyanie Kamarudin; Siti Sakira Kamaruddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1033-1043

Abstract

The advancement of autonomous mobile robots (AMR) is vastly being discovered and applied to several industries. AMR contributes to the development of artificial intelligence (AI), which focuses on the growth of human-interaction systems. However, it is safe to understand that mobile robots work closely in real-time and under changing surroundings. Similarly, some limitations may affect the efficiency of mobile robots. Thus, to improve the system's efficiency and accuracy, mobile robots should adopt the ability to detect incoming obstacles accurately. This paper presents the findings of a brief technology review aimed at identifying the current state of the art and future needs for AMR in object detection. This review paper is presented in the form of a narrative-literature review. Review articles were collected from 2015 until 2022 from journals or conference papers from well-known sources like IEEE Xplore, Science Direct, Scopus, and Web of Science (WOS). The analysis of the articles was discussed in four main topics, AI, object detection, AMR, and deep learning.
Co-Authors Alia Mohd Azri Aliya Hasanah Binti Mohd Mushin Ariff Md Ab Malik Mohamad Soffi Abd Razak Mohd Zaki Zakaria Muhammad Wardi bin Peeyee Muhammad Yunus Iqbal Basheer Nordin Abu Bakar Nurzeatul Hamimah Abdul Hamid Puteri Hasya Damia Abd Samad Raseeda Hamzah Saidatul Izyanie Kamarudin Siti Sakira Kamaruddin Sofianita Mutalib Syamimi Abdul-Khalil Zalilah Abd Aziz