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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Sofianita Mutalib
Universiti Teknologi MARA
Computer Science & IT 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.
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 Ariff Md Ab Malik Muhammad Yunus Iqbal Basheer Nurzeatul Hamimah Abdul Hamid Puteri Hasya Damia Abd Samad Saidatul Izyanie Kamarudin Shuzlina Abdul-Rahman Siti Sakira Kamaruddin Syamimi Abdul-Khalil