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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics
Khyrina Airin Fariza Abu Samah
Universiti Teknologi MARA
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor Nur Hanina Izani Muhammad Zaihani; Rosniza Roslan; Zaidah Ibrahim; Khyrina Airin Fariza Abu Samah
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.118 KB) | DOI: 10.11591/eei.v9i3.2079

Abstract

There are numerous studies on brain imaging applications.  The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor.  Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues.  Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor.  However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance.  They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients.  Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations.  The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS).  The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.
A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia Khyrina Airin Fariza Abu Samah; Nurqueen Sayang Dinnie Wirakarnain; Raseeda Hamzah; Nor Aiza Moketar; Lala Septem Riza; Zainab Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1130-1142

Abstract

This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.
Impact of the COVID-19 pandemic on Malaysian and Indonesian educators in tertiary institutions Chew Chiou Sheng; Khyrina Airin Fariza Abu Samah; Fadilah Ezlina Shahbudin; David Loh Er Fu; Heni Mulyani; Nugraha Nugraha
International Journal of Evaluation and Research in Education (IJERE) Vol 12, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v12i1.23979

Abstract

This study investigated the challenges encountered by educators conducting online teaching during the COVID-19 pandemic in Malaysian and Indonesian higher learning institutions. Quantitative and qualitative methods were used to obtain information in this study. The respondents comprised 250 educators from Malaysian and Indonesian higher learning institutions. A self-developed Likert-scale online questionnaire was given to the respondents. The study findings revealed that Malaysian educators faced greater challenges in mental health, time management, and assessments. In comparison, Indonesian educators experienced more challenges in demonstrating compassion to students during online teaching. Educators in both countries encountered poor internet connectivity, lack of interaction and engagement with students, stress, and anxiety. Opportunities created by the COVID-19 pandemic comprise exploring and learning online teaching tools, producing online teaching and learning materials, conducting research, and writing research papers for publication. Recommendations for addressing online teaching challenges and suggestions for future research are also discussed.
Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals Khyrina Airin Fariza Abu Samah; Nur Maisarah Nor Azharludin; Lala Septem Riza; Mohd Nor Hajar Hasrol Jono; Nor Aiza Moketar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1793-1802

Abstract

Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.
Co-Authors Chew Chiou Sheng David Loh Er Fu Fadilah Ezlina Shahbudin Lala Septem Riza Mohd Nor Hajar Hasrol Jono Mulyani, Heni Nor Aiza Moketar Nugraha Nugraha Nur Hanina Izani Muhammad Zaihani Nur Maisarah Nor Azharludin Nurqueen Sayang Dinnie Wirakarnain Raseeda Hamzah Rosniza Roslan Zaidah Ibrahim Zainab Othman