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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ruhaila Maskat
Universiti Teknologi MARA
Computer Science & IT Engineering

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A framework to shape the recommender system features based on participatory design and artificial intelligence approaches Tajul Rosli Razak; Mohammad Hafiz Ismail; Shukor Sanim Mohd Fauzi; Ray Adderley JM Gining; Ruhaila Maskat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp727-734

Abstract

A recommender system is an algorithm aiming at giving suggestions to users on relevant elements or items such as products to purchase, books to read, jobs to apply or anything else depending on industries or situations. Recently, there has been a surge in interest in developing a recommender system in a variety of areas. One of the most widely used approaches in recommender systems is collaborative filtering (CF). The CF is a strategy for automatically creating a filter based on a user's needs by extracting desires or recommendation information from a large number of users. The CF approach uses multiple correlation steps to do this. However, the occurrence of uncertainty in finding the best similarity measure is unavoidable. This paper outlines a method for improving the configuration of a recommender system that is tasked with recommending an appropriate study field and supervisor to a group of final-year project students. The framework we suggest is built on a participatory design methodology that allows students' individual opinions to be factored into the recommender system's design. The architecture of the recommender scheme was also illustrated using a real-world scenario, namely mapping the students' field of interest to a possible supervisor for the final year project.
Depression prediction using machine learning: a review Hanis Diyana Abdul Rahimapandi; Ruhaila Maskat; Ramli Musa; Norizah Ardi
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.pp1108-1118

Abstract

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the systematic mapping study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were hospital anxiety and depression scale (HADS) and hamilton depression rating scale (HDRS) for general population, while for literature targeting older population geriatric depression scale (GDS) was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and random forest was found to be the most reliable algorithm across the publications.
A taxonomy of Malay social media text Ruhaila Maskat; Yuda Munarko
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp465-472

Abstract

In this paper, we proposed a preliminary taxonomy of Malay social media text. Performing text analytics on Malay social media text is a challenge. The formal Malay language follows specific spelling and sentence construction rules. However, the Malay language used in social media differs in both aspects. This impedes the accuracy of text analytics. Due to the complexity of Malay social media text, many researches has chosen to focus on classifying the formal Malay language. To the best of our knowledge, we are the first to propose a formal taxonomy for Malay text in social media. Narrow and informal categorisations of Malay social media text can be found amidst efforts to pre-process social media text, yet cherry-picked only some categories to be handled. We have differentiated Malay social media text from the formal Malay language by identifying them as Social Media Malay Language or SMML. They consists of spelling variations, Malay-English mix sentence, Malay-spelling English words, slang-based words, vowel-les words, number suffixes and manner of expression.This taxonomy is expected to serve as a guideline in research and commercial products.
Detecting candidates of depression, anxiety and stress through malay-written tweets: a preliminary study Muhammad Zahier Nasrudin; Ruhaila Maskat; Ramli Musa
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.pp787-793

Abstract

Depression, anxiety and stress are not trivial conditions applicable for only the weak-hearted. They can be inflicted by anyone of all age groups, gender, race and social status. While some are courageous to acknowledge their condition, others shy away in shame or denial. In this paper, we proposed a “proactive” approach to detecting candidates of depression, anxiety and stress in an unobtrusive manner by tapping into what Malaysians tweet in Malay language. From this preliminary study, we constructed 165 Malay layman terms which describe depression, anxiety or stress as identified in M-DASS-42 scale. Since Twitter is an informal platform, construction of Malay layman terms is an essential step to the detection of candidates. Our study on 1,789 Malay tweets discovered 6 Twitter users as potential candidates, having high frequency of tweets with any of the layman terms. We can conclude that using tweets can be useful in unobtrusively detecting candidates of depression, anxiety or stress. This paper also identifies open research areas.
Co-Authors Hanis Diyana Abdul Rahimapandi Mohammad Hafiz Ismail Muhammad Zahier Nasrudin Norizah Ardi Ramli Musa Ray Adderley JM Gining Shukor Sanim Mohd Fauzi Tajul Rosli Razak Yuda Munarko