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All Journal Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization
Su-Cheng Haw
Faculty of Information Technology, Multimedia University, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Analysis of multi-criteria recommendation system based on fuzzy algorithm Anaam, Elham Abdulwahab; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy; Tong, Gee-Kok
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7801

Abstract

There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The methodology that is used in the current study is fuzzy multi-critical decision-making to enhance the precision and appropriateness of the recommendations provided to users, and make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms can solve the multi-criteria issues. Furthermore, the research considers unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.
Indonesian-English Textual Similarity Detection Using Universal Sentence Encoder (USE) and Facebook AI Similarity Search (FAISS) Krisnawati, Lucia D.; Mahastama, Aditya W.; Haw, Su-Cheng; Ng, Kok-Why; Naveen, Palanichamy
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.11274

Abstract

The tremendous development in Natural Language Processing (NLP) has enabled the detection of bilingual and multilingual textual similarity. One of the main challenges of the Textual Similarity Detection (TSD) system lies in learning effective text representation. The research focuses on identifying similar texts between Indonesian and English across a broad range of semantic similarity spectrums. The primary challenge is generating English and Indonesian dense vector representation, a.k.a. embeddings that share a single vector space. Through trial and error, the research proposes using the Universal Sentence Encoder (USE) model to construct bilingual embeddings and FAISS to index the bilingual dataset. The comparison between query vectors and index vectors is done using two approaches: the heuristic comparison with Euclidian distance and a clustering algorithm, Approximate Nearest Neighbors (ANN). The system is tested with four different semantic granularities, two text granularities, and evaluation metrics with a cutoff value of k={2,10}. Four semantic granularities used are highly similar or near duplicate, Semantic Entailment (SE), Topically Related (TR), and Out of Topic (OOT), while the text granularities take on the sentence and paragraph levels. The experimental results demonstrate that the proposed system successfully ranks similar texts in different languages within the top ten. It has been proven by the highest F1@2 score of 0.96 for the near duplicate category on the sentence level. Unlike the near-duplicate category, the highest F1 scores of 0.77 and 0.89 are shown by the SE and TR categories, respectively. The experiment results also show a high correlation between text and semantic granularity.
An Automated Face Detection and Recognition for Class Attendance Horn Boe, Chang; Ng, Kok-Why; Haw, Su-Cheng; Naveen, Palanichamy; Abdulwahab Anaam, Elham
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2967

Abstract

Class attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency.
Chronic disease prediction chatbot using deep learning and machine learning algorithms Sia, Mandy; Ng, Kok-Why; Haw, Su-Cheng; Jayaram, Jayapradha
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8462

Abstract

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are k-nearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.
Adaptive Deep Convolution Neural Network for Early Diagnosis of Autism through Combining Personal Characteristic with Eye Tracking Path Imaging Kesavan, Revathi; Palanichamy, Naveen; Haw, Su-Cheng; Ng, Kok-Why
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3046

Abstract

Autism is a large set of illnesses related to brain development, also referred to as autism spectrum disorder (ASD). According to WHO reports, 1 in 100 children is expected to have ASD. Numerous behavioral domains are affected, including linguistic, interpersonal skills, stereotypical and repetitive behaviors which represent an extreme instance of a neurodevelopmental abnormality. Identifying ASD can be difficult and exhausting because its symptoms are remarkably identical to those of many other disorders of the mind. Medical professionals can improve diagnosis efficiency by adapting deep learning practices. In clinics for autism spectrum disorders, eye-tracking scan pathways (ETSP) have become a more common instrument. This approach uses quantitative eye movement analysis to study attentional processes, and it exhibits promising results in the development of indicators that can be used in clinical studies for autism.   ASD can be identified by comparing the abnormal attention span patterns of children’s having the disorder to the children’s who are typically developing. The recommended model makes use of two publicly viable datasets, namely ABIDE and ETSP imaging. The proposed deep convolutional network consists of four hidden convolution layers and uses 5-fold cross-validation strategy. The performance of the proposed model is validated against multilayer perceptron (MLP) and conventional machine learning classifiers like decision tree (DT), k-nearest neighbor (KNN) and Random Forest (RF) using metrics like sensitivity, specificity and area under curve (AUC). The findings demonstrated that without the need for human assistance, the suggested model is capable of correctly identifying children with ASD.
Context-Aware Job Recommender System Azri, Muhammad Haziq Fikri Bin; Haw, Su-Cheng; Ng, Kok-Why; Saad, Mohamad Firdaus Mat
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3021

Abstract

Context-aware recommendation systems have emerged as essential to interactive web content and online job search. Primarily, since so many job offers are published on different online platforms, it can make the users take some time to find good opportunities that match exactly what they are looking for, as well as countless qualified candidates and other characteristics within that context, such as temporality. This comes as no surprise, as many practitioners and researchers have resorted to machine learning to create context-aware job recommendation systems that cater not only to job seekers. In this comparative paper, we have analyzed various machine-learning models for job recommendation systems. Four fundamental pillars are considered: accuracy, scalability, interpretability, and computational efficiency. This paper also studies the extent to which these models are contextual (e.g., how well they can model factors due to user preferences, job requirements, location, industry evolution, and temporality.) and can be used as a recommendation system. This study uses real-world employment data from actual employment statistics (through fixed-period analysis), professional networking platforms, and online job market platforms. The study does so purposefully to be comprehensive because it believes the lessons from remote work are generalizable. Still, the data is from a wide variety of job sectors, job positions, and locations. The group created a test environment for constructing and testing machine learning algorithms. Collaborative filtering, content-based, matrix factorization, deep learning, and many other hybrid approaches have obtained better results. This study was performed on Python with sci-kit-learn, pandas, and NumPy. The proposed system is a context-aware job recommender system that employs many machine learning algorithms to personalize job recommendations concerning user preferences and contextual information such as job location, industry status, and temporal dynamics. The findings underscore the importance of choosing machine learning models that are well-suited for job recommendation systems on a case-by-case basis. This comparative study intends to add to the art by providing algorithmic proof and practical advice to properly leverage machine learning models proposed in a naturalistic, messy setting of context-aware job recommendation systems. 
Hybrid-Based Recommender System Based on Electronic Product Reviews Muhammad Syafiq Chelvam, Nor Liyana Natasha; Haw, Su-Cheng; Krisnawati, Lucia D.; Mahastama, Aditya
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3561

Abstract

The era of abundant information and the continuous introduction of new products and services has made it increasingly challenging for users to navigate numerous options. Recommender systems have emerged as essential tools to help users find personalized and relevant information quickly. This paper proposes a hybrid recommender system that effectively processes online customer reviews using word embedding and clustering techniques. The system generates product-feature words, detects sentiment words and their intensity, analyzes word correlations, and extracts variables from the reviews for the product. Word embedding models, such as Word2Vec, are employed to capture the semantic content of product reviews and descriptions. The attributes extracted from the text data and word embeddings are combined to create a hybrid representation of products. Based on this hybrid representation, the system calculates the similarity among products using cosine similarity and other measures. Finally, it returns a ranked list of recommended best products based on how similar they are to either an inputted product or user preferences. We have implemented the system and experimental evaluations have been carried out on the “Datafiniti Electronics Product Data" dataset. We aim to provide personalized recommendations to users based on online reviews, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age. The developed prototype will provide personalized recommendations to users, ultimately enhancing the user experience and addressing the challenge of information overload in the digital age.
Predictive Analytics for Employability in Malaysian TVET with a Hybrid of Regression and Clustering Methods Mahdin, Hairulnizam; Nurwarsito, Heru; Baharum, Zirawani; Kamri, Khairol Anuar; Hassan, Azman; Haw, Su-Cheng; Arshad, Mohammad Syafwan
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4516

Abstract

Graduate employability remains a high concern for Technical and Vocational Education and Training (TVET) institutions, particularly within Malaysia’s Technical University Network (MTUN), where producing industry-ready graduates is a central goal. While machine learning has transformed fields like healthcare and finance, its application in vocational education remains underexplored—particularly for employability prediction. This study addresses this gap by hybridizing decision trees and clustering to uncover non-linear patterns in student survey data. Guided by Human Capital Theory and SERVQUAL, which inform variable selection (e.g., technical skills as productivity investments), this study integrates multiple linear regression, decision tree regression, and K-Means clustering to identify significant predictors and uncover latent student groupings. Using a publicly available dataset of Likert-scale responses from MTUN students, technical skills and supervisory support consistently emerged as the most impactful employability predictors. Communication showed moderate influence, while training delivery and problem-solving exhibited variable effects depending on the modelling approach. Unlike regression, decision trees revealed non-linear interaction thresholds. For example, students with SVR < 3.5 and TS < 4.0 had 40% lower employability scores, suggesting targeted mentoring could yield disproportionate improvements. Clustering revealed three distinct student profiles, which could support data-driven interventions. This hybrid framework demonstrates the potential for integrating machine learning into institutional analytics for proactive support of employability.
Co-Authors Abdulwahab Anaam, Elham Anaam, Elham Abdulwahab Arshad, Mohammad Syafwan Azri, Muhammad Haziq Fikri Bin Baharum, Zirawani Hairulnizam Mahdin, Hairulnizam Hassan, Azman Heru Nurwarsito Horn Boe, Chang Jayaram, Jayapradha Kamri, Khairol Anuar Kesavan, Revathi Lucia D. Krisnawati Mahastama, Aditya Mahastama, Aditya W. Muhammad Syafiq Chelvam, Nor Liyana Natasha Naveen, Palanichamy Ng, Kok-Why Palanichamy, Naveen Saad, Mohamad Firdaus Mat Sia, Mandy Tong, Gee-Kok