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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) International Journal of Advanced Science Computing and Engineering International Journal of Advances in Artificial Intelligence and Machine Learning
Shahreen Kasim, Shahreen
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Hybrid Logistic Regression Random Forest on Predicting Student Performance Rohman, Muhammad Ghofar; Abdullah, Zubaile; Kasim, Shahreen; Rasyidah, -
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.9.2.3972

Abstract

The research aims to investigate the effects of unbalanced data on machine learning, overcome imbalanced data using SMOTE oversampling, and improve machine learning performance using hyperparameter tuning. This study proposed a model that combines logistic regression and random forests as a hybrid logistic regression, random forest, and random search SV that uses SMOTE oversampling and hyperparameter tuning. The result of this study showed that the prediction model using the hybrid logistic regression, random forest, and random search SV that we proposed produces more effective performance than using logistic regression and random forest, with accuracy, precision, recall, and F1-score of 0.9574, 0.9665, 0.9576. This can contribute to a practical model to address imbalanced data classification based on data-level solutions for student performance prediction.
Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator BinJubier, Mohammed; Ismail, Mohd Arfian; Othman, Muhaini; Kasim, Shahreen; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3449

Abstract

The Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization.
Transformer in mRNA Degradation Prediction Yit, Tan Wen; Hassan, Rohayanti; Zakaria, Noor Hidayah; Kasim, Shahreen; Moi, Sim Hiew; Khairuddin, Alif Ridzuan; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1165

Abstract

The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Ranger’s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively.
Verification of Ph.D. Certificate using QR Code on Blockchain Ethereum Noorhizama, Nur Khairunnisa; Abdullah, Zubaile; Kasim, Shahreen; A Hamid, Isredza Rahmi; Mat Isa, Mohd Anuar
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1584

Abstract

One of the major challenges the university faces is to provide real-time verification of their student's degree certification upon request by other parties. Conventional verification systems are typically costly, time-consuming and bureaucratic against certificate credential misconduct. In addition, the forgery of graduation degree certificates has become more efficient due to easy-to-use scanning, editing, and printing technologies. Therefore, this research proposes verifying Ph.D. certificates using QR codes on the Ethereum blockchain to address certificate verification challenges. Blockchain technology ensures tamper-proof and decentralized management of degree certificates as the certificates stored on the blockchain are replicated across the network. The issuance of certificates requires the use of the issuer's private key, thus preventing forgery. The system was developed using Solidity for the smart contract, PHP, HTML/CSS for the web-based implementation, and MetaMask for blockchain integration. User testing confirmed the successful implementation and functionality of the system. Users can add, update, and delete certificates, generate and scan QR codes, and receive instant verification feedback. The verification system effectively meets all requirements, providing a robust solution for validating Ph.D. certificates. Future research may focus on scalability and adoption, privacy and data protection, user experience, and integration with existing systems. Other researchers can optimize the verification system for widespread adoption and utilization by exploring these areas. This research contributes to securing and efficiently verifying academic certificates using QR codes on the Ethereum blockchain. Ultimately, this work advances the field of certificate verification and promotes trust in academic credentials.
Cardio-Respiratory Motion Prediction Analysis: A Systematic Mapping Study Mohd Fuaad, Nur Atiqah; Hassan, Rohayanti; Ahmad, Johanna; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4814

Abstract

Cardio-respiratory motion prediction analysis is a crucial medical application for enhancing the precision and effectiveness of medical imaging and patient diagnosis, particularly in the cardiac and respiratory context. This systematic mapping study reviews 23 selected research papers to provide a comprehensive overview of emerging trends and future directions in the field, which also highlights challenges and limitations frequently encountered in cardio-respiratory motion prediction and identifies key machine learning, deep learning, and computational paradigm methodologies examining their application frequencies. In addition, the study analyses the number of performance metrics used alongside validation techniques, which are essential for assessing the accuracy and reliability of the predictive models. Furthermore, it explores the most utilized data types and imaging modalities in this domain, such as X-ray, CT, MRI, and ultrasound, discussing their respective advantages and limitations. Ethical considerations, including patient privacy, data security, informed consent, and the potential for bias, are also addressed. This study aims to deepen the understanding of the landscape of cardio-respiratory motion prediction, guiding future research and the development of more effective, reliable predictive models to enhance medical imaging and patient care, providing valuable insights for researchers, practitioners, and technologists in the field.
An Improved Approach of Iris Biometric Authentication Performance and Security with Cryptography and Error Correction Codes Moi, Sim Hiew; Yong, Pang Yee; Hassan, Rohayanti; Asmuni, Hishammuddin; Mohamad, Radziah; Weng, Fong Cheng; Kasim, Shahreen
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1091

Abstract

One of the most challenging parts of integrating biometrics and cryptography is the intra variation in acquired identifiers between the same users. Due to noise in the environment or different devices, features of the iris may differ when it is acquired at different time periods. This research focuses on improving the performance of iris biometric authentication and encrypting the binary code generated from the acquired identifiers. The proposed biometric authentication system incorporates the concepts of non-repudiation and privacy. These concepts are critical to the success of a biometric authentication system. Iris was chosen as the biometric identifier due to its characteristics of high accuracy and the permanent presence throughout an individual’s lifetime. This study seeks to find a method of reducing the noise and error associated with the nature of dissimilarity acquired by each biometric acquisition.  In our method, we used Reed Solomon error-correction codes to reduce dissimilarities and noise in iris data. The code is a block-based error correcting code that can be easily decoded and has excellent burst correction capabilities. Two different distance metric measurement functions were used to measure the accuracy of the iris pattern matching identification process, which are Hamming distance and weighted Euclidean distance. The experiments were conducted with the CASIA 1.0 iris database. The results showed that the False Acceptance Rate is 0%, the False Rejection Rate is 1.54%, and the Total Success Rate is 98.46%. The proposed approach appears to be more secure, as it is able to provide a low rate of false rejections and false acceptances.
Big Healthcare Data: Survey of Challenges and Privacy Bin Jubeir, Mohammed; Ismail, Mohd Arfian; Kasim, Shahreen; Amnur, Hidra; Defni, -
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.246

Abstract

The last century witnessed a dramatic leap in the shift towards digitizing the healthcare workflow and moving to e-patients' records. Health information is consistently becoming more diverse and complex, leading to the so-called massive data. Additionally, the demand for big data analytics in healthcare organizations is increasingly growing with the aim of providing a wide range of unprecedented potentials that are considered necessary for the provision of meaningful information about big data and improve the quality of healthcare delivery. It also aims to increase the effectiveness and efficiency of healthcare organizations; provide doctors and care providers better decision-making information and help them in the early detection of diseases. It also assists in evidence-based medicine and helps to minimize healthcare cost. However, a clear contradiction exists between the privacy and security of big data and its widespread usage. In this paper, the focus is on big data with respect to its characteristics, trends, and challenges. Additionally, the risks and benefits associated with data analytics were reviewed.
Investigation on Java Mutation Testing Tools Abbas, Sara Tarek ElSayed; Hassan, Rohayanti; Halim, Shahliza Abd; Kasim, Shahreen; Ramlan, Rohaizan
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1090

Abstract

Software Testing is one of the most significant phases within the software development life cycle since software bugs can be costly and traumatic. However, the traditional software testing process is not enough on its own as some undiscovered faults might still exist due to the test cases’ inability to detect all underlying faults. Amidst the various proposed techniques of test suites’ efficiency detection comes mutation testing, one of the most effective approaches as declared by many researchers. Nevertheless, there is not enough research on how well the mutation testing tools adhere to the theory of mutation or how well their mutation operators are performing the tasks they were developed for. This research paper presents an investigative study on two different mutation testing tools for Java programming language, namely PIT and µJava. The study aims to point out the weaknesses and strengths of each tool involved through performing mutation testing on four different open-source Java programs to identify the best mutation tool among them. The study aims to further identify and compare the mutation operators of each tool by calculating the mutation score. That is, the operators’ performance is evaluated with the mutation score, with the presumption that the more prominent the number of killed mutants is, the higher the mutation score, thus the more effective the mutation operator and the affiliated tool. 
Study the Field of View Influence on the Monchromatic and Polychromatic Image Quality of a Human Eye Qasim, Adeeb Mansoor; Aljanabi, Mohammad; Kasim, Shahreen; Ismail, Mohd Arfian; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.931

Abstract

In this paper, the effect of the eye field of view (known as F.O.V.) on the performance and quality of the image of the human eye is studied, analyzed, and presented in detail. The image quality of the retinal is numerically analyzed using the eye model of Liou and Brennan with this polymer contact lens. The image, which is in digital form were collected from various sources such as from photos, text structure, manuscripts, and graphics. These images were obtained from scanned documents or from a scene. The color fringing which is chromatic aberration addition to polychromatic effect was studied and analyzed. The Point Spreads Function or (known as PSF) as well as The Modulation Transfers Function (known as MTF) were measured as the most appropriate measure of image quality. The calculations of the image quality were made by using Zemax software. Then, the result of the calculation demonstrates the value of correcting the chromatic aberration. The results presented in this paper had shown that the form of image is so precise to the eye (F.O.V.). The image quality is degraded as (F.O.V.) increase due to the increment in spherical aberration and distortion aberration respectively. In conclusion, then Zemax software that was used in this study assist the researcher potential to design human eye and correct the aberration by using external optics.
A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations Alwaisi, Shaimaa Safaa Ahmed; Abbood, Maan Nawaf; Jalil, Luma Fayeq; Kasim, Shahreen; Mohd Fudzee, Mohd Farhan; Hadi, Ronal; Ismail, Mohd Arfian
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.737

Abstract

The amount of data in our world has been rapidly keep growing from time to time.  In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
Co-Authors A Hamid, Isredza Rahmi Ab Razak, Mohd Faizal Abbas, Sara Tarek ElSayed Abbood, Maan Nawaf Abdullah, Noryusliza Abdullah, Nuraini Abdullah, Zubaile Ahmad Firdaus, Ahmad Ahmad, Johanna Alde Alanda, Alde Aldo Erianda, Aldo Ali Shah, Zuraini Aljanabi, Mohammad Alwaisi, Shaimaa Safaa Ahmed Anuar, Anies Nurfazlin Arrova Dewi, Deshinta Arshad, Mohamad Safwan Asmuni, Hishammuddin Bin Jubeir, Mohammed BinJubier, Mohammed Defni, - Dickenn, Haezel Ann Dwiny Meidelfi Efrizoni, Lusiana Eg Su, Goh Fadly Fadly Ferda Ernawan Gusman, Taufik Halim, Shahliza Abd Hanif Jofri, Muhamad Hassan, Norhasniza Hendrick, - Hidra Amnur Hui, Tay Xin Ichwanul Muslim Karo Karo Indrarini Dyah Irawati Ismail, Mohd Arfian Jalil, Luma Fayeq Jaya, M. Izham Kai Yuen, Simon Chong Kamarudin, Nur Fatihah Kamarudin, Nur Khairani Khairuddin, Alif Ridzuan Ling, Teng Mee Mat Isa, Mohd Anuar Mizan, Muhammad Thaqiyuddin Mohamad Sukri, Khairul Amin Mohamad, Radziah Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fauzi, Abdullah Munzir Mohd Fuaad, Nur Atiqah Mohd Fudzee, Mohd Farhan Mohd Noh, Noraziah Moi, Sim Hiew Muhaini Othman, Muhaini Nazirah, Nurul Ain Noorhizama, Nur Khairunnisa Norung, Muhammad Hazim Muhamad Osman, Mohd Zamri Puspita, Kartika Qasim, Adeeb Mansoor Ramlan, Rohaizan Ramli, Azizul Azhar Rassem, Taha H. Rasyidah, - Rd. Rohmat Saedudin Rohayanti Hassan, Rohayanti Rohman, Muhammad Ghofar Ronal Hadi Ruslaan, Mohd Asyraf Saifannur, Andri Saifunnizam, Syamir Thaqif Salamat, Mohamad Aizi Saringat, Mohd Zainuri Selamat, Norhanim Sujon, Khaled Mahmud Sumatrani Saragih, Majied Thevaraju, Devi Priya Tole Sutikno Weng, Fong Cheng Yit, Tan Wen Yong, Pang Yee Yulherniwati, - Yuris Alkhalifi Zainodin, Muhammad Edzuan Zakaria, Mohd Zaki Zakaria, Noor Hidayah Zakaria, Noor Hidayah Binti Zakaria, Zalmiyah Zamri, Nurul Aqilah