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All Journal JOIV : International Journal on Informatics Visualization JITK (Jurnal Ilmu Pengetahuan dan Komputer) 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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Journal : JOIV : International Journal on Informatics Visualization

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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.
A Microarray Data Pre-processing Method for Cancer Classification Hui, Tay Xin; Kasim, Shahreen; Md Fudzee, Mohd Farhan; Abdullah, Zubaile; Hassan, Rohayanti; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1523

Abstract

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.
Karonese Sentiment Analysis: A New Dataset and Preliminary Result Karo Karo, Ichwanul Muslim; Md Fudzee, Mohd Farhan; Kasim, Shahreen; Ramli, Azizul Azhar
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.6.2-2.1119

Abstract

Amount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, Naïve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 – 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percent
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 Albab, M Ulul 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 M. Ghofar Rohman 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