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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science 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 Electrical & Electronics Engineering

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An Information Entropy Based to Identify Dominant Species for No Biological Data Genes Nazirah, Nurul Ain; Kasim, Shahreen; Meidelfi, Dwiny
International Journal of Advanced Science Computing and Engineering Vol. 2 No. 3 (2020)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1167.907 KB) | DOI: 10.62527/ijasce.2.3.101

Abstract

This report discusses about the dominant species of no biological data genes. This genes are belong to animal species. Gene is one of a process where the biological data encoded in the gene that instructed by the DNA to convert into a functional product such as protein. In gene classification, with the growth of using gene expression database, there are not enough tools to extract the gene expression from these databases. There exist over 23,000 to 50,000 genes for animal genome. So, this might contribute to data redundancy as problems can happen while handling a huge database. Therefore, to overcome the problem, many approaches to cluster and determine the dominant species have been proposed in the previous literature. For this project, in order to determine the dominant species, the information entropy based method is used. As conclusion, the purpose of this research is to identify the dominant species of no biological genes using entropy method proposed.
Application For Petty Cash Management Norung, Muhammad Hazim Muhamad; Kasim, Shahreen; Defni, -
International Journal of Advanced Science Computing and Engineering Vol. 2 No. 3 (2020)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.73 KB) | DOI: 10.62527/ijasce.2.3.102

Abstract

Some people currently face into traps of dept and overspending without even realizing it. The feeling of never having enough cash or living paycheck to paycheck can lead serious final problems that can lead into financial difficulties over a lifetime. This study aims to determine how application can manage the financial problems. Specifically, it manages on how you spend your money and get the overview about it. In this application, it uses financial skills such as budgeting, saving and spending. It refers to the strategies technique to determine the use of an individual. To achieve a better understanding on the money management, a report is created to see the whole month transaction such as saving and spending. The report will show the total amount of money spent and where the money is going. You will understand your expenses with the report and create a budget for future expenses. By having good financial skills, you can have a strategy on how you want to manage your money. Creating and sticking to a budget might seem tough to achieve but it helps us to see full transparency our financial situation.
Performance Comparison of Apriori, ECLAT and FP-Growth Algorithm for No Biological Data Genes for Association Rule Learning Anuar, Anies Nurfazlin; Kasim, Shahreen; Hendrick, -
International Journal of Advanced Science Computing and Engineering Vol. 2 No. 3 (2020)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.2.3.103

Abstract

This project is carried out to study the performance comparison of Apriori Algorithm, ECLAT Algorithm and FP-Growth Algorithm for no biological data genes. There are many genes with no biological data, but for this project we have chosen 4 types of no biological data genes. No biological data genes are genes that have no specific data about themselves such as location, behaviour and function of the genes. Association Rule Learning is a technique implementing big data in finding frequent item-sets. Frequent item-sets are items that occur frequently in the database. The performance of these three algorithms is compared through time efficiency and the ability to process small and large datasets. After the comparison, we can conclude that FP-Growth algorithm is the fastest algorithm for small data-set and Apriori algorithm and ECLAT algorithm takes lesser time to generate the frequent item-sets compared to FP-Growth algorithm.
Systematic Literature Review on Persuasive System Design Framework for Managing Curriculum Performance Saifunnizam, Syamir Thaqif; Md Fudzee, Mohd Farhan; Hanif Jofri, Muhamad; Kasim, Shahreen; Arrova Dewi, Deshinta; Arshad, Mohamad Safwan; Yulherniwati, -
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.9.1.3663

Abstract

Integrating digital resources into educational assessment has led to the widespread adoption of e-portfolios as tools for documenting and evaluating student achievement, thereby transforming traditional evaluation methods. However, the existing frameworks primarily focus on assessing academic performance, often neglecting the comprehensive monitoring of student’s co-curricular activities. To overcome current gaps in comprehensive student evaluation, this study introduces a conceptual framework incorporating persuasive system design (PSD) into an e-portfolio to facilitate efficient co-curricular performance monitoring in Malaysian secondary schools. To ensure a thorough approach to educational evaluation, it is essential to effectively monitor and manage academic and extracurricular performance to understand student progress comprehensively. By adding Physical Activity, Sports, and Co-curriculum Assessment (PAJSK) – specific categories and key PSD elements- primary task support, dialogue support, system credibility support, and social support- that are all designed to improve user engagement and system dependability in an educational environment, the framework builds on the Oinas-Kukkonen and Harijumaa PSD Model. This study adapts and discusses the persuasive design elements to meet the goals of educational assessment frameworks by comparing PSD implementation in e-health, e-tourism, e-commerce, and e-learning. The results offer an overview of developing a practical, engaging e-portfolio framework that facilitates comprehensive student evaluation, especially in educational environments focusing on co-curricular achievement.
Real-time smart driver sleepiness detection by eye aspect ratio using computer vision Kai Yuen, Simon Chong; Zakaria, Noor Hidayah Binti; Eg Su, Goh; Hassan, Rohayanti; Kasim, Shahreen; Sutikno, Tole

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp677-686

Abstract

The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision.
AI and the Optimization of Product Placement: Enhancing Sales through Strategic Positioning kasim, Shahreen; Zakaria, Mohd Zaki; Efrizoni, Lusiana; Fadly, Fadly
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.381

Abstract

This study aims to analyze the impact of strategic product placement and promotion strategies using the Customer's Purchase Behavior Dataset. The study utilized a controlled experimental design, wherein trial stores were matched with control stores based on pre-trial performance metrics, including total sales and customer demographics. A detailed exploratory data analysis (EDA) was conducted to segment customers based on life-stage and purchasing behaviour. Additionally, a t-Test was performed to determine whether price sensitivity and purchasing patterns differed significantly between mainstream, budget, and premium customer segments. The results indicate that trial stores implementing strategic initiatives experienced a measurable uplift in sales compared to their control counterparts. Young and mid-age singles and couples in the mainstream category were found to be more willing to pay a premium for chips, whereas families tended to purchase in bulk. The t-test confirmed statistically significant differences in purchasing behaviour across customer segments. The findings suggest that a data-driven, segment-specific marketing approach can optimise retail performance by aligning promotions and pricing with the behavioural tendencies of different consumer groups. This study demonstrates that well-targeted strategic retail initiatives can significantly improve sales performance. The insights derived from this research provide retailers with actionable strategies for tailoring product placement and promotions to maximise customer engagement. Future work should incorporate machine learning techniques to refine predictive models for real-time decision-making in retail marketing.
Comparative Analysis of Robust Imputation Techniques for Enhancing Cervical Cancer Prediction with Missing Data Mizan, Muhammad Thaqiyuddin; Ernawan, Ferda; Kasim, Shahreen; Erianda, Aldo; Mohd Fauzi, Abdullah Munzir
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.9.5.4501

Abstract

Handling missing data is a critical challenge in machine learning applications, as it can significantly affect the accuracy and reliability of predictive models. Addressing this issue is crucial for developing robust systems that can deliver high-performance results. This study provides a comparative analysis of the robust imputation technique for cervical cancer prediction with incomplete information. This study has investigated the importance of robust imputation techniques, particularly Soft Imputer, in addressing missing data challenges and enhancing model performance. This study investigates the impact of various imputations across five distinct approaches: KNN imputer, PCA imputer, MICE imputer, XGBoost imputer, LightGBM imputer, and feature selection methods. These imputation data are tested on several machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Decision Tree (DT), Support Vector Classifier (SVC), Logistic Regression (LR), Extra Trees Classifier (ETC), CatBoost Classifier, Stochastic Gradient Descent (SGD), and Gradient Boosting (GB) for improving classification accuracy of cervical cancer prediction. The evaluation reveals that the soft imputer method achieves a balanced and effective handling of missing data, significantly improving the reliability of the models. Among the tested methods, LightGBM and XGBoost deliver strong results, each achieving an average accuracy of 96.91%. MICE demonstrated the lowest average accuracy at 95.94%, although it still performs reliably in managing missing data. The findings provide valuable insights for enhancing predictive accuracy in future work by integrating advanced imputation strategies for high-dimensional and complex datasets.
Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting Puspita, Kartika; Ernawan, Ferda; Alkhalifi, Yuris; Kasim, Shahreen; Erianda, Aldo
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.9.5.4609

Abstract

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.
A Hybrid Approach for Malicious URL Detection Using Ensemble Models and Adaptive Synthetic Sampling Sujon, Khaled Mahmud; Hassan, Rohayanti; Zainodin, Muhammad Edzuan; Salamat, Mohamad Aizi; Kasim, Shahreen; Alanda, Alde
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.9.5.4627

Abstract

Malicious URLs pose a significant cybersecurity threat, often leading to phishing attacks, malware infections, and data breaches. Early detection of these URLs is crucial for preventing security vulnerabilities and mitigating potential losses. In this paper, we propose a novel approach for malicious URL detection by combining ensemble learning methods with ADASYN-based oversampling to address the class imbalance typically found in malicious URL datasets. We evaluated three popular machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, and incorporated ADASYN (Adaptive Synthetic Sampling) to handle the class-imbalanced nature of our selected dataset. Our detailed experiments demonstrate that the application of ADASYN can significantly increase the performance of the predictive model across all metrics. For instance, XGBoost saw a 2.2% improvement in accuracy, Random Forest achieved a 1.0% improvement in recall, and Decision Tree displayed a 3.0% improvement in F1-score. The Decision Tree model, in particular, showed the most substantial improvements, particularly in recall and F1-score, indicating better detection of malicious URLs. Finally, our findings in this research highlighted the potential of ensemble learning, enhanced by ADASYN, for improving malicious URL detection and demonstrated its applicability in real-world cybersecurity applications.
Co-Authors Ab Razak, Mohd Faizal Abdullah, Noryusliza Abdullah, Nuraini Ahmad Firdaus, Ahmad Alde Alanda, Alde Aldo Erianda, Aldo Ali Shah, Zuraini Anuar, Anies Nurfazlin Arrova Dewi, Deshinta Arshad, Mohamad Safwan Defni, - Dickenn, Haezel Ann Dwiny Meidelfi Efrizoni, Lusiana Eg Su, Goh Fadly Fadly Ferda Ernawan Hanif Jofri, Muhamad Hassan, Norhasniza Hendrick, - Indrarini Dyah Irawati Jaya, M. Izham Kai Yuen, Simon Chong Kamarudin, Nur Fatihah Kamarudin, Nur Khairani Ling, Teng Mee Mizan, Muhammad Thaqiyuddin Mohamad Sukri, Khairul Amin Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fauzi, Abdullah Munzir Mohd Noh, Noraziah Nazirah, Nurul Ain Norung, Muhammad Hazim Muhamad Osman, Mohd Zamri Puspita, Kartika Ramli, Azizul Azhar Rassem, Taha H. Rasyidah, - Rd. Rohmat Saedudin Rohayanti Hassan, Rohayanti Ruslaan, Mohd Asyraf Saifunnizam, Syamir Thaqif Salamat, Mohamad Aizi Saringat, Mohd Zainuri Selamat, Norhanim Sujon, Khaled Mahmud Thevaraju, Devi Priya Tole Sutikno Yulherniwati, - Yuris Alkhalifi Zainodin, Muhammad Edzuan Zakaria, Mohd Zaki Zakaria, Noor Hidayah Binti Zakaria, Zalmiyah Zamri, Nurul Aqilah