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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Shazlyn Milleana Shaharudin

Universiti Pendidikan Sultan Idris

Author-ID : 2406829

Computer Science & IT Education Electrical & Electronics Engineering Engineering

Published : 5 Documents Claim Missing Document

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Articles

An efficient method to improve the clustering performance using hybrid robust principal component analysis-spectral biclustering in rainfall patterns identification Shazlyn Milleana Shaharudin; Shuhaida Ismail; Siti Mariana Che Mat Nor; Norhaiza Ahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and biclustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65%-70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCAkMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.

Development and validation of early childhood care and education pre-service lecturer instrument Shazlyn Milleana Shaharudin; Noorazrin Abd Rajak; Noor Wahida Md. Junus; Nor Azah Samat
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents to develop and validate the Early Childhood Care and Pre-Service Lecturer Instrument constructed to determine their level of competencies toward the quality of early childhood carers-educators’ professionalism in Malaysia. Components which affect the early childhood quality were characterized through inclusive literature reviews alongside interviews conducted with experts and experienced lecturers. In this study, two experts were elected to review this instrument so as to enhance its validity while 70 more lecturers in Malaysia were involved. There are four scales in principal component analysis pertaining the quality of early childhood professionalism, namely: (1) disposition, (2) knowledge, (3) skills, and (4) practices. The component loading range or respective instrument item were between 0.56 and 0.79, while the range for respective scales the alpha reliability coefficient were between 0.90 and 0.94. Concisely, the findings from this study corroborated the weight and consistency of the ECCE Pre-Service Lecturer Instrument.

A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia Siti Mariana Che Mat Nor; Shazlyn Milleana Shaharudin; Shuhaida Ismail; Nurul Hila Zainuddin; Mou Leong Tan
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.

Internet of things: security requirements, attacks and counter measures Maria Imdad; Deden Witarsyah Jacob; Hairulnizam Mahdin; Zirawani Baharum; Shazlyn Milleana Shaharudin; Mohd Sanusi Azmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1520-1530

Internet of Things (IoT) is a network of connected and communicating nodes. Recent developments in IoT have led to advancements like smart home, industrial IoT and smart healthcare etc. This smart life did bring security challenges along with numerous benefits. Monitoring and control in IoT is done using smart phone and web browsers easily. There are different attacks being launched on IoT layers on daily basis and to ensure system security there are seven basic security requirements which must be met. Here we have used these requirements for classification and subdivided them on the basis of attacks, followed by degree of their severity, affected system components and respective countermeasures. This work will not only give guidelines regarding detection and removal of attacks but will also highlight the impact of these attacks on system, which will be a decision point to safeguard system from high impact attacks on priority basis.

A new hyhbrid coefficient of conjugate gradient method Nur Syarafina Mohamed; Mustafa Mamat; Mohd Rivaie; Shazlyn Milleana Shaharudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1454-1463

Hybridization is one of the popular approaches in modifying the conjugate gradient method. In this paper, a new hybrid conjugate gradient is suggested and analyzed in which the parameter is evaluated as a convex combination of while using exact line search. The proposed method is shown to possess both sufficient descent and global convergence properties. Numerical performances show that the proposed method is promising and has overpowered other hybrid conjugate gradient methods in its number of iterations and central processing unit per time.

Co-Authors Deden Witarsyah Hairulnizam Mahdin Maria Imdad Mohd Rivaie Mohd Sanusi Azmi Mou Leong Tan Mustafa Mamat Noor Wahida Md. Junus Noorazrin Abd Rajak Nor Azah Samat Norhaiza Ahmad Nur Syarafina Mohamed Nurul Hila Zainuddin Shuhaida Ismail Siti Mariana Che Mat Nor Zirawani Baharum

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