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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohd Zaki Salikon
Universiti Tun Hussein Onn Malaysia (UTHM)

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A comparative analysis of classification techniques on predicting flood risk Nazri Mohd Nawi; Mokhairi Makhtar; Mohd Zaki Salikon; Zehan Afizah Afip
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.pp1342-1350

Abstract

Flood is a temporary overflow of a dry area due to overflow of excess water, runoff surface waters or undermining of shoreline. In Malaysia itself in 2014, the country grieved with the catastrophic flood event in Kuala Krai, Kelantan, which caused of human lives, public assets and money lost. Due to uncertainties in flooding event, it is vital for Malaysia to have pre-warning system that assist related agencies in to categorize land areas that face high risk of flood so preventive actions can be planned in place. This paper conducts a comparative analysis of three classifications in classifying the risk of flood, whether high or low. The classification experiment conducts using three variants of Bayesian approaches, which are Bayesian Networks (BN), Naive Bayes (NB), and Tree Augmented Naive Bayes (TAN). The outcome of this research shows that Tree Augmented Naive Bayes (TAN) has the best algorithms as compared to others algorithms in classifying the risk of flood.
Parkinson disease classification: a comparative analysis on classification techniques Nazri Mohd Nawi; Mokhairi Makhtar; Zehan Afizah Afip; Mohd Zaki Salikon
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.pp1351-1358

Abstract

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.
Co-Authors Mokhairi Makhtar Nazri Mohd Nawi Zehan Afizah Afip