Malaysia citizens are categorised into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. Based on recent studies by the World Bank, Malaysia is expected to enter the high-income economy status no later than the year 2024. Thus, it is essential to clarify the B40 population through a predictive classification as a prerequisite towards developing a comprehensive action plan by the government. This paper is aimed at identifying the best machine learning models using Naive Bayes, Decision Tree and k-Nearest Neighbors algorithm for classifying the B40 population. Several data pre-processing task such as data cleaning, feature engineering, normalisation, feature selection: Correlation Attribute, Information Gain Attribute and Symmetrical Uncertainty Attribute and sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data. Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other. For the experiments, a dataset from National Poverty Data Bank called eKasih obtained from the Society Wellbeing Department, Implementation Coordination Unit of Prime Minister's Department (ICU JPM), consisting of 99,546 households from 3 different states: Johor, Terengganu and Pahang are used to train each of the machine learning model. The experimental results using 10-Fold Cross-Validation method demonstrates that the overall performance of Decision Tree model outperformed the other models and the significance test specified the result is statistically significance.
The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.
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