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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohd Saifullah Rusiman
Universiti Tun Hussien Onn Malaysia
Computer Science & IT Engineering

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K-means clustering analysis and multiple linear regression model on household income in Malaysia Gan Pei Yee; Mohd Saifullah Rusiman; Shuhaida Ismail; Suparman Suparman; Firdaus Mohamad Hamzah; Muhammad Ammar Shafi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp731-738

Abstract

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.
An adjustment degree of fitting on fuzzy linear regression model toward manufacturing income Nurfarawahida Ramly; Mohd Saifullah Rusiman; Muhammad Ammar Shafi; Suparman .S; Firdaus Mohamad Hamzah; Ozlem Gurunlu Alma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp543-551

Abstract

Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.
Co-Authors Firdaus Mohamad Hamzah Gan Pei Yee Muhammad Ammar Shafi Nurfarawahida Ramly Ozlem Gurunlu Alma Shuhaida Ismail Suparman .S Suparman Suparman