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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ozlem Gurunlu Alma
Mugla Sitki Kocman University
Computer Science & IT Engineering

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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 Mohd Saifullah Rusiman Muhammad Ammar Shafi Nurfarawahida Ramly Suparman .S