Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique

According to the Department of Statistics Malaysia (DOSM) in 2018, manufacturing industry accounted for 91.2% of temporary disability cases and 6.9% of permanent disability cases. Even though there is an increasing number of research on analyzing occupational accidents at automotive manufacturing in...

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Main Author:	Siti Nor Farah Jawahir, Fadzil
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	T55-55.3 Industrial Safety Industrial Accident Prevention
Online Access:	https://etd.uum.edu.my/10130/1/s819767_01.pdf https://etd.uum.edu.my/10130/2/s819767_02.pdf
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id	my-uum-etd.10130
record_format	uketd_dc
spelling	my-uum-etd.101302022-12-07T00:32:34Z Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique 2022 Siti Nor Farah Jawahir, Fadzil Mohd Jamil, Jastini Mohd Shaharanee, Izwan Nizal Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences T55-55.3 Industrial Safety. Industrial Accident Prevention According to the Department of Statistics Malaysia (DOSM) in 2018, manufacturing industry accounted for 91.2% of temporary disability cases and 6.9% of permanent disability cases. Even though there is an increasing number of research on analyzing occupational accidents at automotive manufacturing industry in Malaysia, each research aimed for different purposes and methods. This study predicts the tendency of temporary and permanent disability by accurately identifying the characteristics of workplace accidents that occurred within automotive manufacturing in Malaysia. Decision Tree was used to build the predictive modelling of occupational accidents at automotive manufacturing industry. Decision Tree models were constructed with various algorithms (Chi-square, Gini Index and Entropy), numbers of tree branches (two and three) and data partitions (80/20, 70/30 and 60/40). The different models were compared to determine the best model for predicting and identifying the effects of occupational accidents. The best model was a three-branch decision tree model using Chi-Square as the nominal target criterion and 60/40 data partition. The testing accuracy value is 75.52%. The most important variables in the model were types of accident, cause of accidents and job types. This study produced a set of significant factors in explaining safety workplace system and built a predictive model for predicting effect of occupational accidents. It can be served as a guideline to safety management in automotive manufacturing industry in Malaysia. 2022 Thesis https://etd.uum.edu.my/10130/ https://etd.uum.edu.my/10130/1/s819767_01.pdf text eng 2025-03-03 staffonly https://etd.uum.edu.my/10130/2/s819767_02.pdf text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohd Jamil, Jastini Mohd Shaharanee, Izwan Nizal
topic	T55-55.3 Industrial Safety Industrial Accident Prevention
spellingShingle	T55-55.3 Industrial Safety Industrial Accident Prevention Siti Nor Farah Jawahir, Fadzil Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
description	According to the Department of Statistics Malaysia (DOSM) in 2018, manufacturing industry accounted for 91.2% of temporary disability cases and 6.9% of permanent disability cases. Even though there is an increasing number of research on analyzing occupational accidents at automotive manufacturing industry in Malaysia, each research aimed for different purposes and methods. This study predicts the tendency of temporary and permanent disability by accurately identifying the characteristics of workplace accidents that occurred within automotive manufacturing in Malaysia. Decision Tree was used to build the predictive modelling of occupational accidents at automotive manufacturing industry. Decision Tree models were constructed with various algorithms (Chi-square, Gini Index and Entropy), numbers of tree branches (two and three) and data partitions (80/20, 70/30 and 60/40). The different models were compared to determine the best model for predicting and identifying the effects of occupational accidents. The best model was a three-branch decision tree model using Chi-Square as the nominal target criterion and 60/40 data partition. The testing accuracy value is 75.52%. The most important variables in the model were types of accident, cause of accidents and job types. This study produced a set of significant factors in explaining safety workplace system and built a predictive model for predicting effect of occupational accidents. It can be served as a guideline to safety management in automotive manufacturing industry in Malaysia.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Siti Nor Farah Jawahir, Fadzil
author_facet	Siti Nor Farah Jawahir, Fadzil
author_sort	Siti Nor Farah Jawahir, Fadzil
title	Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
title_short	Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
title_full	Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
title_fullStr	Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
title_full_unstemmed	Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique
title_sort	predicting occupational accident at automotive manufacturing industry in malaysia using decision tree technique
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10130/1/s819767_01.pdf https://etd.uum.edu.my/10130/2/s819767_02.pdf
_version_	1776103747930292224

Predicting occupational accident at automotive manufacturing industry in Malaysia using decision tree technique

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