Generally, the high number of occupational accidents in Indonesia has been perceived to be caused by the unsafe behavior of workers, unsafe conditions, or both.
Īccording to the Global Competitiveness Index (2011), Indonesia is one of the ASEAN countries with the highest number of work accidents, with a fatal working accident index of 40 per 100.000 workers. Additionally, several additional techniques, including the manifold learning technique (i.e., Laplacian Eigenmaps) and the autoencoder (i.e., NVAE) were also employed. Many data-mining methods, including association rule mining (ARM), Bayesian networks (BNs), and decision trees (DT), have been employed to find hidden patterns in enormous amounts of data. By using data mining, meaningful patterns and trends can be extracted from vast volumes of data. Therefore, it is important to analyze unsafe behavior not only based on the unsafe behavior itself, but also from other project attributes. The characteristics and frequency of unsafe behavior may vary depending on type, contract value, construction period, and other project characteristics. In every project, various types of unsafe behaviors can occur. Therefore, it is important to investigate unsafe behavior to improve the policies regarding safety management systems. As a result, safety management and safety performance can increase significantly.Ĭonstruction accidents are highly correlated with unsafe behavior at construction sites, resulting in low safety performance. Hence, the findings are reliable to be used as guideline information for safety trainers to prioritize related safety trainings and for safety inspectors when carrying out inspections on construction sites. The ARM results were evaluated with a reliability evaluation method before being validated by construction safety experts. These unsafe behaviors were associated with accident types of falling, and being struck or cut by items, as well as violations of Manpower and Transmigration Ministerial Regulation 01/1980, and Manpower Ministerial Regulation 09/2016. The most frequent unsafe behaviors were workers putting tools and materials in random places, workers not attaching safety lines at provided places, and workers moving work tools and materials in ways that were not in accordance with procedures. As a result, two consolidated rules were obtained. Association rule mining was used to explore the database. In Indonesia, this is the first study to investigate 2503 instances of unsafe behavior that occurred across Indonesian construction projects in relation to their attributes to obtain insightful knowledge by using the association rule mining (ARM) method. One contributing factor to work accidents is unsafe behavior by workers at construction sites. The frequency of work accidents in construction projects is relatively high.
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