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Mining industry has a wide range of activities and work stages which involve mining workers, starting from general investigations, explorations, to reclamation. It is considered as one of the industries with a high-level risk. Mining workers are synonymous with shift and roster work patterns, and physical work that requires strength, flexibility and endurance of sustained muscles that contribute to fatigue. Generally, fatigue is defined as a state of weariness due to prolonged wakefulness, long work periods, and is characterized by decreased alertness, impaired decision making, and reduced capacity for neurobehavioral performance (Akerstedt, 1995; Dinges, 1995). The purpose of this study is to determine the average alertness score of exploration drilling service workers in coal mines which is influenced by the level of fatigue of workers calculated based on the quality and quantity of sleep using the SAFTE Fatigue Model. This research is a quantitative descriptive cross-sectional design, from June to August 2019. The results state that the majority of respondents have an alertness score in the reduced category (66.67%). The cross variable distribution shows that the lowest proportion of alertness is in the age group of 31-40 years (50.00%), moderate sleep quality (70.00%) and quantity of sleep poor (33.33%). There are differences in alertness scores between morning shift and night shift respondents, and that is the night shift has lower average alertness scores than morning shift respondents. The recommendations of this study are to improve sleep hygiene by providing health education, ensuring optimal room temperature and adequate exposure to natural lighting, and minimizing the exposure to noise and gadgets..
Metabolic syndrome has a large impact on the health condition of workers, this can increase costs incurred by the company and lead to loss of productivity it is necessary to research the risk factors for metabolic syndrome in office workers at PT X which is expected to prevent and control the prevalence of metabolic syndrome in order to reduce risk factor of metabolic syndrome in future. This research was conducted on office workers at PT X with 106 research respondents during February - August 2020 in Jakarta. PT X is an engineering service provider of the Indusrial Automation that has clients in several process industries whose daily activities are sedentary. The design of this research study uses cross sectional method. The aim is to determine the relationship between the dependent variable (individual risk factors and occupational risk factors) with the independent variable Metabolic Syndrome. The results showed that the proportion of metabolic syndrome is 4,97% which the highest metabolic syndrome component was found in high triglycerides 20,4%, low HDL levels is 14,9% and had abdominal obesity is 14,4%. Which had 1 symptom of metabolic syndrome criteria is 25,4% and which had 2 symptoms of metabolic syndrome criteria are 8,3%. Although the analysis found no significant results on the relationship of individual factors and occupation of the metabolic syndrome, but on some individual risk factors such as smoking have a risk of 3,35 times greater than not smoking, and higher education levels 2,44 times higher risk compared to lower education levels.
Hasil: Variabel bulan, lokasi, jam dan tipe unit memiliki hubungan signifikan dengan variabel penyebab langsung (nilai-P < 0,05). Variabel usia, pengalaman, jabatan, departemen dan status kontrak tidak berhubungan dengan variabel penyebab langsung (nilai-P > 0,05). Sedangkan variabel barrier memiliki hubungan signifikan dengan variabel emerging risk (nilai-P < 0,0001).
Kesimpulan: Variabel terkait dengan job factor (bulan, lokasi, jam dan tipe unit) perlu diintervensi untuk menurunkan potensi munculnya penyebab langsung kecelakaan. Standar internasional tentang manajemen risiko juga perlu diperbarui sehingga praktek penilaian risiko dimasa mendatang mampu mengantisipasi emerging risk akibat barrier.
Method: Both quantitative and qualitative analysis were used for this study. The main data is secondary data in the form of Incident Investigation Reports of PT. X in 2020, there were 822 incidents from all sites on the islands of Java and Sumatra. Triangulation was carried out using primary data in the form of in-depth interviews, FGDs and document reviews. Bivariate statistical tests with the Chi Square method are used to see the relationship between the basic cause variables (month, location, hour, contract status, age, experience, department, position and type of unit) and the immediate cause variables (unsafe conditions and unsafe acts). Meanwhile, multivariate statistical tests with the Linear Regression methode are then used to see the relationship between barrier variables and emerging risk variables.
Result: The barrier variable are significantly correlated to emerging risk variables (P-value < 0.0001). Meanwhile, the basic cause variables including month, location, hour and type of unit had a significant relationship with the immediate causes variable (P-value < 0.05). The basic cause variables including age, experience, position, department, and contract status are not related to the immediate cause variables (P-value > 0.05).
Conclusion: Variables related to job factors including month, location, hour and type of unit need to be intervened to reduce the developing of accident immediate causes. International standards regarding risk management also need to be updated so that future risk assessment practices are able to anticipate emerging risks of barrier.
