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Based on data for 2019-2022, 17 work accidents were reported at PT. XYZ, out of 17 accidents, 14 occurred in production. The general objective of this research is to analyze the factors related with work accidents in production workers at PT. XYZ. The research design used is cross sectional. The population and sample of 152 workers used saturated sampling technique. The data used are primary data derived from questionnaires and observations as well as secondary company data. Data analysis used the chi-square test. The results of the study 40.1% of workers had experienced work accidents with the most types of accidents being pinched, most workers were of mature age, male, secondary education, working period ≤ 5 years, shift work pattern, had a positive attitude, often/very often perform unsafe actions, low/medium fatigue, good physical condition, poor supervision, good training, good socialization, often/very often get inappropriate PPE, conducive housekeeping and often/very often intersect with unsafe conditions. Then there is a relationship between knowledge, unsafe actions, physical conditions, training and unsafe conditions with work accidents (p value <0.05). So based on the research results it is expected that PT. XYZ can always make continuous improvements in work accident prevention efforts.
X construction project workers have relatively high work hazards and risks, particularly while working under the COVID-19 pandemic situation. As construction projects must go on, the workers likely have a higher risk of the COVID-19 exposure. When some workers directly or indirectly are exposed to the COVID-19, their jobs are taken over by co-workers. This case results in fatigue for construction workers. Work fatigue is one of the causes of occupational accidents as the fatigue reduces their focus, decision-making abilities, muscle strength, communication skills, productivity, alertness, physical and psychological performance and work motivation. This study aimed to determine the impact of the COVID-19 pandemic on fatigue in PT. X workers. This study applied an observational analytic method with a cross-sectional study design. Samples were taken from the total population of foundry workers in construction projects as many as 100 workers. *Multidimensional Fatigue Inventory Questionnaire*. Data analysis with a quantitative approach used univariate and bivariate analysis. Based on the results of the Pearson correlation analysis with a significant level of p < 0.05, variables that had a relationship with fatigue variable were age (p = 0.048), sleep time (p = 0.040), comorbid (p=0.004) and the COVID-19 pandemic (p=0.001)
Resiko bekerja di perusahaan migas PT X yang berlokasi di offshore Natuna adalah relatif tinggi. Sepanjang tahun 2018 – 2023 terjadi fluktuasi kecelakaan kerja di PT. X. Bahkan setelah dua tahun (tahun 2020 dan 2019) tidak terjadi kecelakaan kerja untuk kategori recordable injury (kasus di atas FAC), di tahun 2021 terjadi lagi 3 kasus (1 RWDC dan 2 MTC) dan di tahun 2022 terjadi 4 kasus (1 LWDC, 1 RWDC, dan 2 MTC). Di tahun 2023 terjadi 1 kasus (1 RWDC). Korban kecelakaan kerja di tahun 2021 didominasi oleh pekerja kontrak dan pekerja tetap sedangkan kecelakaan kerja di tahun 2022 dan 2023 semuanya terjadi pada pekerja kontrak. Sebagian besar kecelakaan yang terjadi penyebab langsungnya adalah unsafe acts. Sampai saat ini belum ada analisis menyeluruh dari data investigasi kecelakaan-kecelakaan yang telah dilakukan PT X untuk mendapatkan faktor-faktor penyebab dasar dari semua kecelakaan tersebut. Dengan demikian, penelitian perlu dilakukan, dan karena berhubungan dengan human factor, maka pada penelitian ini akan dianalisis faktor-faktor yang menyebabkan kecelakaan kerja tersebut dengan metode Human Factor Analysis and Classification System (HFACS). Tujuan: Menganalisis faktor-faktor yang memengaruhi kecelakaan kerja di PT X antara tahun 2018 – 2023 dengan metode HFACS. Metode: Penelitian ini adalah penelitian deskriptif analitik dengan pendekatan kualitatif. Data sekunder yang digunakan berupa rekaman kejadian kecelakaan dan laporan investigasi atas 41 kecelakaan di PT X. Data sekunder tersebut kemudian diklasifikasikan sesuai dengan empat (4) tahapan kegagalan di metode HFACS, yaitu unsafe acts, precondition of unsafe acts, unsafe supervision, dan organizational influence. Pengklasifikasian ini divalidasi oleh dua ahli keselamatan kerja, di mana hasil validasinya relatif tinggi (96%). Hasil: Hasil penelitian menjelaskan bahwa faktor-faktor HFACS yang mempengaruhi kecelakaan terbesar berturut-turut adalah adverse mental state (51,2%), skill-based error (39%), routine violations (34,1%), dan tools/technological dan resource management (masing-masing 31,7%). Kemudian disusul oleh decision error (29,3%), inadequate supervision (22%), failed to correct problem dan organizational process masing-masing (17,1%), lalu supervisory violation dan organizational climate masing-masing (9,8%). Kesimpulan: Faktor-faktor HFACS yang memengaruhi kecelakaan kerja di PT X dapat digunakan sebagai masukan untuk perbaikan program K3 perusahaan guna menurunkan angka kecelakaan dengan memprioritaskannya pada faktor HFACS yang bersifat latent failure baru kemudian pada faktor active failure-nya, karena latent failure - jika diperbaiki- akan menjadi kunci untuk mencegah berulangnya kecelakaan.
The risks of working for the PT X , an oil and gas company located offshore Natuna are relatively high. Throughout 2018 – 2023 there were fluctuations in work accidents at PT. X. Even after two years (2020 and 2019) there was no work accident for the recordable injury category (cases above FAC), in 2021 there were 3 cases (1 RWDC and 2 MTC) and in 2022 there were 4 cases (1 LWDC, 1 RWDC, and 2 MTC). In 2023 there was 1 case (1 RWDC). Work accident victims in 2021 are dominated by contract workers and permanent workers, while work accidents in 2022 and 2023 all occur in contract workers. Most of the accidents that occur are directly caused by unsafe acts. Until now there has been no comprehensive analysis of accident investigation data that has been carried out by PT X to obtain the basic causal factors of all these accidents. Thus, research needs to be carried out, and because it is related to human factors, this research will analyze the factors that cause work accidents using the Human Factor Analysis and Classification System (HFACS) method. Objective: Analyzing the factors that influence work accidents at PT X between 2018 – 2023 using the HFACS method. Method: This research is descriptive analytical research with a qualitative approach. The secondary data used is in the form of recordings of accidents and investigation reports on 41 accidents at PT X . This classification was validated by two occupational safety experts, where the validation results were relatively high (96%). Results: The research results explain that the HFACS factors that influence the biggest accidents are adverse mental state (51.2%), skill-based errors (39%), routine violations (34.1%), and tools/technological and resources, respectively. management (31.7% each). Then followed by decision errors (29.3%), inadequate supervision (22%), failed to correct problems and organizational processes respectively (17.1%), then supervisory violations and organizational climate respectively (9.8%). Conclusion: The HFACS factors that influence work accidents in PT X can be used as input for improving the company's H&S program to reduce the number of accidents by prioritizing the HFACS factors which are latent failures and then the active failure factors, because latent failures - if corrected - will become key to preventing recurrence of accidents.
