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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.
Keselamatan kerja pada jalur hauling di industri pertambangan batubara merupakan aspek penting yang memerlukan perhatian serius, mengingat tingginya risiko kecelakaan kerja pada aktivitas pengangkutan material. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi kecelakaan kerja dan menyusun model pencegahan berbasis Human Factor Analysis and Classification System for Mining Industry (HFACS-MI) serta metode investigasi Australian Transport Safety Bureau (ATSB). Pendekatan campuran digunakan dalam penelitian ini, dengan data kuantitatif diperoleh melalui survei terhadap 420 operator dump truck di tiga perusahaan tambang terbuka di Kalimantan, serta data kualitatif dari wawancara mendalam, Focus Group Discussion (FGD), dan observasi langsung di lapangan. Analisis regresi linier berganda digunakan untuk menguji pengaruh enam variabel independen faktor eksternal, pengaruh organisasi, kepemimpinan tidak berkeselamatan, pengendalian risiko, kondisi lingkungan, dan tindakan individu terhadap kecelakaan. Hasil penelitian menunjukkan bahwa seluruh variabel memiliki pengaruh signifikan, dengan kepemimpinan tidak berkeselamatan dan kondisi lingkungan sebagai faktor dominan penyebab kecelakaan. Temuan ini mengindikasikan perlunya penguatan pengawasan, perbaikan perilaku kerja operator, serta peningkatan kualitas jalur hauling. Model pencegahan yang diusulkan menitikberatkan pada penguatan kepemimpinan, pengendalian risiko, dan perawatan infrastruktur hauling secara berkelanjutan untuk menurunkan angka kecelakaan kerja di sektor pertambangan.
Occupational safety in hauling roads within the coal mining industry is a critical aspect that requires serious attention, considering the high risk of work accidents during material transportation activities. This study aims to analyze the factors influencing occupational accidents and to develop a preventive model based on the Human Factor Analysis and Classification System for Mining Industry (HFACS-MI) and the Australian Transport Safety Bureau (ATSB) investigation method. A mixed-method approach was used, with quantitative data collected through a survey of 420 dump truck operators across three open-pit mining companies in Kalimantan, and qualitative data gathered from in-depth interviews, focus group discussions (FGDs), and direct field observations. Multiple linear regression analysis was employed to assess the influence of six independent variables external factors, organizational influence, unsafe leadership, risk control, environmental conditions, and individual actions on work accidents. The results indicated that all variables had a significant effect, with unsafe leadership and environmental conditions emerging as the dominant contributing factors. These findings highlight the need to strengthen supervision, improve operator behavior, and enhance the quality of hauling road infrastructure. The proposed accident prevention model emphasizes the reinforcement of leadership roles, risk control management, and continuous improvement of hauling infrastructure to reduce the incidence of occupational accidents in the mining sector.
Kelelahan merupakan hal umum yang dikeluhkan oleh pekerja dan hampir 20% pekerja melaporkan gejala kelelahan. Khusus pada pengemudi, kelelahan berkontribusi secara signifikan terhadap kecelakaan transportasi. Selain meningkatnya kerugian akibat kecelakaan, kelelahan mengemudi juga menyebabkan kerugian finansial yang besar di sesluruh dunia. Kelelahan mengemudi juga dialami oleh pekerja tambang batubara di Indoneisa, khususnya pekerja operator. Penelitian ini bertujuan untuk mendapatkan determinan strategis kelelahan dari faktor individu, faktor pekerjaan dan faktor eksternal pada operator tambang batubara di Kalimantan dan Sumatra tahun 2021. Penelitian ini menggunakan desain potong lintang pada 2 perusahaan tambang batubara, 7 lokasi tambang, dan 480 operator. Pengumpulan data dilakukan secara daring dengan menggunakan kuesioner. Kelelahan diukur dengan menggunakan 3 instrumen yaitu checklist individual streght-20 (CIS-20), multidimensionall fatigue inventory-20 (MFI-20), dan swedish occupational fatigue inventory (SOFI). Tipe kelelahan yang diteliti meliputi kelelahan umum, kelelahan fisik, kelelahan mental, dan kelelahan emosional. Beberapa instrumen standar juga digunakan dalampenelitian ini seperti perceived stress scale untuk mengukur stres, dutch boredom scale untuk mengukur rasa bosan, dan Pittsburg sleep quality index untuk mengukur kualitas tidur. Analisis data menggunakan chi-square dan regresi logistik ganda. Operator yang menjadi responden penelitian mempunyai umur 32,13 ± SD 6,1 tahun (21 – 58 tahun), sedangkan IMT diperoleh rerata sebesar 24,86 ± SD 3,1 dengan rentang (16,51 – 33,75) serta IMT terbanyak 24,22. Terdapat 26,9% operator termasuk kategori obesitas. Mayoritas operator sudah menikah (85%) dan sebagian besar (63,5%) tinggal di luar mess dengan keluarga (55,8%) serta mayoritas (96%) berpendidikan SMA atau sederajat. Rerata masa kerja operator adalah 9,52 ± SD 4,2 tahun dengan rentang (124 tahun) serta masa kerja terbanyak adalah 10 tahun. Sebagian besar operator bekerja di area tambang (65,8%). Jumlah operator berdasarkan pola shift dan waktu shift, masingmasing 240 (50%). Prevalensi kelelahan pada operator berkisar antara 20%-31% dengan kelelahan umum 24,8%, kelelahan fisik 22,3%, kelelahan mental 32,3%, dan kelelahan emosional 30,6%. Determinan strategis kelelahan pada operator terdiri dari faktor individu yang meliputi tingkat stres, kualitas tidur, gangguan eksternal tidur, faktor pekerjaan meliputi lokasi kerja di tambang, dan faktor eksternal yaitu tinggal dengan keluarga. Sedangkan faktor pola shift kerja dan masa kerja merupakan faktor kontekstual. Faktor yang menjadi determinan pada semua tipe kelelahan adalah faktor tingkat stres dan lokasi kerja. Determinan strategis pada kelelahan umum adalah tingkat stres (OR=3,0), lokasi kerja (OR=2,5), kualitas tidur (OR=1,8), dan tinggal dengan keluarga (OR=1,6). Pada kelelahan fisik, determinan strategis adalah tingkat stres (OR=2,5), gangguan eksternal tidur (OR=2,2), dan lokasi kerja di tambang (OR=1,7). Kelelahan mental mempunyai determinan strategis yaitu kualitas tidur (OR= 2,1), lokasi kerja di tambang (OR=2,1), tingkat stres (OR=1,7) dan gangguan eksternal tidur (OR=1,6). Sedangkan kelelahan emosional mempunyai determinan strategis yaitu tingkat stres (OR=2,0), lokasi kerja (OR=1,9), dan kualitas tidur (OR=1,9). Kelelahan emosional juga mempunyai faktor pola shift yang merupakan faktor kontekstual dengan OR= 1,9 dan masa kerja dengan OR=1,6. Dapat disimpulkan bahwa determinan kelelahan pada operator tambang batubara meliputi faktor individu, faktor pekerjaan, dan faktor eksternal. Kelelahan mental merupakan tipe kelelahan yang paling banyak dirasakan oleh operator. Determinan kelelahan yang terdapat pada semua tipe kelelahan adalah tingkat stres dan lokasi kerja. Semua determinan dapat menjadi perhatian dalam pengembangan kebijakan dan program manajemen risiko kelelahan di perusahaan tambang batubara. Kata kunci: kelelahan, kerja shift, kualitas tidur, operator batubara, stress
Fatigue is a common complaint by workers, and almost 20% of workers report symptoms of fatigue. Especially for drivers, fatigue contributes significantly to transportation accidents. In addition to the increasing loss due to accidents, driving fatigue is also causing many financial losses worldwide. Fatigued driving is also experiencing by coal mining workers in Indonesia, especially operator workers. This study aims to obtain strategic determinants of fatigue from individual factors, occupational factors, and external factors for coal mining operators in Kalimantan and Sumatra in 2021. This study uses a cross-sectional design for two coal mining companies, seven mine sites, and 480 operators. Data collection was online using a questionnaire. Fatigue was measured using three instruments, namely the individual strength-20 checklist (CIS-20), the multidimensional fatigue inventory-20 (MFI-20), and the Swedish occupational fatigue inventory (SOFI). The types of fatigue studied included general fatigue, physical exhaustion, mental fatigue, and emotional exhaustion. Several standard instruments were used in this study, such as the perceived stress scale to measure stress, the Dutch boredom scale to measure boredom, and the Pittsburgh sleep quality index to measure sleep quality. Data analysis used chi-square and multiple logistic regression. Operators who became research respondents had an age of 32.13 ± SD 6.1 years (21 – 58 years), while the BMI obtained an average of 24.86 ± SD 3.1 with a range (16.51 – 33.75) and the highest BMI 24.22. There are 26.9% of operators included in the obese category. The majority of operators are married (85%), and most (63.5%) live outside the mess with their families (55.8%), and the majority (96%) have a high school education or equivalent. The average operators' tenure is 9.52 ± SD 4.2 years with a range (1-24 years), and the most tenure is ten years. Most of the operators work in the mining area (65.8%). The number of operators based on shift patterns and shift times is 240 (50%). The prevalence of fatigue in operators ranges from 20%-31%, with general fatigue 24.8%, physical fatigue 22.3%, mental fatigue 32.3%, and emotional fatigue 30.6%. The strategic determinants of operator fatigue consist of individual factors, including stress levels, sleep quality, external sleep disturbances, work factors including work locations in the mine, and external factors, namely living with family. The work shift pattern and working period are contextual factors. Factors that determine all types of fatigue are stress levels and work locations. The strategic determinants of general fatigue were stress level (OR=3.0), work location (OR=2.5), sleep quality (OR=1.8), and living with family (OR=1.6). On physical exhaustion, strategic determinants were stress level (OR=2.5), external sleep disturbance (OR=2.2), and work location in the mine (OR=1.7). Mental fatigue has strategic determinants, namely sleep quality (OR = 2.1), work location in the mine (OR = 2.1), stress level (OR = 1.7) and external sleep disturbances (OR = 1.6). Meanwhile, emotional exhaustion has strategic determinants, namely stress level (OR=2.0), work location (OR=1.9), and sleep quality (OR=1.9). Emotional fatigue has a shift pattern as a contextual factor with OR = 1.9 and job tenure with OR = 1.6. The conclusion is the determinants of fatigue in coal mine operators include individual factors, occupational factors, and an external factor. Mental fatigue is the type of fatigue most felt by operators. The determinants of fatigue found in all fatigue types are work sites and stress levels. All determinants might be accountable for developing fatigue policy and risk management programs at coal mining companies. Keywords: coal mining operator, fatigue, shift work, sleep quality, stress