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Kata Kunci :Kecelakaan lalu lintas tambang, tabrakan, sistem pertahanan, Swiss CheeseModel, HFACS-MI
In mining process activities, there are potential hazards that poses a risk to be anaccident. Collision is one of accident types that frequently happen on miningtraffic operations jobsite PT SS (41%) and it has tendency to occur repeatedly.This study aimed to gain an overview of defences system in preventing accidentsaccording to Swiss Cheese Model framework. The research was conducted with aqualitative approach through mining traffic accident data analysis in one ofjobsite in PT SS, an open coal mining contractor company, using the HumanFactors Analysis and Classification System in Mining Industry (HFACS-MI).Based on the analysis of 53 cases of mining traffic accidents, revealed that themost common problems were skill-based errors, adverse mental states,coordination and communication, inadequate leadership, and organizationprocess. It can be concluded that the existing defences system to prevent miningtraffic accidents has not been optimal yet. Therefore, defences systemimprovement, either targeted to the individual or organizational, is needed tocontrol accident risk.
Key words:Mine traffic accident, collision, defences system, Swiss Cheese Model, HFACSMI
This research examines work-related accidents in the mining industry categorized as injury cases, using the Human Factors Analysis and Classification System in Mining Industry (HFACS-MI). The mining industry is known as a high-risk sector, where serious incidents such as major injuries and fatalities frequently occur despite investigations and preventive measures. The study aims to identify the main causal factors of workplace accidents based on the HFACS-MI framework, which classifies human errors and systemic weaknesses within mining organizations. Using data from injury-related accident cases in 2024, both qualitative and quantitative analyses were conducted to assess contributing factors from the operator level up to the organizational level.The findings reveal that the majority of accidents were triggered by unsafe acts, particularly skill-based errors, indicating deficiencies in workers' basic competencies. Additionally, latent failures, such as inadequate supervision and organizational inefficiencies, were also found to play a significant role.
Penelitian ini mengkaji kecelakaan kerja di industri pertambangan yang tergolong dalam kategori cedera, menggunakan metode Human Factors Analysis and Classification System in Mining Industry (HFACS-MI). Industri pertambangan dikenal sebagai sektor dengan risiko tinggi, di mana insiden besar seperti cedera kerja serius dan kematian sering kali terjadi meskipun telah dilakukan investigasi dan tindakan pencegahan.
Studi ini bertujuan untuk mengidentifikasi faktor-faktor penyebab utama kecelakaan berdasarkan pendekatan sistem HFACS-MI yang mengklasifikasikan kesalahan manusia dan kelemahan sistemik dalam organisasi tambang. Berdasarkan data kecelakaan kasus cedera Tahun 2024, analisis dilakukan secara kualitatif dan kuantitatif terhadap berbagai faktor, dari tingkat operator hingga organisasi.
Hasil penelitian mengungkapkan bahwa kecelakaan paling banyak dipicu oleh unsafe acts, terutama skill-based errors, yang menunjukkan kelemahan pada kompetensi atau keterampilan dasar pekerja. Selain itu, faktor latent failures seperti lemahnya supervisi dan ketidakefisienan sistem organisasi juga berperan penting.
This research examines work-related accidents in the mining industry categorized as injury cases, using the Human Factors Analysis and Classification System in Mining Industry (HFACS-MI). The mining industry is known as a high-risk sector, where serious incidents such as major injuries and fatalities frequently occur despite investigations and preventive measures. The study aims to identify the main causal factors of workplace accidents based on the HFACS-MI framework, which classifies human errors and systemic weaknesses within mining organizations. Using data from injury-related accident cases in 2024, both qualitative and quantitative analyses were conducted to assess contributing factors from the operator level up to the organizational level. The findings reveal that the majority of accidents were triggered by unsafe acts, particularly skill-based errors, indicating deficiencies in workers' basic competencies. Additionally, latent failures, such as inadequate supervision and organizational inefficiencies, were also found to play a significant role.
Industri pertambangan merupakan kegiatan industri yang mempunyai risiko tinggi. Faktor manusia telah diidentifikasi sebagai penyebab paling umum terjadinya kecelakaan besar di industri pertambangan. Oleh karena itu, penelitian ini bertujuan menganalisis data kecelakaan di PT. X dengan menggunakan kerangka analisis faktor manusia dan sistem klasifikasi industri pertambangan (HFACS-MI). Metode penelitian ini melibatkan pengumpulan data kualitatif untuk 322 kasus kecelakaan di PT. X yang terjadi pada tahun 2018-2022 dari basis data Sistem Manajemen Insiden yang dikategorikan sebagai cedera yang dapat dicatat. Faktor penyebab kecelakaan ini diberi kode menggunakan kerangka HFACS-MI. Data kecelakaan dianalisis menggunakan statistik deskriptif. Temuan penelitian menunjukkan bahwa 84% dari seluruh kecelakaan melibatkan pekerja kontraktor dan 16% melibatkan pekerja tetap PT. X. Hasil analisis menggunakan kerangka HFACS-MI menunjukkan bahwa setiap lapisan atau tingkatan memberikan kontribusi terhadap kecelakaan, yaitu faktor luar (44%), pengaruh organisasi (68%), kepemimpinan tidak aman (90%), prasyarat tindakan tidak aman (99%), dan tindakan tidak aman (99,7%). Temuan ini menekankan perlunya fokus pada pengurangan jumlah kesalahan manusia selama operasi penambangan untuk mengurangi tren kecelakaan saat ini. Kerangka kerja HFACS-MI telah terbukti menjadi alat penting untuk analisis kecelakaan yang kuat terhadap faktor manusia di pertambangan.
The mining industry is an industrial activity with high risks. Human factors have been identified as the most common cause of major accidents in the mining industry. Therefore, this research aims to analyze accident data at PT. X using the human factors analysis and classification system-mining industry framework (HFACS-MI). This research collected qualitative data for 322 accident cases at PT. X occurring from 2018 to 2022 from the Incident Management System database categorized as recordable injuries. Factors causing the accidents were coded using HFACS-MI framework. Accident data were analyzed using descriptive statistics. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved the PT. X permanent workers. The results of analysis using the HFACS-MI framework show that each layer or level contributes to accidents, namely outside factors (44%), organizational influences (68%), unsafe leadership (90%), preconditions of unsafe acts (99%), and unsafe acts (99.7%). These findings emphasize the need to focus on reducing the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.