ALVANOV, THOMAS THEO (2025) PENURUNAN TINGKAT RISIKO KECELAKAAN PENGOPERASIAN DRONE MENGGUNAKAN METODE FMEA DAN BOWTIE ANALYSIS. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to identify and evaluate the operational risks of UAV/drones in infrastructure inspections by integrating Failure Modes and Effects Analysis (FMEA) and Bowtie Analysis (BTA). The FMEA assesses severity, occurrence, and detection, which are multiplied to obtain the Risk Priority Number (RPN). The study sample comprises 27 accident reports of drone operations, purposively selected based on incident data from 2018 to 2024. Data analysis involves calculating the RPN to identify critical risks, followed by an in-depth assessment of preventive and mitigative barriers using BTA. Results show that navigation system failure (RPN = 504), battery failure (RPN = 448), and operator error (human error) (RPN = 441) are the highest risk factors. Other risks have RPN values ranging from 48 to 280. Preventive and mitigation barriers play a crucial role in risk control, with each failure mode having at least two preventive barriers and two mitigation barriers. After implementing risk control strategies, the highest RPN values reduced to 135, 128, and 108, while other risks ranged between 28 and 96. This study demonstrates the effectiveness of FMEA and BTA integration in identifying, analyzing, and reducing drone operational risks, providing valuable insights for optimizing mitigation strategies. Keywords: drone, FMEA, Bowtie Analysis, risk, infrastructure Penelitian ini bertujuan untuk mengidentifikasi dan mengevaluasi risiko operasional UAV/drone dalam kegiatan inspeksi infrastruktur melalui integrasi metode Failure Modes and Effects Analysis (FMEA) dan Bowtie Analysis (BTA). Variabel FMEA yang digunakan meliputi tingkat keparahan (severity), frekuensi kejadian (occurrence), dan kemampuan deteksi (detection) yang dihitung menjadi Risk Priority Number (RPN). Sampel penelitian terdiri dari 27 data insiden kecelakaan pengoperasian drone yang diambil dengan teknik purposive sampling berdasarkan ketersediaan laporan kecelakaan dari tahun 2018 hingga 2024. Teknik analisis data dilakukan dengan menghitung RPN untuk mengidentifikasi risiko kritis, kemudian dianalisis secara mendalam terkait barrier preventif dan mitigasi melalui BTA. Hasil penelitian menunjukkan bahwa gangguan sistem navigasi RPN=504), kegagalan baterai (RPN=448), dan kelalaian operator (human error) (RPN=441) menjadi faktor kegagalan tertinggi. Risiko lainnya memiliki nilai RPN yang berkisar antara 48-280. Barrier preventif dan mitigasi memainkan peran penting dalam pengendalian risiko, dengan setiap penyebab memiliki minimal dua barrier preventif dan dua barrier mitigasi untuk konsekuensi. Setelah penerapan strategi pengendalian, RPN tertinggi menurun menjadi 135, 128, dan 108, sementara risiko lainnya berkisar antara 28-96. Penelitian ini menunjukkan efektivitas integrasi FMEA dan BTA dalam mengidentifikasi, menganalisis, dan mengurangi risiko operasional drone, serta membantu merancang strategi mitigasi yang lebih optimal. Kata Kunci: Drone, FMEA, Bowtie Analysis, risiko, infrastruktur
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