Manajemen risiko pekerjaan prasarana sarana dan utilitas umum (PSU) jalan menggunakan metode severity index dan root cause analysis berbasis Python - Cintya Violita Saruni; Fabian Johanes Manoppo; Marthin Dody J Sumayouw (PADURAKSA: Jurnal Teknik Sipil Universitas Warmadewa, 2025)
Infrastructure, Facilities, and Public Utilities (PSU) are physical facilities that play a crucial role in creating a healthy, safe, and affordable residential environment. One of the main aspects of PSU is the construction of neighborhood roads in residential and settlement areas. Risk management is essential to ensuring project success by identifying and mitigating potential risks that may hinder PSU road construction. This study aims to analyze and manage risks in PSU road projects in North Sulawesi using the Severity Index (SI) and Root Cause Analysis (RCA) methods, implementing the analysis with Python, and developing mitigation strategies. Data were collected through interviews and questionnaires with contractors, Commitment Making Officials (PPK), field directors, and project supervisors. To ensure data reliability, each risk variable was tested for relevance before further analysis. The SI method was used to prioritize risks based on their impact, while the RCA method identified the root causes of dominant risks. Python was applied to automate the Severity Index analysis, facilitate the RCA 5 Whys process, and visualize data to enhance efficiency and accuracy. The SI analysis identified 11 dominant risks out of 28, covering natural, material and personnel, cost and time, and environmental risks. Key risks included unpredictable weather, design changes in the field, and project delays. The root causes included a lack of consideration for weather factors, insufficient technical personnel, and ineffective communication. The proposed mitigation strategies include developing contingency plans, increasing technical personnel, and improving coordination among stakeholders. This study helps PSU road construction users and service providers identify, understand, and respond to risks more effectively while also providing Python as a risk management tool to automate risk identification and analysis using the SI and RCA methods for future applications.