Evaluating Fisherman Insurance Participation using Bagging Multivariate Adaptive Regression Splines - Azmi, Ulil; Soehardjoepri, Soehardjoepri; Saputri, Prilyandari Dina; Salsabila, Thalia Rizki; Iswara, Widya; Zakaria, Roslinazairimah (Universitas Bumigora, 2025)
The Fishermen’s Insurance Premium Assistance Program and the Independent Fishermen’s Insurance Scheme are initiatives by the Indonesian government aimed at enhancing the protection of fishermen, whose occupations are considered high-risk compared to other professions. One of the regions actively participating in both programs is Lekok District, located in Pasuruan Regency, East Java Province. The objective of this research is to analyze the factors influencing fishermen’s participation in self-funded insurance schemes using the Multivariate Adaptive Regression Spline method. The research is based on primary data collected through direct surveys and structured questionnaires distributed to fishermen in Lekok District. The results of this research are that five key variables significantly influence participation, with the most influential factor being participation in outreach or socialization activities. Other important factors include the number of family members (X4), income (X3), and age (X1), while fishing experience (X5) does not show a significant effect. The model’s classification accuracy on the training data reached 82%, while on the test data it was 75.8%. Furthermore, applying the bootstrap aggregation technique to Multivariate Adaptive Regression Splines models significantly improved classification accuracy to 92% on the training data and 100% on the test data. The findings are expected to support stakeholders in formulating strategies to increase fishermen’s engagement in independent insurance programs. Strengthening such participation is crucial for reducing occupational risks, ensuring the sustainability of fishing activities, and improving the welfare and resilience of the fishing community.