Evaluating Fisherman Insurance Participation using Bagging Multivariate Adaptive Regression Splines
DOI:
https://doi.org/10.30812/varian.v8i3.5373Keywords:
Bootstrap Aggregating, Fishermen, Independent Fishermen’s Insurance, Multivariate Adaptive Regression, Spline, RiskAbstract
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.
Downloads
References
Al-Musaylh, M. S., Deo, R. C., Adamowski, J. F., & Li, Y. (2018). Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35, 1–16. https://doi.org/10.1016/j.aei.2017.11.002
Amin, M. M., Zainal, A., Azmi, N. F. M., & Ali, N. A. (2020). Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection. IOP Conference Series: Materials Science and Engineering, 864(1), 012059. https://doi.org/10.1088/1757-899X/864/1/012059
Asyya, M. F., & Agusta, I. (2021). Analisis Partisipasi Nelayan dalam Program Asuransi Nelayan. Jurnal Sains Komunikasi dan Pengembangan Masyarakat [JSKPM], 5(2), 294–311. https://doi.org/10.29244/jskpm.v5i2.818
Bose, A., Hsu, C.-H., Roy, S. S., Lee, K. C., Mohammadi-ivatloo, B., & Abimannan, S. (2021). Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network. Computers and Electrical Engineering, 95, 107405. https://doi.org/10.1016/j.compeleceng.2021.107405
Brahmantyo, Y., Riaman, R., & Sukono, F. (2021). Willingness to Pay of Fishermen Insurance Using Logistic Regression with Parameter Estimated by Maximum Likelihood Estimation Based on Newton Raphson Iteration. Jurnal Matematika Integratif, 17(1), 15. https://doi.org/10.24198/jmi.v17.n1.32037.15-21
Cox, D. R., & Snell, E. J. (1989, May 15). Analysis of Binary Data, Second Edition. CRC Press.
De Andres, J., Lorca, P., De Cos Juez, F. J., & Sánchez-Lasheras, F. (2011). Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Systems with Applications, 38(3), 1866–1875. https://doi.org/10.1016/j.eswa.2010.07.117
Eubank, R. L. (1999, February 9). Nonparametric Regression and Spline Smoothing (0th ed.). CRC Press. https://doi.org/10.1201/9781482273144
Friedman, J. H. (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/aos/1176347963
Gonzalez Ariza, A., Arando Arbulu, A., Navas González, F. J., Delgado Bermejo, J. V., & Camacho Vallejo, M. E. (2021). Discriminant Canonical Analysis as a Validation Tool for Multivariety Native Breed Egg Commercial Quality Classification. Foods, 10(3), 632. https://doi.org/10.3390/foods10030632
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. https://doi.org/10.1007/978-0-387-84858-7
Hasyim, M., Rahayu, D. S., Muliawati, N. E., Hayuhantika, D., Puspasari, R., Anggreini, D., Hastari, R. C., Hartanto, S., & Utomo, F. H. (2018). Bootstrap Aggregating Multivariate Adaptive Regression Splines (Bagging MARS) to Analyse the Lecturer Research Performance in Private University. Journal of Physics: Conference Series, 1114, 012117. https://doi.org/10.1088/1742-6596/1114/1/012117
Hlokoe, V. R., Mokoena, K., & Tyasi, T. L. (2022). Using multivariate adaptive regression splines and classification and regression tree data mining algorithms to predict body weight of Nguni cows. Journal of Applied Animal Research, 50(1), 534–539. https://doi.org/10.1080/09712119.2022.2110498
Holmes, S., & Huber, W. (2019). Modern statistics for modern biology. Cambridge University Press.
Otok, B. W., Rumiati, A. T., Ampulembang, A. P., & Azies, H. A. (2023). ANOVA Decomposition and Importance Variable Process in Multivariate Adaptive Regression Spline Model. International Journal on Advanced Science, Engineering and Information Technology, 13(3), 928–934. https://doi.org/10.18517/ijaseit.13.3.17674
Rupilu, R. A. H. W., & Rosadi, D. (2024). Classification Analysis Using Bootstrap Aggregating Multivariate Adaptive Regression Spline (Bagging MARS). BAREKENG: Jurnal Ilmu Matematika dan Terapan, 18(3), 1381–1390. https://doi.org/10.30598/barekengvol18iss3pp1381-1390
Seno, M. E., Zeini, H. A., Imran, H., Noori, M., Henedy, S. N., & Ghazaly, N. M. (2024). Advancing in creep index of soil prediction: A groundbreaking machine learning approach with Multivariate Adaptive Regression Splines. Results in Materials, 24, 100641. https://doi.org/10.1016/j.rinma.2024.100641
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ulil Azmi, Soehardjoepri, Prilyandari Dina Saputri, Thalia Rizki Salsabila, Widya Iswara, Roslinazairimah Zakaria
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Most read articles by the same author(s)
- Ulil Azmi, Wawan Hafid Syaifudin, Peramalan Harga Komoditas dengan Menggunakan Metode Arima-Garch , Jurnal Varian: Vol. 3 No. 2 (2020)
- Wawan Hafid Syaifudin, Ulil Azmi, Aplikasi Model Predictive Control (MPC) Pada Optimasi Portofolio Komoditas , Jurnal Varian: Vol. 3 No. 2 (2020)
- Galuh Oktavia Siswono, Ulil Azmi, Wawan Hafid Syaifudin, Mortality Projection on Indonesia's Abridged Life Table to Determine the EPV of Term Annuity , Jurnal Varian: Vol. 4 No. 2 (2021)
- Ulil Azmi, Soehardjoepri Soehardjoepri, Rudi Prihandoko, Iqra Asif, Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake , Jurnal Varian: Vol. 7 No. 1 (2023)
