Coastal Communities' Experiences in Facing Climate Change: A Study on Socio-Ecological Resilience in the Northern Coast of Java

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Sufriady Syam

Abstract

Climate change significantly impacts coastal communities, affecting their livelihoods, cultural identity, and social cohesion. Although previous research has explored socio-economic and policy responses to climate change, limited attention has been given to subjective experiences and cultural narratives shaping adaptive behaviors. This study addresses this gap by exploring how coastal communities in North Java perceive and interpret environmental changes through an interpretative phenomenological approach. The study reveals that adaptive strategies are deeply embedded in cultural identity, communal solidarity, and spiritual beliefs, challenging conventional adaptation models focused on technical and economic solutions. Data were collected through in-depth interviews and field observations, analyzed using Interpretative Phenomenological Analysis (IPA) to identify themes of environmental perception, local adaptation strategies, and social-ecological resilience. The findings demonstrate that cultural narratives and social networks significantly shape adaptive behaviors, emphasizing the need for culturally sensitive adaptation policies. This research contributes to a more holistic understanding of social-ecological resilience by integrating emotional, cultural, and social dimensions, paving the way for future studies to explore cross-cultural narratives and intergenerational dynamics in climate adaptation.

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References

Brucal, G.E., Colobong, J. B., De Guzman, D. B., De Leon, J. N. M., Samaniego, L. A., and Yong, E. D. (2021). Papaya Maturity Classification in MATLAB Platform using Lab Method and DHT11 Sensor. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), London, United Kingdom, 2021, pp. 171-175, doi: 10.1109/WorldS451998.2021.9514053

Fadhlurrahman, I. (2024). Produksi pepaya nasional naik 25.5% dalam 5 tahun terakhir. Katadata Databoks. Retrieved July 29, 2024. https://databoks.katadata.co.id/datapublish/2024/07/25/volume-produksi-pepaya-menurut-provinsi

Matsuzaka, Y., & Yashiro, R. (2023). AI-Based Computer Vision Techniques and Expert Systems. AI, 4(1), 289-302. https://doi.org/10.3390/ai4010013

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.48550/arXiv.1506.02640

Sadya, S. (2022). Produksi pepaya di Indonesia capai 1,17 juta ton pada 2021. DataIndonesia.id. Retrieved July 1, 2024. https://dataindonesia.id/agribisnis-kehutanan/detail/produksi-pepaya-di-indonesia-capai-117-juta-ton-pada-2021

Shimaoka, A. M., Ferreira, R. C., & Goldman, A. (2024). The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks. SBC Reviews on Computer Science, 4(1), 28–43. https://doi.org/10.5753/reviews.2024.3757

Syahrudin, E., Utami, E., & Hartanto, A. D. (2024). Enhanced YOLOv8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation. J. RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(2), 199–207. https://doi.org/10.29207/resti.v8i2.5529

Utami, H. S., Susanto, S., & Hapsari, D. P. (2022). Keragaman Kualitas Fisik dan Kimia Buah Pepaya Calina di Balumbangjaya. Jurnal Penelitian Pertanian Tropis, 13(1), 109–119. https://doi.org/10.29244/jhi.13.2.109-119

Widyasari, K. B. D. R. N., Rosiani, U. D., & Pramudhita, A. N. (2021). Implementasi Sistem Pendeteksi Tingkat Kematangan Buah Pepaya Menggunakan Metode RGB. SMATIKA Jurnal, 11(1), 32–36. http://dx.doi.org/10.32664/smatika.v11i01.536

Xiao, B., Nguyen, M., & Yan, W. Q. (2023). Fruit ripeness identification using YOLOv8 model. Multimedia Tools and Applications, 28040–28056. http://dx.doi.org/10.1007/s11042-023-16570-9