Ridho, Muhammad Rasyid (2025) Integration of Spatial Finance-Multi Criteria Decision Analysis with Monte Carlo Risk Simulation for Measuring Credit Risk in the Fisheries Aquaculture Sector. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sektor akuakultur di Indonesia memiliki potensi ekonomi yang besar, namun disisi lain, dihadapkan pada risiko kredit yang tinggi akibat bencana alam seperti banjir, yang berkontribusi pada tingginya NPL. Penilaian risiko kredit yang umum digunakan saat ini belum sepenuhnya mempertimbangkan faktor risiko geografis. Oleh karena itu, penelitian ini memposisikan diri dalam mengembangkan model untuk mengukur risiko kredit akibat banjir. Model ini mengintegrasikan pendekatan Spatial Finance, Spatial Multi-Criteria Decision Analysis (AHP-GIS), dan simulasi risiko Monte Carlo untuk mengukur risiko kredit pada sektor Akuakultur, sebuah domain yang belum pernah dieksplorasi sebelumnya dengan pendekatan ini. Sebagai studi kasus, penelitian ini menggunakan data historis kerugian kredit akibat banjir periode 2020-2022 dari Kabupaten Kampar, Riau. Model ini memetakan zona kerentanan banjir berdasarkan kriteria geospasial yang telah diberi bobot melalui AHP. Hasil utama penelitian ini menunjukkan bahwa penggabungan faktor spasial secara signifikan memengaruhi proyeksi kerugian. Portofolio kredit di zona risiko banjir tinggi menunjukkan estimasi kerugian maksimum (Value at Risk - VaR) sebesar 4,67% lebih tinggi dibandingkan dengan skenario penilaian konvensional. Dengan demikian, model yang diajukan ini memberikan kerangka kerja baru bagi lembaga keuangan. Tujuannya adalah untuk menilai risiko kredit, mengurangi potensi gagal bayar, dan mendukung keberlanjutan pembiayaan di sektor akuakultur dengan mempertimbangkan faktor lingkungan dan geografis.
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Indonesia's aquaculture sector, despite its significant economic potential, faces high credit risk due to natural disasters like floods, contributing to high Non Performing Loans (NPLs). Current credit risk assessments do not fully account for geographical risk factors. Therefore, this study aims to develop a model to measure credit risk caused by floods. This model integrates a Spatial Finance approach, Spatial Multi-Criteria Decision Analysis (AHP-GIS) and Monte Carlo risk simulation to measure credit risk in the aquaculture sector, a domain that has not been previously explored using this approach. As a case study, this research uses historical credit loss data due to floods from 2020-2022 in Kampar Regency, Riau. The model maps flood vulnerability zones based on geospatial criteria weighted through AHP. The main results of this study show that incorporating spatial factors significantly impacts loss projections. Credit portfolios in high flood risk zones
exhibit a maximum loss estimate, Value at Risk, 4.67% higher than conventional assessment scenarios. Thus, the proposed model provides a new framework for financial institutions. Its purpose is to assess credit risk, reduce potential defaults, and support the sustainability of financing in the aquaculture sector by measurably considering environmental factors and geographical factors.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Akuakultur, Banjir, Spatial Finance, Analisis Spasial, Risiko Kredit, Aquaculture, Spatial Finance, Credit Risk, Spatial Analysis, Floods |
| Subjects: | H Social Sciences > HG Finance > HG3751 Credit--Management. |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26101-(S2) Master Thesis |
| Depositing User: | Muhammad Rasyid Ridho |
| Date Deposited: | 04 Aug 2025 11:35 |
| Last Modified: | 04 Aug 2025 11:37 |
| URI: | http://repository.its.ac.id/id/eprint/126697 |
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