Rahmaningrum, Ardilla Yeni Rahmaningrum (2026) Optimasi Portofolio Kredit Pinjaman Menggunakan Model Markowitz Dan LPM untuk Meminimalkan Risiko Gagal Bayar. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pengelolaan portofolio kredit yang baik menjadi kunci penting bagi lembaga keuangan agar tetap sehat secara finansial sekaligus mampu menekan risiko gagal bayar, terutama di tengah pesatnya pertumbuhan layanan financial technology yang menuntut strategi alokasi kredit yang lebih terukur. Berbagai pendekatan kuantitatif telah dikembangkan
untuk mengoptimalkan alokasi suatu portofolio, di antaranya Model Markowitz dan metode Lower Partial Moment (LPM). Model markowitz dan LPM mengukur risiko dengan cara berbeda, namun perbandingan sistematis keduanya pada portofolio kredit di Indonesia masih jarang dilakukan. Tujuan dari penelitian ini adalah menerapkan dan membandingkan kedua metode tersebut dengan penyelesaian optimasi menggunakan Quadratic Programming untuk meminimalkan risiko gagal bayar. Data yang digunakan berupa data outstanding pinjaman bulanan LPBBTI (Layanan Pendanaan Bersama Berbasis Teknologi Informasi) pada enam provinsi di Pulau Jawa untuk periode Desember 2023 hingga Desember 2025. Hasil penelitian menunjukkan bahwa kedua metode secara konsisten menempatkan Jawa Timur sebagai wilayah dengan bobot terbesar dan menghasilkan portofolio yang jauh lebih efisien dibandingkan alokasi kredit ke satu wilayah tunggal. Model Markowitz unggul dalam efisiensi risiko-return secara keseluruhan, sedangkan metode LPM memberikan perlindungan yang lebih kuat terhadap penurunan nilai outstanding pinjaman. Temuan ini menunjukkan bahwa Jawa Timur menjadi wilayah paling optimal dalam menekan risiko gagal bayar, dan kedua metode dapat menjadi pertimbangan alternatif bagi lembaga keuangan sesuai dengan profil toleransi risikonya.
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Good credit portfolio management is essential for financial institutions to maintain financial health while mitigating default risk, particularly amidst the rapid growth of financial technology services that demand more measurable credit allocation strategies. Various quantitative approaches have been developed to optimize portfolio allocation, including the Markowitz Model and the Lower Partial Moment (LPM) method. Although the Markowitz Model and LPM measure risk differently, systematic comparisons between the two regarding credit portfolios in Indonesia remain scarce. The objective of this study is to apply and compare both methods using Quadratic Programming optimization to minimize default risk. The dataset comprises monthly outstanding loan data from LPBBTI (Information Technology-Based Co-Funding Services) across six provinces on Java Island for the period of December 2023 to December 2025. The results show that both methods consistently assign the largest weight to East Java and generate a significantly more efficient portfolio compared to single-region credit allocation. The Markowitz Model excels in overall risk-return efficiency, whereas the LPM method provides stronger protection against downside declines in loan outstanding values. These findings indicate that East Java is the most optimal region for minimizing default risk, and both methods can serve as viable alternatives for financial institutions according to their risk tolerance profiles.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Portofolio Kredit, Risiko Gagal Bayar, Model Markowitz, Lower Partial Moment, Quadratic Programming. Credit Portfolio, Default Risk, Markowitz model, Lower Partial Moment, Quadratic Programming |
| Subjects: | H Social Sciences > HG Finance > HG3751 Credit--Management. H Social Sciences > HG Finance > HG4012 Mathematical models H Social Sciences > HG Finance > HG4529.5 Portfolio management Q Science > QA Mathematics Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Ardilla Yeni Rahmaningrum |
| Date Deposited: | 29 Jul 2026 02:25 |
| Last Modified: | 29 Jul 2026 02:25 |
| URI: | http://repository.its.ac.id/id/eprint/139372 |
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