Membangun Program Kemitraan sebagai Kebijakan untuk Menurunkan Non-Performing Loans UMKM di Kabupaten Buleleng
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis penyebab tingginya Non-Performing Loans (NPL) UMKM di Kabupaten Buleleng dan alternatif kebijakan untuk mengatasinya. Dalam empat tahun terakhir, NPL UMKM di daerah ini tertinggi di Bali, berdampak pada UMKM, perbankan, dan ekonomi nasional. Observasi menunjukkan banyak UMKM sektor pertanian dan perkebunan mengalami kredit macet akibat pendapatan tidak menentu, tingginya biaya produksi, serta fluktuasi harga komoditas. Akses pemasaran yang terbatas dan rendahnya kualitas SDM memperburuk kondisi ini. Berakhirnya relaksasi restrukturisasi kredit pasca-pandemi semakin meningkatkan rasio NPL UMKM. Penelitian ini menggunakan metode kualitatif dengan sumber data primer dan data sekunder. Data primer diperoleh melalui wawancara terhadap lima pemilik UMKM yang mengalami kredit macet. Data sekunder diperoleh melalui dokumentasi terkait kebijakan pemberdayaan UMKM yang telah dilakukan oleh Dinas Perdagangan, Perindustrian, Koperasi, dan UKM Kabupaten Buleleng. Hasil penelitian menunjukkan bahwa rendahnya pendapatan, keterbatasan pemasaran, dan kualitas SDM yang kurang menjadi faktor utama tingginya NPL UMKM. Solusi jangka panjang bagi Dinas Perdagangan, Perindustrian, dan Koperasi, UKM Kabupaten Buleleng yakni membangun platform kemitraan antara UMKM dan perusahaan besar dalam rantai pasok. Kemitraan ini akan memberikan akses pasar lebih luas, stabilitas harga, serta dukungan teknologi dan keahlian, yang pada akhirnya meningkatkan kemampuan UMKM dalam membayar kredit. Rekomendasi ini dapat dituangkan dalam Peraturan Bupati tentang Penguatan Kemitraan UMKM dalam Rantai Pasok untuk Mengurangi NPL UMKM. This policy paper analyzes the causes of the high Non-Performing Loans (NPL) of MSMEs in Buleleng Regency and offers policy alternatives to address the issue. Over the past four years, MSME NPLs in this region have been the highest in Bali, impacting MSMEs, banking institutions, and the national economy. Observations indicate that many MSMEs in the agriculture and plantation sectors experience loan defaults due to unstable income, high production costs, and fluctuating commodity prices. Limited market access and low human resource quality further exacerbate this condition. The expiration of post-pandemic credit restructuring relaxation has also increased the MSME NPL ratio. This policy paper employs a qualitative method through interviews with five MSME owners who have experienced loan defaults. The analysis reveals that low income, restricted market access, and inadequate human resource quality are the main factors contributing to high MSME NPLs. As a long-term solution, the Buleleng Regency Office of Trade, Industry, Cooperatives, and MSMEs is advised to establish a partnership platform between MSMEs and large corporations within the supply chain. This partnership would provide broader market access, price stability, and support in technology and expertise, ultimately enhancing MSMEs' ability to repay loans. This recommendation can be formalized in a Regent Regulation on Strengthening MSME Partnerships in the Supply Chain to Reduce MSME NPLs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".