Analisis Efisiensi Rantai Pasok Bawang Merah Di Kabupaten Bantul
Bibliographic record
Abstract
Bawang merah (Allium ascalonicum) sebagai salah satu komoditas unggulan Kabupaten Bantul yang memberikan kontribusi cukup tinggi terhadap perkembangan ekonomi. Tujuan dari penelitian ini adalah menganalisis kondisi rantai pasok bawang merah dan menyusun alternatif skenario sistem manajemen rantai pasok bawang merah di Kabupaten Bantul. Penelitian berlangsung pada bulan April – Juli 2019. Penelitian menggunakan analisis deskriptif, evaluasi dengan membandingkan aktivitas anggota rantai pasok dengan menggunakan analisis marjin pemasaran, farmer’s share serta analisis AHP. Teknik pengambilan sampel yang digunakan dalam penelitian ini adalah snowball sampling, sejumlah 50 petani dan 10 pedagang. Hasil penelitian menunjukan bahwa terdapat 3 saluran rantai pasok bawang merah di Kabupaten Bantul, saluran I (petani-pedagang besar lokal-pengecer lokal-konsumen), saluran II (petani-pedagang pengumpul-pedagang besar lokal-pengecer lokal-konsumen), saluran III (petani-pedagang pengumpul-pedagang besar non lokal-pengecer non lokal-konsumen). Berdasarkan analisis AHP, dalam membentuk manajemen rantai pasokan bawang merah yang efisien, kriteria meningkatkan kemitraan atau bekerjasama semua pihak menjadi prioritas yang paling berperan penting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".