Penentuan prioritas strategi pengembangan industri pengolahan apel di Bumiaji menggunakan QSPM
Bibliographic record
Abstract
Pengembangan wilayah bertujuan mendorong pertumbuhan daerah melalui optimalisasi sumber daya lokal. Kecamatan Bumiaji di Kota Batu, salah satu sentra produksi apel terbesar di Jawa Timur, menghadapi tantangan penurunan produksi dan luas lahan pertanian apel. Penelitian ini bertujuan merumuskan dan menentukan prioritas strategi pengembangan industri pengolahan apel di Kecamatan Bumiaji menggunakan Quantitative Strategic Planning Matrix (QSPM). Metode meliputi analisis konten untuk mengidentifikasi karakteristik industri, analisis lingkungan internal-eksternal, analisis SWOT untuk merumuskan strategi, dan evaluasi prioritas dengan QSPM. Hasil penelitian menunjukkan industri pengolahan apel berperan penting bagi ekonomi lokal dan pemberdayaan komunitas, dengan kekuatan pada kualitas dan diversifikasi produk. Kelemahan utama meliputi kapasitas produksi terbatas dan fluktuasi harga bahan baku. Peluang mencakup dukungan eksternal dan perkembangan pariwisata, sementara ancaman meliputi rendahnya produktivitas apel dan persaingan daerah lain. Analisis SWOT dan QSPM menghasilkan 11 strategi alternatif, dengan prioritas utama pada inovasi berkelanjutan, peningkatan kualitas produk, dan strategi pemasaran efektif. Implementasi strategi ini diharapkan mendorong pengembangan industri pengolahan apel di Kecamatan Bumiaji, memperkuat ekonomi lokal, meningkatkan kesejahteraan masyarakat, dan mempertahankan apel sebagai komoditas khas Kota Batu.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".