Analisis Komoditas Unggulan Pada Kawasan Subsektor Perkebunan Di Kecamatan Balikpapan Timur
Bibliographic record
Abstract
Berdasarkan RTRW Kota Balikpapan Tahun 2012-2032 bahwa Kecamatan Balikpapan Timur berfungsi sebagai pusat perdagangan dan jasa agro skala kota yang memiliki potensi kawasan peruntukkan pertanian. Subsektor perkebunan memiliki luasan wilayah paling besar dibandingkan yang lain dengan persentase sebesar 50,35% dan menjadi salah satu sektor pertanian yang potensial. Pada nilai produktivitas mengalami penurunan dari tahun 2017 ke 2018 sehingga tujuan dalam penelitian ini menentukan komoditi unggulan pada subsektor perkebunan di Kecamatan Balikpapan Timur menggunakan kriteria komoditi unggulan. Metode analisis yang digunakan adalah analisis LQ, Shift-Share, dan survei primer. Sehingga berdasarkan hasil analisis yang dilakukan dapat diketahui bahwa dari analisis LQ yang termasuk sektor basis adalah komoditi karet, kelapa dalam, kopi robusta, lada, kakao dan kemiri. Dari hasil analisis shift-share komoditi lada termasuk komoditi yang progresif atau pertumbuhan yang maju serta dari hasil bobot kriteria komoditi unggulan dan survei primer bahwa komoditi karet menjadi komoditi unggulan di Subsektor Perkebunan Kawasan Pertanian di Kecamatan Balikpapan Timur. Kata kunci: Subsektor Perkebunan, Kriteria Komoditi Unggulan, Komoditi Unggulan
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".