PENGEMBANGAN KOMODITAS HORTIKULTURA UNGGULAN LEMBANG TADONGKON BERDASARKAN ANALISIS LQ, SHIFT SHARE, DAN TIPOLOGI KLASSEN
Bibliographic record
Abstract
Lembang Tadongkon is a highland area with considerable potential for horticultural development. Its fertile soils, deeply rooted farming traditions, and the availability of local labor make it highly suitable for development as an agropolitan hub. This study aims to identify leading horticultural commodities in the region and formulate strategies for their sustainable development. The methods employed include Location Quotient (LQ) analysis, Shift Share analysis, and SWOT analysis. Data were obtained from both primary and secondary surveys and analyzed in two stages: qualitative SWOT (SWOT matrix) and quantitative SWOT (weighting and scoring). The results of the LQ, Shift Share, and Klassen typology analyses for eleven horticultural commodities in Lembang Tadongkon reveal that five commodities have LQ >1 spinach (1.21), cabe katokkon chili (1.07), long beans (1.24), water spinach (1.09), and eggplant (1.38) indicating that they are base commodities with strong development potential. However, only tomatoes exhibited positive growth in the Shift Share analysis (0.84), while the remaining commodities showed negative growth. According to the Klassen typology, all commodities remain in the “Lagging” category, as both their contribution and growth rates are below the regional average. Accordingly, strategies are required to enhance cultivation technology, post-harvest management, and institutional capacity to transform base commodities into sustainable leading commodities. Quantitative SWOT analysis positions Lembang Tadongkon in Quadrant I (progressive), indicating that the area possesses substantial strengths and opportunities for integrated development. Recommended strategies include production expansion, downstream product development, strengthening farmer organizations, and leveraging agro-tourism potential based on local flagship commodities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".