GIS-AHP Based Suitability Assessment Model for Smallholder Coffee Plantations: A Case Study from Jember, Indonesia
Bibliographic record
Abstract
This study develops models to classify the suitability of smallholder coffee plantations, aiming to strengthen the coffee agroindustry that depends on smallholder farmers as its main raw material source.Since most coffee production areas and farmers come from smallholder plantations, the supply of coffee cherries largely relies on this sector.The research integrates Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods to map plantation suitability in Jember Regency, East Java Province.The model effectively identified and mapped 85,033.53hectares of smallholder coffee plantations.Suitability analysis revealed that 9.32% of plantations were categorized as non-potential, 32.72% as developing, and 57.96% as potential.These results demonstrate the model's capability to visualize and evaluate the distribution and potential of smallholder coffee plantations in the region.The findings offer valuable insights for regional development planning, particularly in determining priority areas for infrastructure investment, farmer empowerment, and agroindustrial expansion.Additionally, the model supports land use policy by providing spatially detailed information to optimize plantation development while minimizing environmental risks.This framework can also be applied to other smallholder-based agricultural systems in tropical regions to promote evidence-based decision-making and sustainable agroindustrial growth.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".