Utilization of Artificial Neural Network for Assessment of Relationship Marketing Influence of Tomato (Solanum lycopersicum) Farmers
Bibliographic record
Abstract
Tomato plants have the potential to be a significant source of income because they are consumed daily and have high economic value.However, tomato production is constrained by suboptimal distribution, which is evident from low consumer loyalty due to inadequate relationship marketing.The Lembang region of West Java Province, Indonesia, a prominent horticultural center for tomatoes, serves as the research location.This study aims to analyze the impact of relationship marketing by tomato farmers on consumer loyalty, with the goal of making recommendations to enhance trust and ensure the continuity of tomato production.The analysis employs techniques such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and MicMac methods, which complement and reinforce each other.The analysis results reveal that relationship marketing is the primary factor in building consumer loyalty among tomato farmers in the Lembang area, as demonstrated by the ANFIS program's prediction accuracy of 97.1%.The variables that affect the loyalty of tomato farmers in consumers are price suitability, crop sales, trust in consumers.The implications of this research indicate a relevant relationship between demand and supply so that production continuity and price stability can be maintained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".