Selection of the Best Village Crop Potential Using the Multi-Attribute Border Approximation Area Comparison (MABAC) Method
Bibliographic record
Abstract
The difference in the location of each geographical condition of each village resulted in many different types of superior agricultural products in each village, this resulted in not all villages in Langkat Regency being able to utilize the crops in their village. This has caused the Community and Village Empowerment Service of Langkat Regency to work very hard in providing support for the progress of each village in developing existing agricultural products. The support provided by the government through the Langkat Regency DPMD is subsidized fertilizer, subsidized seeds and so on. Based on the results of research that has been conducted at the DPMD of Langkat Regency, the selection process to determine the village with the best agricultural products which is done manually can slow down the results of the decisions given and the results obtained are ineffective and inefficient. In overcoming this, it is necessary to build a system to streamline the process of selecting villages with the best agricultural products that have been properly computerized by utilizing the process of the Decision Support System (DSS). In this study a Decision Support System (DSS) will be built using the Multi-Attribute Border Approximation Area Comparison (MABAC) method which in this method is known as a method that can provide solutions in making a decision compared to other methods. The system was successfully built using the PHP programming language with a MySQL database. In the system built, the appropriate criteria to be used in supporting the final results of decisions that have been successfully analyzed and applied to the system are land area, income per harvest, number of workers and number of harvests each year. Based on the results of the research that has been done, the MABAC method is able to determine the ranking of the processed data based on the results of the total value of the criterion function.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".