Decision Support System In Land Selection For Rubber Tree Planting Using The Moora Method
Bibliographic record
Abstract
Decision support system in the selection of land for rubber tree planting in order to minimize time and help PT. GERGAS UTAMA to determine quality land so as to increase rubber productivity. This method also has a good level of selectivity because it can determine the purpose of conflicting criteria. Where the criteria can be profitable (benefit) or unfavorable (cost). The selection of the right rubber area will certainly affect the production of rubber plants. In the process of selecting the right rubber area, of course, there are several criteria that will determine whether or not a land is suitable for producing rubber plantations such as temperature, rainfall, drainage, soil texture and so on. PT Gergas Utama has difficulty in the process of selecting this land because the selection process is still done manually which of course will take longer so that it becomes ineffective and affects the productivity of the rubber plant.Based on the description above, it is necessary to build a decision support system in the selection of land for rubber tree planting in order to minimize time and help PT Gergas Utama to determine quality land so as to increase rubber productivity. In the development of this decision support system, the MOORA (Multi-Objective Optimization by Ratio Analysis) method is used, which is a method that has a level of flexibility and ease of understanding in separating the subjective part of an evaluation process into decision weight criteria with several decision-making attributes. The program used in the development of the Decision Support System is PHP for a web-based system and MySQL as a database management system. The results ofthis program indicate that the Decision Support System for the selection of land for planting rubber trees can be utilized by PT. GERGAS UTAMA is the rubber plantation sector in selecting the best land for planting. The results of the ranking calculation from the MOORA (Multi-Objective Optimization by Ratio Analysis) method from a total of 10 rubber land planting locations with a value of 0.369, the result of the sum of the criteria weights being the highest. the best has the highest value of 0.369.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.012 | 0.003 |
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".