IMPLEMENTASI METODE MOORA (MULTI OBJECTIVE OPTIMIZATION ON THE BASIC OF RATIO ANALYSIS) UNTUK REKOMENDASI PEMILIHAN TYPE SEPEDA MOTOR TERBAIK (Studi Kasus : CV. Satu Hati Perkasa)
Bibliographic record
Abstract
Motorcycle is one of the means of transportation that is loved by the community because it has a small size, fast and the price is not too expensive compared to other transportation equipment. Now many types of motorcycles complete with advantages and advantages. This of course will make it difficult for consumers to make the right choice, according to the desired criteria. To make it easier for buyers to choose the type of motorcycle that suits their needs, a decision support system is designed to recommend the appropriate motorcycle type.This system is built with accurate calculations using the MOORA method (Multi Objective Optimization on The Basic of Ratio Analysis) so that the accuracy of calculations is more guaranteed that is applied using PHP MySQL software. With this system, customers / buyers have no difficulty choosing the type of motorcycle that suits their needs and finances so that it will create a convenient and fast buying and selling process.From the 17 data, it can be seen that the results manually on the recommendation of a motorcycle type can be seen that A_3 is the highest alternative with a value of 27.336773. In other words the A_3 type motorcycle Vario 150 is the best motorcycle.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".