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)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it