Analysis of the Selection of the Best Household Ceramics Using the Complex Proportional Assessment (COPRAS) Method
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Bibliographic record
Abstract
Rapid advances in communication and information technology due to globalization have had a significant impact on a number of industries, including the industrial sector. The industry is taking great advantage of the capabilities of this technology to search, store, distribute and present information. The ceramic sector in Indonesia looks increasingly promising every year. One type of building material that functions to cover the floor and beautify its appearance is ceramic. When choosing ceramics, consumers become confused because of the availability of various brands (vendors) with different themes and quality. When deciding on product quality, a decision support system can be implemented to offer a structured evaluation that assists stakeholders in the business and consumers in assessing high-quality ceramic options. DSS The complex proportional assessment method, or COPRAS, is used in system design. In improving the accuracy and efficiency of decision making, the COPRAS approach can evaluate several options and estimate them based on their utility level when attribute values ??are expressed in intervals. Based on the findings of this research, the application of the COPRAS method in the decision-making process to determine the best household ceramics can be used in selecting the best household ceramics by collecting data on ceramic criteria and the alternative used is the type of ceramic. The weights obtained for each criterion are then normalized which are then used to determine the Ui for each alternative, so that based on the results of this research the best household ceramics are obtained, namely Redhorse type ceramics with a Ui value of 100%, Fortuna type with a Ui value of 99.27%. , Prato type with a Ui value of 98.82%, Crystal type with a Ui value of 98.71%, Mulia type with a Ui value of 88.50%, Vancouver type with a Ui value of 88.24%, Murano type with a Ui value of 84.97% and the Virginia type with a Ui value of 79.77%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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