Implementation of Data Analytics for the Accuracy of Service Time Prediction Models
Bibliographic record
Abstract
The performance of repair services is very important to determine the achievement of consumer confidence. The strategy that needs to be made is to pay attention to the timeliness of repairs so that no one is harmed between consumers and service providers. Tracing information on data from repair services is one effective way to determine the accuracy of computer repair time. The collection of information used comes from the Istidata Indopacific Solution Center (IISC) repair service dataset, consisting of a collection of data on the completion time of product unit repairs that are achieved and not achieved. Repair completion time is the time in accordance with the agreement between the repair service party and the consumer. Data processing is carried out by processing analytical data by utilizing the Weka Tools software with the application of classification with the J48 decision tree method which is the development of the C4.5 algorithm. The effectiveness of this method was tested using 10-fold cross validation, where from the results of the confusion matrix measurement an accuracy of 99.5% was obtained. The result states that the J48 decision tree method is effective and can be used to predict the accuracy of computer repair time.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".