Implementation of a Rule-Based Decision Support System in Determining the Level of Customer Satisfaction with Services
Bibliographic record
Abstract
Sarmi Car Wash is a car and motorbike washing service located at Jalan Sultan Hasanuddin No. 52, offering pick-up and drop-off services within a specified range. To enhance employee performance and customer service, the business seeks to implement a decision support system (DSS) to monitor employee performance and assess customer satisfaction effectively. This research aims to design a web-based DSS application that utilizes rule-based assessments of employee performance, integrating data such as employee names and tenure as references for developing the system. The study employed data collection methods including observation, interviews, documentation, and literature review. The system was tested using the black-box method, confirming that it functions as intended. Additionally, based on a user assessment questionnaire, the application achieved an average score of 80.4%, indicating its suitability and effectiveness for implementation. The developed system provides an efficient solution for improving service quality and employee performance monitoring at Sarmi Car Wash.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 0.001 |
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".