The Use of An Accounting Information System for Monitoring Website-Based Sales Call Activities in Garment Company
Bibliographic record
Abstract
There are several ways that can be used to increase sales, one of which is using a sales call. At PT. Ricky Mumbul Daya, sales call is one of the main activities carried out to increase sales. Although the process of recording revenues and costs related to sales call activities at that company has used the system, but the process is still not optimal. This is because the data input process is only done by the cashier. Meanwhile, the monitoring process is still done manually. The purpose of this research is to create a Website-Based Accounting Information System for Sales Call Monitoring and tested whether the level of performance and effort expectations of the system has an influence on user behavioral intention. The system development method used is the System Development Life Cycle. Meanwhile, to test the level of the three variables, it will begin with collecting data with a questionnaire and then the data will be analyzed using several testing methods. The result of this research is that the information system created can complete the expected tasks and concluded that the level of performance and effort expectations of the system have an influence on the user’s behavioral intention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".