PENERAPAN APLIKASI E-COMMERCE BERBASIS BUSINESS TO CONSUMERS PADA TOKO CANADA MENGGUNAKAN FRAMEWORK CODEIGNITER
Bibliographic record
Abstract
Toko Canada is a company that sells various kinds of fashion needs for men and women. The system used in sales activities is still manual, which is still using notebooks in processing the data. So in carrying out sales activities is still difficult and takes a lot of time. There needs to be a sales system to facilitate sales activities. Business To Consumers is one of the e-commerce models, which is a business process where sales deal directly with buyers. Buying and selling directly through word of mouth is still done in the era of technological developments like now. However, the buying and selling process was deemed less efficient and the scope of sales was felt to be less extensive. So that these problems are not sustainable, an e-commerce business model website will be built for customers. In building an e-commerce system, the author uses the prototype method by designing the system using the Unified Modeling Language (UML). Then MySQL supported to manage existing databases. The final result of this research is an e-commerce system at Toko Canada that can help and facilitate various sales activities. The conclusion of this research is that the sales application that has been made makes it easy for owners to get accurate reports and can also help employees to record reports.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".