REKAYASA PROSES BISNIS PADA E-COMMERCE B2B–B2C MENGGUNAKAN SISTEM AFILIASI
Bibliographic record
Abstract
E-commerce B2B-B2C is a kind of E-commerce with forms of interaction Bussiness to Bussiness (B2B) and Bussiness to Customer (B2C) where there is interaction between producers (company, home industries, providers of goods and services) with distributors and retailers, which followed by distributors and retailers to consumers based on electronic media that is connected to the internet. The system is engineered using Affiliate System where the activity in these systems sell products or services of another person without having to buy or have products or services. The affiliate system uses a system of revenue sharing or commission in accordance with the agreement. How affiliate system can support B2B-B2C interactions as well as the application of information and communication technology appropriate to support business processes will be explorated in this research. Results to be obtained from this research is an E-commerce system that provides ease of process to support an information system of selling based on mobile and web from providers of goods and services to retailer and from the retailer to the consumer.Keywords:E -Commerce, Business Process, B2B, B2C, Afiliate, provider of goods, retailers, consumers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".