Pengembangan Website untuk Bali Perabot
Bibliographic record
Abstract
Bali Perabot is a trading business that sells various types of furniture, ranging from spring beds, cabinets, and other furniture types. Bali Perabot owner still wants to expand his business. In order to expand the area of marketing, this business has to open branches up to three branches. It surely requires a huge cost and difficult management. The complexity of the business process management is compounded when the store has to serve a lot of customers who do not do transactions, but just merely inquired information about the product that is being sold, especially if it adjacents to several festivals. Based on these problems, Bali Perabot requires a more effective solution in order to expand the marketing scope without having to open many branches and to serve a better information for customers. The solution obtained was to develop a marketing website based on the needs of Bali Perabot owner by using Joomla CMS and is equipped with additional extensions, named VirtueMart 3.0.6.2 and SJ Filter for VirtueMart - Joomla! Module. The developed website has eventually provided convenience to customers who want to search a furniture based on product category, price, and several important specifications needed to determine a product selection.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.115 | 0.069 |
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".