PERANCANGAN SISTEM INFORMASI PENJUALAN BERBASIS WEBSITE PADA DIVISI PERCETAKAN CV MEDIA ONE MART MAKASSAR
Bibliographic record
Abstract
This research aimed to determine: 1) the design of a web-based sales information system in the Printing Division of CV Media One Mart Makassar; 2) customer perceptions of the web-based sales information system in the Printing Division of CV Media One Mart Makassar. The issue examined in this study was the digital marketing (Facebook, Instagram, Tokopedia, and Shopee) utilized in the Printing Division of CV Media One Mart Makassar, which was not managed properly. The method used in this study was the waterfall method, consisting of: 1) requirement analysis; 2) design; 3) implementation; 4) verification; 5) maintenance. Data collection techniques employed were questionnaires and interviews. The data analysis technique used was quantitative data analysis. The result of this research indicated that: 1) The design of the web-based sales information system was created using a Content Management System (CMS) through the stages of planning, visualization, designing the interface, and integrating separate elements into a cohesive whole; 2) Customer perceptions of the web-based sales information system were categorized as "strongly agree" for utilization.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".