Penerapan Data Mining Korelasi Penjualan Spare Part Mobil Menggunakan Metode Algoritma Apriori (Studi Kasus: CV. Citra Kencana Mobil)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract

 
 
 
 By utilizing customer data that has been stored in the database, the management can find out how the current sales system is less efficient, therefore a system is needed to process information data more quickly and accurately in increasing sales of car spare parts using the Data Mining application. The Apriori Algorithm method that works by searching for and finding associated patterns among the products being marketed, so that later it can help companies improve the associated items. And with the sales transaction data, the company can know better how they should increase the spare part stock in the company. From the results of testing the sale of car spare parts with 589 data, it was found that 81 rules were formed and the highest Best Rule was obtained and a minimum support value of 1% and a confidence value of 11% If the type of car is Avanza / Xenia and the brand is Toyota, the spare parts used are filters. Air. With supporting spare parts in the database of 1% and certainty of spare parts of 11.
 
 
 
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it