Bibliographic record
Abstract
Pendekatan yang digunakan pada paper ini ialah metode Naive Bayes Classifier. Metode ini bekerja dalam himpunana data kemudian di ekstrak menjadi pengetahuan baru yang akan digunakan untuk optimasi strategi pemasaran. Algoritma Naive Bayes Classifier juga bekerja dalam tipe data numerik yang dapat memudahkan dalam proses analisa. Proses pada metode ini yaitu proses analisa pola data penjualan yang telah ada sebelumnya (Learning Phase) berdasarkan atribut-atribut yaitu jenis, waktu, ukuran yang di ujikan dan proses dari analisa. Penelitian ini menghasilkan pengetahuan baru. Selain hal tersebut dari proses analisa dengan metode Naive Bayes Classifier yaitu menghasilkan pola penjualan berdasarkan atribut-atribut yang telah di tentukan. Hasil dari proses analisa ini akan di gunakan untuk kepentingan perusahan dalam upaya optimasi strategi pemasaran. Pengetahuan baru ini juga dapat memberikan informasi penting seperti hasil prediksi minat pembeli yang dapat digunakan dalam efektivitas dan efisiensi pemasaran dan peningkatan penjualan.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".