IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI POLA PEMBELIAN SEPEDA MOTOR PADA SHOWROOM CV. VIVA MAS MOTORS DENGAN METODE ALGORITMA C4.5
Bibliographic record
Abstract
Semakin kompetitif perusahaan dalam memproduksi sepeda motor membuat showroom sepeda motor membutuhkan analisa perubahan prediksi, yaitu kecenderungan tingkah laku pelanggan dalam memilih sepeda motor. Berdasarkan fakta tersebut ditemukan suatu permasalahan bagaimana memprediksi pola pembelian terhadap sepeda motor. Aplikasi data mining yang dibuat dapat memprediksi pola pembelian dengan metode Algoritma C4.5. Dengan field merk, tahun, dan harga pada tabel sepeda motor dihitung dengan gain, field dengan nilai terbesar menjadi root dari tree. Hasil tree dapat menggambarkan kecenderungan prediksi pola pembelian. Diharapkan dari rule yang ditentukan dapat memberikan informasi kepada Manager dalam melakukan analisa untuk menentukan stock dan sasaran pangsa pasar.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".