IMPLEMENTASI ALGORITMA C4.5 UNTUK KLASIFIKASI PRODUK LARIS SEPEDA MOTOR HONDA PADA CV CENDANA MOTOR CEPIRING
Bibliographic record
Abstract
CV Cendana Motor Cepiring merupakan salah satu perusahaan penjualan sepeda motor merek Honda di Kabupaten Kendal. Persaingan penjualan sepeda motor yang ketat menuntut perusahaan untuk menentukan strategi penjualan yang tepat untuk dapat menaikkan penjualan dan pemasaran produk agar dapat menarik minat para konsumen. Dalam mengetahui ketertarikan konsumen terhadap produk motor Honda, maka dilakukan penelitian mengenai prediksi produk laris sepeda motor Honda dari setiap wilayah kecamatan di Kabupaten Kendal. Metode penelitian yang digunakan adalah algoritma C4.5 decision tree dengan prosesnya menggunakan lima langkah pada KDD (Knowledge Discovery in Databases). Dari penelitian ini, menghasilkan klasifikasi dengan akurasi sebesar 99% yang menunjukkan bahwa algoritma C4.5 cocok digunakan untuk mengukur perkiraan penjualan sepeda motor Honda terlaris.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".