IMPLEMENTASI ALGORITMA C4.5 UNTUK KLASIFIKASI PRODUK LARIS SEPEDA MOTOR HONDA PADA CV CENDANA MOTOR CEPIRING
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
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.000 | 0.004 |
| 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