Penerapan Algoritma C4.5 dan Random Forest pada Tingkat Penjualan Serum Somethinc di Shopee
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
Online buying and selling activities in Indonesia are increasing. Shopee is an online buying and selling platform with the highest visits in Indonesia in the fourth quarter of 2022. The category with the highest transactions at Shopee is beauty products. Somethinc is a very successful local beauty product at Shopee which have highest sales of serum products in Indonesia. This study applies the classification method C4.5 and Random Forest to see important variables in the sales of Somethinc serum at Shopee. The variables used come from store profiles which include: number of followers, number of products, chat performance, store rating, and length of stay. Continuous sales data is discretized using k-means into ordinal data with low, medium, and high levels. There is an imbalance of data in the sales class so that the SMOTE technique is used. The C4.5 algorithm produces a decision tree that contains rules for classification. Random Forest generates the order of variable importance based on the Mean Decrease Gini (MDG) values in descending order, which are as follows: number of followers, number of products, message performance, joining time, and store rating.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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