Penerapan Data Mining untuk Memprediksi Jumlah Produk Terlaris Menggunakan Algoritma Naive Bayes Studi Kasus (Toko Prapti)
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
Toko Prapti is a small privately owned company that sells basic necessities,. So far, the prapti shop produces sales data every day, but the results obtained show that the prapti shop has not maximized the data so that it becomes a data accumulation. Therefore, the researcher conducted a study on product sales data by utilizing and applying data mining using the nave Bayes classifier algorithm to determine the interest in purchasing goods at the prapti shop. data. In this study, the author uses the waterfall system development method. The author implements this research using a web programming language, namely PHP, using the CodeIgniter framework with MySQl database. The system built with the nave Bayes algorithm includes product sales data, nave calculations of each attribute and reporting. This system produces 4 attributes that greatly affect the results of the classification. The attributes used in this research are the attributes are quarter 1, quarter 2, quarter 3 and quarter 4. Prediction results obtained using the nave Bayes algorithm produce information that can be used by stores to identify the best-selling products purchased by consumers so that it can help prapti shops to find and determine the target market more accurately. Sources of data taken from the previous 1 year with system accuracy using a confusion matrix resulted in 83.3% accuracy, 84.2% precision and 88.9% recall. Â Â Â Keywords : Data mining, Nave bayes Classifier, Code Igniter, Confusion Matrix
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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