A Comparative Study of Traditional and Deep Learning Approaches for Multiclass Classification of Tourism News
Bibliographic record
Abstract
Text mining is a process of extracting knowledge contained in unstructured text, using Natural Language Processing techniques to analyze, group, and extract patterns.Text mining enables various tasks, such as text classification, information extraction, and sentiment analysis.In this study, a comparison was made between Artificial Neural Network (ANN) and Support Vector Machine (SVM) with hyperparameter tuning in classifying Indonesian tourism news.The classification was divided into 4 classes, namely natural tourism, artificial tourism, cultural tourism, and non-tourism.With the use of hyperparameter tuning on SVM, the highest F1-score was 97.73% and the average computing time was 90.35 seconds.The classification results using ANN produced an F1-score value of 97% and a computing time of 166.87 seconds.This shows that the traditional machine learning methods can match the accuracy of deep learning while requiring less computing time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".