Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns
Bibliographic record
Abstract
Batik is a work of art from Indonesia that has many types and pattern. One of the batik producing areas is Surakarta, the famous pattern in this area are Sawat, Sementrante, and Satriomanah. The problem that arises is the difficulty of distinguishing the three existing pattern because they have a high level of similarity. Therefore, this research aims to solve these problems using NB and RF methods. As a feature extraction, a Gray Level Cooccurrence Matrix is used as a texture feature extraction. The research phase includes methods for dataset collection, preprocessing, feature extraction, and classification. These two methods, RF and NB, can be used as methods for batik fabric classification. The most accurate result obtained by the RF method was 97.91% accurate in dataset A, while the NB method was 96.66% accurate on the same dataset. According to the research results, it is found that the RF method outperforms the NB method in classifying the types of batik patterns.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".