Perbandingan Algoritma Adaboost Dan Gradient Boosting Dalam Klasifikasi Kadar Lemak Pada Keju Dengan Penerapan Rekayasa Fitur, Penyeimbangan Data, Seleksi Fitur, Dan Penanganan Data Hilang
Bibliographic record
Abstract
Cheese is one of the most popular processed dairy products.The fat content in cheese can vary depending on various factors during its manufacturing process.This study aims to classify the fat content of cheese into two categories, lower fat and higher fat, by comparing the performance of two machine learning algorithms: AdaBoost and Gradient Boosting.The dataset used was sourced from the Canadian Cheese Directory.To maximize data quality and model performance, a comprehensive series of data pre-processing stages were implemented.These stages included handling missing values, feature engineering to create new predictive variables, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and concluded with feature selection to choose the most relevant attributes.The processed data was then divided into training data and ten test data samples-five from the lower fat category and five from the higher fat category.The test results showed that the Gradient Boosting algorithm achieved a superior accuracy of 93%, outperforming AdaBoost, which obtained an accuracy of 86%.These findings indicate that after undergoing meticulous data preprocessing stages, the Gradient Boosting algorithm demonstrates superior performance in classifying cheese fat content on this dataset.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.015 | 0.018 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.714 | 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; both teacher heads agree on what is shown here.
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".