Implementasi SMOTE untuk mengatasi Imbalance Class pada Klasifikasi Car Evolution menggunakan K-NN
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Bibliographic record
Abstract
Abstrak Permasalahan ketidakseimbangan kelas akan terus ada karena data tidak dapat dipaksa untuk selalu seimbang. Ketidakseimbangan kelas memberikan dampak yang tidak baik pada hasil klasifikasi dimana kelas minoritas sering disalah klasifikasikan sebagai kelas mayoritas. Hal ini dapat menurunkan nilai accuracy hasil klasifikasi. SMOTE merupakan salah satu turunan teknik over-sampling untuk menanggulangi ketidakseimbangan kelas dengan menyeimbangkan dataset dengan meningkatkan ukuran kelas minor. SMOTE diterapkan pada klasifikasi dataset car evolution menggunakan algoritma klasifikasi KNN. Hasil klasifikasi dievaluasi akurasinya menggunakan 10fold-cross validation dengan membandingkan hasil klasifikasi yang hanya menggunakan KNN dan menggunakan KNN dan SMOTE. Hasil penelitian menunjukkan bahwa p enggunaan SMOTE mampu mengatasi imbalance class dengan menaikkan nilai akurasi rata-rata sebesar 9.97%. Semakin kecil nilai k pada klasifikasi K-NN maka semakin besar tingkat akurasinya Dari hasil uji dengan k=3, k=5 dan k=10, maka akurasi klasifikasi tertinggi K-NN dengan k=3 menggunakan SMOTE sebesar 93.11%. Kata kunci —klasifikasi,K-NN,SMOTE,Imbalance Class Abstract The problem of imbalance class will continue to exist because the data cannot be forced to always balance. Imbalance class has an unfavorable impact on the classification results where the minority class is often misclassified as the majority class. This can lower the accuracy value of the classification results. SMOTE is a derivative of the over-sampling technique to overcome class imbalances by balancing the dataset by increasing the size of the minor class. SMOTE is applied to the classification of the car evolution dataset using the KNN classification algorithm. The classification results are evaluated for their accuracy using 10fold-cross validation by comparing the classification results using only KNN and using KNN and SMOTE. The results showed that the use of SMOTE was able to overcome the imbalance class by increasing the average accuracy value by 9.97%. The smaller the k value in the K-NN classification, the greater the level of accuracy. From the test results with k = 3, k = 5 and k = 10, the highest K-NN classification accuracy with k = 3 uses SMOTE of 93.11% Keywords — classification,K-NN,SMOTE,Imbalance Class
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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