PENERAPAN METODE K-NEAREST NEIGHBOR DAN INFORMATION GAIN PADA KLASIFIKASI KINERJA SISWA
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
Education is a very important problem in the development of a country. One way to reach the level of quality of education is to predict student academic performance. The method used is still using an ineffective way because evaluation is based solely on the educator's assessment of information on the progress of student learning. Information on the progress of student learning is not enough to form indicators in evaluating student performance and helping students and educators to make improvements in learning and teaching. K-Nearest Neighbor is an effective method for classifying student performance, but K-Nearest Neighbor has problems in terms of large vector dimensions. This study aims to predict the academic performance of students using the K-Nearest Neighbor algorithm with the Information Gain feature selection method to reduce vector dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with k values (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the largest average accuracy of 74.068 while the K-Nearest Neighbor and Information Gain methods obtained the highest average accuracy of 76.553. From the results of these tests it can be concluded that Information Gain can reduce vector dimensions, so that the application of K-Nearest Neighbor and Information Gain can improve the accuracy of the classification of student performance better than using the K-Nearest Neighbor method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it