Classification Analysis of Student Ability in Learning Using Clustering Method at SMA Tunas Pelita
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to classify the assessment of the learning process at SMA Tunas Pelita Binjai T.A. 2018/2019 based on the average grade X, additional subjects applied technology, and student absenteeism classified using Matlab.The data is processed based on learning grouping as much as 2 clusters with different centroids, namely for cluster 1 the average value of even and odd semesters for class X (85.0), additional subjects of applied technology (86.3) and student attendance (2.4) and cluster 2 the average grades of odd-even semesters for class X (68.2), additional subjects of applied technology (70.3) and student attendance (2.4). In the final result, it can be seen that the grouping of learning at SMA Tunas Pelita Binjai with 100 data can be divided into 2 groups, namely group 1 with 62 data with an average value of odd and even semesters and high additional applied technology and student absenteeism. low grades are classified as students with good grades and group 2 as many as 38 data with an average value of odd, even semesters and low values of applied technology and high student absenteeism belonging to students who have poor grades.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it