Klasifikasi Penentuan Siswa Berprestasi Menggunakan Algoritma Naïve Bayes Classifier DI PT.Yes Study Education Group Indonesia
Bibliographic record
Abstract
PT. Yes Study Education Group Indonesia is an overseas education consultancy founded by international alumni and based in Toronto, Canada, with experience helping thousands of students from various parts of the world to achieve their dream of studying abroad. However, it is not easy to study abroad because there are several factors and documents that must be prepared, such as passports, visas, and English test certificates like the Test Of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS). To achieve optimal results, good learning outcomes are required; furthermore, of course, learning outcomes are indicators of student achievement, so an algorithm is needed to determine student performance, with the aim of serving as a supporting tool in evaluating the learning process and outcomes using the naïve bayes classifier algorithm with a trial dataset of 200 student names along with their respective scores, from which 80 test records were obtained. From these calculations, the Gaussian NB model with a 50:50 split validation yielded an accuracy of 73%, scenario 2 with a 60:40 ratio yielded 75% accuracy, scenario 3 with a 70:30 ratio yielded 76.6% accuracy, scenario 4 with an 80:20 ratio yielded 82.2% accuracy, and scenario 5 with a 90:10 ratio yielded 85% accuracy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".