Klasifikasi Penentuan Siswa Berprestasi Menggunakan Algoritma Naïve Bayes Classifier DI PT.Yes Study Education Group Indonesia
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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