ANALISIS KLASIFIKASI SARAN PESERTA PELATIHAN MENGGUNAKAN PENDEKATAN MACHINE LEARNING
Bibliographic record
Abstract
Saran peserta pelatihan tergolong jarang mendapatkan perhatian dan dianalisis lebih lanjut. Analisis terhadap saran peserta pelatihan dapat bermanfaat dalam mengidentifikasi faktor-faktor yang perlu diperhatikan dalam manajemen penyelenggaraan pelatihan. Text mining dan machine learning merupakan pendekatan terkini yang dapat digunakan untuk memperoleh pola tertentu pada data tidak terstruktur berupa teks. Artikel ini membangun model klasifikasi menggunakan algoritma naïve bayes berdasarkan dataset saran peserta pelatihan. Model tersebut digunakan untuk memprediksi kategori saran peserta yang dapat memudahkan penyelenggara pelatihan mengidentifikasi aspek-aspek prioritas yang perlu dievaliuasi. Hasil pemodelan memiliki akurasi 60,81% dan dapat digunakan untuk memprediksi label kategori saran peserta. Namun demikian, Kinerja model dapat ditingkatkan dengan melatih model menggunakan data baru, menggunakan model klasifikasi lain, atau modifikasi terhadap algoritma. Hasil klasifikasi saran tahun 2024 menunjukkan aspek sarana dsn prasarana, serta tata laksana pelatihan menjadi dua aspek yang harus ditindaklanjuti oleh penyelenggara pelatihan.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".