Penerapan Fuzzy Mamdani Dengan Particle Swarm Optimization (PSO) dan MAPE (Mean Absolute Percentage Error) Pada Penilaian Kinerja Pegawai
Bibliographic record
Abstract
Pegawai tidak hanya harus berpenampilan menarik tapi juga harus mempunyai keahlian dalam menyeselesaikan pekerjaan yang di berikan. Didalam Perguruan Tinggi memiliki pegawai yang cakap, pintar dan berwawasan luas. Perguruan tinggi yang berkompeten adalah perguruan tinggi yang memiliki pelayanan pendidikan yang berkompeten bukan hanya dalam pengajaran namun dalam bidang pelayanan administrasi mahasiswa. Dalam Proses Belajar Mengajar di Perguruan Tinggi, Pegawai mempunyai peran penting dalam kelancaran berjalannya perkuliahan. Misalnya dalam pembuatan Daftar Hadir Perluliahan dan Berita Acara Perkuliahan. Peningkatan pelayanan terhadap mahasiswa tidak terlepas dari kinerja pegawai. Penilaian terhadap kinerja pegawai melalui 3 (tiga) variabel yaitu : Variabel Keahlian, Variabel Disiplin dan Variabel Sikap. Dengan Nilai parameter : Sangat Rendah (SR), Rendah (R), Cukup (C), Baik (B) dan Sangat Baik (SB), dari paramater tersebut akan diketahui hasil Penilaian Kinerja Pegawai.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".