MODEL PENGUKURAN KEKHUSYUKAN SHOLAT DENGAN PEMERIKSAAN ELEKTROFISIOLOGI KUANTITATIF
Bibliographic record
Abstract
Latar belakang: Penelitian sebelumnya didapatkan dua jenis daerah otak yang terlibat dalam sholat yakni pro tugas dan kontra tugas. Hasil ini perlu ditindaklanjuti untuk mendapatkan model pengukuran yang dapat mengkuantifikasi tingkat kekhusyukan seseorang ketika sholat dengan menggunakan pendekatan studi elektrofisiologi. Selanjutnya model kuantifikasi ini dapat digunakan untuk memberikan umpan balik tentang kualitas khusyuk sholat, dan memberikan peluang identifikasi faktor determinan kesehatan dari aktivitas sholat dengan berbasis studi elektrofisiologi. Tujuan: Mendapatkan model pengukuran kekhusyukan sholat berbasis pemeriksaan elektrofisiologi Metoda: Pengukuran amplitudo gelombang alfa diukur pada saat basal selama dua menit dan pada saat melakukan sholat. Elektroensefalogram direkam secara kontinu dengan alat EEG Natus Neurologi TM (Canada), 20 elektroda (sistem 10 – 20) dari Ag/AgCl dengan impedance 5 kilo Ohm, data didapat dengan software XLtek, filter 1 – 70 Hz, sensitifitas 7 mikro volt/mm, rentang power 0 – 196 mikro volt. Dipilih hasil rekaman yang bebas artefak untuk kemudian dianalisis. Pelaporan dengan menggunakan pendekatan event related desynchronization / event related resynchronization dari amplitudo gelombang alfa. Hasil dan Kesimpulan: Secara umum didapatkan ERD tertinggi saat takbir, kemudian menurun sepanjang sholat. ERD kontra tugas didapatkan meningkat setelah momen takbir. Terdapat perbedaan pola ERD pada dua subyek penelitian. Pola pertama, tinggi, kemudian naik dan selanjutnya turun landai. Pola kedua, saat takbir tinggi, kemudian turun curam dan kemudian melandai.
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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".