PENGEMBANGAN APLIKASI PENCARIAN AYAT TERINTEGRASI DALAM KAJIAN TEKNIK PERENCANAAN WILAYAH DAN KOTA BERBASIS WEBSITE
Bibliographic record
Abstract
Penelitian ini mengembangkan aplikasi pencarian ayat, altaqur.com, yang terintegrasi dalam kajian Teknik Perencanaan Wilayah dan Kota (T.PWK) untuk memudahkan mahasiswa dalam menemukan ayat terkait. Metodologi penelitian meliputi pengembangan aplikasi, desain tampilan, implementasi, dan analisis persepsi pengguna. Hasil kajian menunjukkan keterkaitan ayat dengan berbagai aspek T.PWK seperti perencanaan wilayah, tata ruang, lingkungan, dan lain-lain. Aplikasi ini juga menyediakan al-Qur’an digital dan murotal. Uji coba terhadap mahasiswa T.PWK menunjukkan respon positif, dengan aplikasi ini dianggap membantu dalam pencarian ayat dan penyelesaian tugas akademik. Aplikasi ini telah diuji coba pada beberapa mahasiswa T.PWK dari hasil pencarian data melalui google form tentang persepsi mahasiswa dalam penggunaan aplikasi ini, ada 100 responden dan hampir keseluruhan menjawab sangat membantu dan memudahkan pengguna dalam mencari ayat yang dibutuhkan dan mampu menyelesaikan tugas kuliah baik tugas mata kuliah agama maupun tugas besar pembuata skripsi
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.136 | 0.056 |
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".