APLIKASI PELAYANAN ADMINISTRASI DESA KARANG DIMA BERBASIS WEB
Bibliographic record
Abstract
Saat ini teknologi sangat berperan penting dalam semua bidang, salah satunya dalam bidang Administrasi. Desa Karang Dima merupakan salah satu desa yang berada di Kecamatan Labuhan Badas, mempunyai luas wilayah 32,14 pada tahun 2018. Desa Karang Dima mempunyai 2.047 kepala keluarga dengan jumlah penduduk 6.951 jiwa.Penelitian ini bertujuan menyelsaikan masalah yang ada dalam Pelayanan Administrasi di Desa Karang Dima dengan merancang dan membangun Aplikasi Pelayanan Administrasi Penduduk. Aplikasi ini fokus pada pelayanan Administrasi (pembuatan surat) agar proses yang dilakukan lebih mudah dan cepat sehingga dapat memuaskan masyarakat dalam pelayanan administrasi oleh staf Desa Karang Dima. Aplikasi berbasis WEB dikembangkan menggunakan bahasa pemrograman PHP dengan menggunakan Mysql sebagai database. Metode pengembangan perangkat lunak menggunakan metode Waterfall. Pengujian perangkat lunak dilakukan melalui pengujian Black-Box. Hasil akhir dari penelitian ini adalahAplikasi Pelayanan Administrasi Desa Karang Dima Berbasis Web yang dapat membantu Admin Desa dalam memberikan pelayanan administrasi kepada warga desa secara cepat dan tepat.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.030 |
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".