PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN PEMBERANGKATAN HAJI DENGAN METODE DECISION TREE PADA KANTOR KEMENTERIAN AGAMA KOTA BINJAI
Bibliographic record
Abstract
Pengetahuan tentang tata cara dan aturan tentang pelaksanaan haji bagi para calon jamaah haji merupakan hal terpenting sehingga semua proses yang wajib dalam pelaksanaan haji dilakukan oleh para jamaah haji. Pengolahan data calon jamaah haji pada Kantor Kementerian Agama Kota Binjai adalah suatu tugas dari sub bagian penyelenggaraan haji dan umroh. Untuk mengolah data jamaah tersebut masih banyak kekurangan karena proses masih dilakukan secara manual. Berdasarkan permasalahan diatas,maka penulis ingin melakukan penelitian membangun Perancangan Sistem Pendukung Keputusan Pemberangkatan Haji dengan Metode Decision Tree pada Kantor Kementerian Agama Kota Binjai. Dengan penerapan aplikasi ini diharapkan dapat memudahkan informasi terhadap pengelola haji khususnya kantor Kementerian Agama Kota Binjai .Sistem ini dapat menyajikan informasi yang tepat dan akurat untuk kebutuhan dalam menyajikan informasi dan data penyelenggaraan calon jamaah haji sehingga memudahkan pegawai dalam menginput data.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".