Perancangan Sistem Informasi Pemilihan Duta Siswa Peduli Bencana Kota Tarakan Berbasis Scrum
Bibliographic record
Abstract
Penelitian ini bertujuan untuk merancang sistem informasi pemilihan Duta Siswa Peduli Bencana. Pemilihan duta ini menghadapi berbagai tantangan, seperti pengelolaan data peserta, proses seleksi, dan evaluasi yang terstruktur. Dalam penelitian ini, metodologi Scrum diterapkan untuk memastikan proses perancangan sistem dapat dilakukan secara iteratif dan adaptif. Data kebutuhan sistem dikumpulkan melalui observasi dan wawancara dengan pihak BPBD serta dilakukan analisis literatur terkait penelitian yang dilakukan. Hasil penelitian menunjukkan bahwa Scrum berhasil memfasilitasi perancangan sistem yang mencakup fitur pendaftaran, pengelolaan berkas, pengumuman hasil seleksi, serta partisipasi pemilih secara online. Dengan pendekatan ini, sistem dapat mengelola proses pemilihan secara efisien dan transparan. Penggunaan Scrum juga memungkinkan tim pengembang melakukan penyesuaian sesuai kebutuhan pengguna selama proses sprint. Kesimpulan dari penelitian ini menegaskan bahwa penerapan Scrum efektif dalam pengembangan sistem ini. Dengan demikian, sistem yang dihasilkan dapat memberikan kontribusi signifikan dalam mendukung program Duta Siswa Peduli Bencana.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.031 |
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".