Perancangan Arsitektur Data Pada Fungsi Analisis Kondisi Umum Daerah Kabupaten Bintan Menggunakan Framework Togaf Adm
Bibliographic record
Abstract
Badan Perencanaan, Pembangunan, Penelitian, dan Pengembangan (BAPELITBANG) Kabupaten Bintan, salah satu dari tugasnya adalah mempersiapkan RPJMD (Rencana Pembangunan Jangka Menengah) yang dikenal sebagai RPJMD teknokratik. Saat mempersiapkan RPJMD dibutuhkan data dan informasi kondisi umum daerah, yang digunakan sebagai dasar untuk mengeksplorasi, memproyeksikan, memprediksi kondisi lima tahun ke depan. Ada beberapa tahapan sebelum melakukan proses analisis data dan analitik, salah satunya adalah data preprocessing. Salah satu kesulitan yang dihadapi oleh BAPELITBANG adalah tahapan data preprocessing, karena data tersebar di Organisasi Perangkat Daerah (OPD) dengan berbagai macam format. Solusi untuk menyelesaikan permasalahan ini dimulai dari merancang arsitektur data. Framework yang digunakan untuk merancang arsitektur adalah The Open Group Arcitecture Framework (TOGAF) Architecture Development Method (ADM) Fase C, yaitu Arsitektur Sistem Informasi. Hasil dari Perancangan adalah cetak biru (Blueprint) yang dapat digunakan sebagai panduan dalam mengimplementasikan dan sebagai basis ke tahapan selanjutnya, yaitu data cleansing, data filter / enrich, classification, data analytics, modelling prediction, data delivery, dan data visualization.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".