PENERAPAN DATA MINING KORELASI UMUR, PANGKAT DAN PENDIDIKAN TERHADAP JABATAN PADA POLRES BINJAI MENGGUNAKAN METODE ALGORITMA APRIORI
Bibliographic record
Abstract
Mengenai penyediaan informasi jabatan, Personel harus memenuhi persyaratan untuk menduduki jabatan tersebut, Sesuai dengan Peraturan Kepolisian (PERKAP) tentang jabatan personel. Contohnya ialah seorang personel harus mencapai Ajun Komisaris Besar Polisi (AKBP) sehingga ia bisa menduduki jabatan sebagai Kepala Kepolisian Resort (KAPOLRES). Kegiatan menghubungkan data personel dengan menggunakan Algoritma Apriori dapat di lakukan dengan aturan aturan tertentu sehingga dapat menghasilkan hubungan antara pangkat dan umur dan mampu membantu para personel agar mengetahui informasi jenjang karir nya kedepan melalui sistem informasi kepolisian. Dari data personel yang mencakup umur, pangkat dan keahlian yang di korelasikan dengan jabatan menggunakan metode Algoritma Apriori terdapat nilai minimum Support 30% dan confidence nya 50% sehingga mendapatkan Best Rule nya adalah 15%. Dari hasil yang didapat yaitu jika usia Usia Polisi U2( 32 - 45 ), Pangkat Polisi APD (AIPDA) dan Pendidikan SMAmaka Jabatan yang diterima Polisi lebih cenderung kepada SSB (SATSABHARA) Hasil pengetahuan informasi baru untuk membantu para personel agar mengetahui informasi jenjang karir polisi kedepan dengan pangkat yang ia duduki sekarang berdasarkan support dan confidence sesuai pangkat dan umur
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".