Pengelompokan Provinsi di Indonesia Menggunakan Algoritma Partitioning Around Medoids (PAM) Terhadap Indikator Pembentuk Indeks Pembangunan Manusia (IPM) Tahun 2020
Bibliographic record
Abstract
The human development index is an indicator that can convert, human development and become a country's measure. Bank Indonesia the central bank/bi's decision to raise its benchmark interest rate by 25 basis points to 8.25 percent would be lower than the previous quarter of this year, he said. The algontma to be used on this research is partitioning Around medoids The algorithm partitioning around medoids is done by sifting through data Which is analyzed into the cluster-clusters that exist. The data used in mni suppresses the ipm phasing indicator, which is the biologic age. The rupiah's current exchange rate against rp9,100 per dollar in the Jakarta interbank spot market on Tuesday afternoon strengthened to rp9,310/9,329 per dollar in the Jakarta interbank spot market on Tuesday. The result of a 34 proxies grouping in Indonesia is based on indicators of human development in 2020, at two optimum clusters, In the first half of 2007, the company's total assets in the first quarter of 2007 fell to rp2.1 trillion from rp2.1 trillion in the same period last year
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".