Penerapan K-Means Dalam Mengelompokkan Nilai Tambah Industri Besar/Sedang Menurut Kabupaten/Kota
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Each region must have industries both primary industries, secondary industries, manufacturing industries, construction industries, service industries and the quarter industry. These industries must produce an output that will be used or consumed by consumers or the public. More and more industries in a region indicates that the region has a lot of market demand from the community and the more industries, the income in the industry of a region increases. In this study the data was taken from a government website namely BPS (Statistics Indonesia) - www.bps.go.id which is a website that presents various statistical data from each region. There are 2 clusters in this study, namely high level clusters (C1) and low level clusters (C2). This study stopped at the 2nd iteration and there were centroid data generated namely high level centroid (78177, 56543, 42610, 155596) and low level centroid namely: ((3513.3), (3448.8), (2390.9) ), (4568)). From the calculation process that has been carried out there are 2 high-level districts / cities namely (Deli Serdang and Medan) and 31 other low-level districts / cities. It is hoped that this research can be input to the government in each region to inform the output data generated from industry in each region, and then the data can be used whenever needed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it