PEMANFAATAN DATAMINING PADA PENGELOMPOKAN PROVINSI TERHADAP PENCEMARAN LINGKUNGAN HIDUP
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
This research aims to provide input for the government so that it can immediately tackle water pollution given the many adverse effects that lurk in various aspects of life. The method used in this study researchers used the method of K-means clustering datamining algorithm. The data used in this study are the number of villages according to the type of environmental pollution in 2018 which consists of 34 provinces in Indonesia obtained through the official website of the Directorate of Statistics Indonesia. The variable used is water pollution. The variable used is water pollution. Data is grouped into 2 clusters, namely provinces that have high levels of water pollution (C1) and provinces that have low levels of water pollution (C2). K-Means Clustering algorithm in this study produces 2 iterations, so the final result is: high water pollution (C1) in the provinces of North Sumatra, West Java, Central Java, East Java, for low level water pollution (C2) is in provinces of Aceh, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Kep.Bangka Belitung, Kep.Riau, DKI Jakarta, DI Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, Papua.Keywords:Datamining, Clustering, K-means , Water pollution
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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