Penerapan Algoritma K-Means Untuk Menentukan Jumlah Produksi Kayu Bulat Berdasarkan Jenis Kayu Di Provinsi Jawa Barat
Bibliographic record
Abstract
Introduction: According to data from the Central Bureau of Statistics (BPS), log production fluctuated every quarter of 2020. Log production experienced a decline in the second quarter from a total production of 14.58 million m3 in the first quarter to 13.87 million m3. Purpose: to apply the K-Means data mining technique which is classified as a potential log production based on wood species with high and low criteria. Method: The type of research to be used is quantitative research. Discussion result: based on data on production and types of logs from 2016 to 2020, the West Java Forestry Service, log production in each district/city area in West Java is not evenly distributed for products and types of logs processed, therefore with the application of the K-Means algorithm is expected to help the production potential and types of logs in the West Java region. Therefore, the West Java Forestry Service determines the grouping of logs based on wood species into 2 clusters, namely high and low. Conclusion: The data is calculated based on 2 clusters, namely clusters with low potential (C1) and clusters with high potential (C2). The Forest Management Unit (KPH) area with the highest log production potential (C2) is the North Bandung KPH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".