Penerapan Algoritma K-Means Untuk Menentukan Jumlah Produksi Kayu Bulat Berdasarkan Jenis Kayu Di Provinsi Jawa Barat
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
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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