Analisis Persebaran UMKM Kota Malang Menggunakan Cluster K-means
Bibliographic record
Abstract
UMKM (Usaha Mikro Kecil dan Menengah) merupakan usaha produktif yang telah terbukti memberikan lapangan kerja dan menjadi penggerak roda perekonomian di Indonesia. Kota Malang dianggap memiliki potensi besar di sektor UMKM. Di sisi lain, UMKM juga menghadapi berbagai masalah, seperti keterbatasan modal kerja, kurangnya pembinaan terhadap sumber daya manusia, dan lain sebagainya. Pengelompokan UMKM di Kota Malang dapat memudahkan pemerintah terkait dalam hal memilih peminjaman modal, menentukan potensi usaha dan menetapkan strategi pemasaran. Pada penelitian ini, pengelompokan UMKM di Kota Malang dilakukan dengan algoritma K-means cluster analysis. Hasil yang diperoleh adalah terbentuk 3 cluster, di mana algoritma K-means mengelompokkan kecamatan Blimbing ke cluster 1, kecamatan Klojen ke cluster 2, kecamatan Sukun ke cluster 3, Kecamatan Kedung Kandang ke cluster 3, dan Kecamatan Lowokwaru ke cluster 3.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".