PENGCLUSTERAN JENIS USAHA UKM BERDASARKAN PROGRAM BANTUAN DI KOTA BINJAI MENGGUNAKAN ALGORITMA K-MEANS
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengelompokkan jenis usaha UKM berdasarkan program bantuan yang diterima di Kota Binjai menggunakan algoritma K-Means. Permasalahan distribusi bantuan yang belum tepat sasaran dan tidak merata mendorong perlunya pemetaan berbasis data. Metode yang digunakan adalah algoritma K-Means yang diimplementasikan dengan MATLAB R2014a, dengan variabel domisili kecamatan, jenis usaha, dan jenis bantuan. Pengujian dilakukan dengan jumlah cluster 3 hingga 6 untuk menentukan model paling optimal. Hasil terbaik diperoleh pada model 6 cluster dengan nilai cluster variance terendah sebesar 0,7759, menunjukkan distribusi data yang paling kompak. Masing-masing cluster memiliki karakteristik berbeda yang merepresentasikan kebutuhan spesifik UKM di tiap wilayah. Dengan pendekatan ini, strategi penyaluran bantuan dapat dilakukan secara lebih objektif, efisien, dan sesuai kebutuhan. Penelitian ini diharapkan menjadi acuan dalam pengambilan keputusan berbasis data oleh pemerintah daerah dalam mendukung pemerataan ekonomi di Kota Binjai.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".