Analisis Potensi Ekonomi untuk Meningkatkan Daya Saing Di Kawasan Perbatasan Nusa Tenggara Timur
Bibliographic record
Abstract
Abstrak Kawasan Perbatasan Darat Provinsi NTT yang meliputi Kabupaten Malaka, Kabupaten Belu, Kabupaten TTU dan Kabupaten Kupang. Berdasarkan data BPS NTT tahun 2016-2019 menunjukan pertumbuhan ekonomi pada keempat wilayah tersebut selalu mengalami kenaikan dari tahun ke tahun. Tujuan Penelitian ini untuk mengetahui potensi perekonomian pada masing-masing daerah yang dapat mendorong peningkatan daya saing di Kawasan Perbatasan Nusa Tenggara Timur. Metode yang digunakan adalah analisis LQ, Shift Share serta Tipologi Klassen. Hasil analisis menunjukan bahwa potensi ekonomi wilayah perbatasan darat Provinsi Nusa Tenggara Timur secara umum ada di sektor administrasi pemerintahan, pertahanan dan jaminan sosial wajib. Namun masing-masing wilayah tetap memiliki keunggulan yang berbeda-beda seperti Kabupaten Kupang unggul pada sektor penggalian dan pertambangan, Kabupaten TTU unggul pada sektor transportasi dan pergudangan, Kabupaten Belu unggul pada sektor perdagangan besar dan Kabupaten Malaka unggul pada sektor industri pengolahan. Masing-masing sektor unggulan harus mampu didorong sebagai leading sektor yang nantinya akan memberikan dampak pengganda bagi perekonomian wilayah. Kata Kunci : Potensi Ekonomi, Kawasan Perbatasan, Daya saing Abstract The land border area of NTT Province which includes Malacca Regency, Belu Regency, TTU Regency and Kupang Regency. Based on NTT BPS data for 2016-2019 shows economic growth that always increases from year to year. The purpose of this study is to determine the economic potential to increase competitiveness in the East Nusa Tenggara Border Area. The method used is the analysis of LQ, Shift Share and Klassen Typology. The results of the analysis show that the economic potential of the land border area of East Nusa Tenggara Province in general is in the government administration sector, defense and mandatory social security. However, each region still has different advantages such as Kupang Regency excels in the quarrying and mining sector, TTU Regency excels in the transportation and warehousing sector, Belu Regency excels in the wholesale trade sector and Malaka Regency excels in the manufacturing sector. Each sector must be able to be encouraged as a leading sector which will later have a multiplier impact on the regional economy. Keyword : Economic Potential, Border area, Competitiveness
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".