Analisis Sektor Potensial Dalam Pengembangan Pembangunan Perekonomian Provinsi Gorontalo (Analysis of Potential Sectors in the Development of the Economic Development of Gorontalo Province)
Bibliographic record
Abstract
Abstrak Ketimpangan yang tinggi dibarengi dengan pembangunan yang meningkat di Provinsi Gorontalo. Hal tersebut tercermin dari Produk Domestik Regional Bruto (PDRB) yang meningkat dan didukung dengan laju pertumbuhan yang dicapai Provinsi Gorontalo jauh diatas laju pertumbuhan nasional. Sehingga terdapat indikasi manfaat pembangunan tidak dirasakan oleh seluruh lapisan masyarakat Provinsi Gorontalo. Penelitian ini bertujuan untuk mengidentifikasi sektor-sektor ekonomi yang potensial dan berdaya saing untuk dapat dikembangkan pembangunannya sehingga dapat meningkatkan perekonomian serta membantu meminimalisir ketimpangan di Provinsi Gorontalo. Penelitian ini menggunakan data sekunder berupa PDRB Provinsi Gorontalo dan PDB Indonesia tahun 2010-2019, dengan alat analisis yang digunakan adalah Dynamic Location Quotinet (DLQ), Model Rasio Pertumbuhan (MRP), dan Skalogram. Hasil penelitian menunjukkan pada analisis DLQ terdapat 10 sektor ekonomi yang basis dimasa mendatang, pada analisis MRP terdapat 9 sektor ekonomi yang termasuk dalam kategori 2 artinya memiliki pertumbuhan yang menonjol pada wilayah studi dibanding dengan wilayah referensi, dan pada analisis Skalogram terdapat 4 sektor ekonomi yang dijadikan prioritas pengembangan pembangunan. Kata Kunci: Sektor Ekonomi, Basis Ekonomi, Daya Saing. Abstract High inequality has been accompained by increased development in Gorontalo Province. This is reflected in the increasing Gross Domestic Regional Product (GRDP) supported by the growth rate achieved by Gorontalo Province which is far above the national growth rate. So that there are indication that the benefits of development are not felt by all levels of society in Gorontalo Province. This study aims to identify potential and competitive economic sectors to be developed so that they can improve the economy and help minimize inequality in Gorontalo Province. This study uses secondary data in the form of GRDP of Gorontalo Province and Indonesian GDP in 2010-2019, with the analytical tools used are Dynamic Location Quotient (DLQ), Growth Ratio Model (MRP), and Skalogram. The results showed that in the DLQ analysis there were 10 economic sectors that were based in the future, in the MRP analysis there were 9 economic sectors which were include in category 2, meaning that they had prominent growth in the study area compared to the reference area, and in the analysis Skalogram there were 4 sector economic which made a development development priority. Keywords: Economic Sector, Economic Basis, Competitiveness.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".