Perkiraan Migrasi Perkelompok Umur Provinsi Banten Tahun 2020
Bibliographic record
Abstract
Abstract. In this research, we will calculate the estimated net migration figures for male and female population age groups in the year 2020 for the Province of Banten based on data from the 2020 Population Census. The data analysis technique used is by comparing the Population Growth Rate (PGR) of Indonesia with that of the Province of Banten. By obtaining the difference between the PGR of Indonesia and the PGR of the Province of Banten, we can determine the estimated population change due to migration, or the number of net migrants in the Province of Banten in the year 2020. According to the results of the 2020 Population Census (SP 2020), the PGR of Indonesia is 1.25% per year, while the PGR of the Province of Banten is 1.10% per year, resulting in a difference of -0.15% or -0.015. From the calculations, the estimated number of net migrants in the Province of Banten is -17,875 people. To break down the data into age groups, we use the Age Specific Net Migration Rate (ASNMR) of the Province of Banten based on data from the 2015 Inter-Census Population Survey adjusted to produce negative ASNMR values for the year 2020. This results in the Net Migration Index for males and females, which are -127 and -281 times, respectively. Abstrak. Dalam penelitian ini akan menghitung perkiraan angka migrasi neto perkelompok umur penduduk laki-laki dan penduduk perempuan tahun 2020 Provinsi Banten berdasarkan data hasil Sensus Penduduk tahun 2020. Teknik analisis data yang digunakan adalah dengan membandingkan Laju Pertumbuhan Penduduk (LPP) Indonesia dengan LPP Provinsi Banten. Dengan didapatkannya nilai selisih antara LPP Indonesia dengan LPP Provinsi Banten, dapat diketahui perkiraan perubahan penduduk karena migrasi, atau dapat diketahui jumlah migran neto penduduk Provinsi Banten tahun 2020. Hasil Sensus Penduduk (SP) 2020, LPP Indonesia adalah 1,25 % pertahun sedangkan LPP Provinsi Banten adalah 1,10% pertahun, sehingga ada selisih sebesar -0,15% atau sebesar -0,015. Dari hasil perhitungan didapat perkiraan jumlah migrasi neto penduduk Provinsi Banten adalah -17.875 orang. Untuk memecah menjadi kelompok umur digunakan Age Specific Net Migration Rate (ASNMR) Provinsi Banten berdasarkan data Survei Penduduk Antar Sensus (SUPAS) 2015 yang disesuaikan, sehingga menghasilkan perkiraan nilai ASNMR tahun 2020 bernilai negatif, dan menghasilkan Indeks Migrasi Neto untuk penduduk laki-laki dan perempuan masing-masing adalah -127 dan -281 kali.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".