PENINGKATAN KEMISKINAN PERKOTAAN, SUBURBAN, DAN PERDESAAN PADA AWAL PANDEMI COVID-19 DI KABUPATEN KENDAL
Bibliographic record
Abstract
In 2020, during the early stages of the Covid-19 pandemic, Kendal Regency witnessed an increase in poverty rates as both national and global levels. Urban areas in Kendal Regency experienced a higher surge of 4.42 percent in low-income families compared to rural areas, which saw only a 0.43 percent increase. Suburbanization played a significant role due to Kendal Regency's proximity to Semarang City, the capital of Central Java Province. Interestingly, poverty-related issues were more prevalent in suburban areas. Consequently, a study was conducted to analyze poverty in urban, suburban, and rural areas in Kendal Regency. The research aimed to achieve two objectives: (1) establish spatial zoning in Kendal Regency based on the three categories, and (2) analyze the increase in poverty during the early period of the pandemic in each category. Spatial zoning was performed using the K-Means Clustering technique, while descriptive quantitative techniques and spatial analysis with the Moran Index and Local Indicators of Spatial Autocorrelation (LISA) were used for analysis. The results indicated that the Covid-19 pandemic affected the composition of poor households differently across urban, suburban, and rural areas. Additionally, the analysis revealed that poverty tended to cluster in suburban areas of Kendal Regency.Keywords: poverty, urban, suburban, rural, Covid-19AbstrakPada tahap awal pandemi Covid-19 di tahun 2020, Kabupaten Kendal mengalami peningkatan angka kemiskinan sebagaimana terjadi di lingkup nasional maupun global. Daerah perkotaan di Kabupaten Kendal mengalami lonjakan yang lebih tinggi sebesar 4,42 persen pada keluarga miskin dibandingkan dengan daerah perdesaan yang hanya mengalami peningkatan sebesar 0,43 persen. Suburbanisasi memainkan peran penting karena kedekatan Kabupaten Kendal dengan Kota Semarang, ibu kota Provinsi Jawa Tengah. Menariknya, isu-isu terkait kemiskinan lebih banyak terjadi di daerah suburban. Oleh karena itu, dilakukan penelitian untuk menganalisis kemiskinan di perkotaan, suburban, dan perdesaan di Kabupaten Kendal. Penelitian ini bertujuan untuk mencapai dua tujuan: (1) menetapkan zonasi tata ruang di Kabupaten Kendal berdasarkan ketiga kategori tersebut, dan (2) menganalisis peningkatan kemiskinan pada periode awal pandemi di setiap kategori. Zonasi spasial dilakukan dengan menggunakan teknik K-Means Clustering, sedangkan teknik deskriptif kuantitatif dan analisis spasial dengan Moran Index dan Local Indicators of Spatial Autocorrelation (LISA) digunakan untuk analisis. Hasilnya menunjukkan bahwa pandemi Covid-19 memengaruhi komposisi rumah tangga miskin secara berbeda di perkotaan, suburban, dan perdesaan. Selain itu, analisis mengungkapkan bahwa kemiskinan cenderung mengelompok di daerah suburban Kabupaten Kendal.Kata kunci: kemiskinan, perkotaan, suburban, perdesaan, Covid-19
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".