Penilaian Tingkat Resiliensi Kota Bogor untuk Mewujudkan Resilient City: Studi Kasus Lingkup Kecamatan di Kota Bogor
Bibliographic record
Abstract
Abstract. Resilient city is a resilient city that can absorb and recover from pressure or shocks, one of which is a natural disaster. Bogor City is not spared from the threat of disaster, although according to the initial assessment of the Ministry of ATR/BPN shows that the level of resilience of Bogor City is classified as "good", but the level of resilience is still macro and it is not known exactly whether the level of resilience applies uniformly throughout the Bogor City area or there are several sub-districts that actually indicate a low level of resilience. This study was conducted to determine the diversity of resilience levels per sub-district in Bogor City. Therefore, the approach method used is an empirical and quantitative approach method with primary data collection methods (observation and questionnaires), as well as secondary data collection methods (institutional documents, literature studies, and internet studies). The analysis method used is descriptive statistical analysis to measure the level of resilience per sub-district in Bogor City. Based on the results of the analysis, it can be seen that the level of resilience within the scope of sub-districts in Bogor City is included in 2 (two) categories, namely "Medium High Resilient" in West Bogor District (3.62), Bogor Tanah Sareal District (3.52), North Bogor District (3.45), and East Bogor District (3.44), as well as "Medium Resilient" in Central Bogor District (3.34) and South Bogor District (3.24). Abstrak. Resilient city adalah kota tangguh yang dapat menyerap dan pulih dari tekanan atau guncangan salah satunya bencana alam. Kota Bogor tidak luput dari ancaman bencana tersebut, meskipun menurut assessment awal Kementerian ATR/BPN menunjukkan bahwa tingkat resiliensi Kota Bogor tergolong “baik”, namun tingkat resiliensi tersebut masih bersifat makro dan tidak diketahui secara pasti tingkat ketahanan tersebut berlaku secara seragam di seluruh wilayah Kota Bogor atau terdapat beberapa kecamatan yang justru mengindikasikan tingkat resiliensi rendah. Penelitian ini, dilakukan untuk mengetahui keberagaman tingkat resiliensi per kecamatan di Kota Bogor. Maka, metode pendekatan yang digunakan adalah metode pendekatan empiris dan kuantitatif dengan metode pengumpulan data primer (observasi dan kuesioner), serta metode pengumpulan data sekunder (dokumen instansional, studi kepustakaan, dan studi keinternetaan). Metode analisis yang digunakan adalah analisis statistik deskriptif untuk mengukur tingkat resiliensi per kecamatan di Kota Bogor. Berdasarkan hasil analisis, dapat diketahui tingkat resiliensi dalam lingkup kecamatan di Kota Bogor termasuk ke dalam 2 (dua) kategori yaitu “Medium High Resilient” pada Kecamatan Bogor Barat (3,62), Kecamatan Bogor Tanah Sareal (3,52), Kecamatan Bogor Utara (3,45), dan Kecamatan Bogor Timur (3,44), serta “Medium Resilient” pada Kecamatan Bogor Tengah (3,34) dan Kecamatan Bogor Selatan (3,24).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".