ANALISIS KESELARASAN PENGGUNAAN LAHAN TERHADAP DAYATAMPUNG PENDUDUK DAN KONSENTRASI NO2 DI KECAMATANRAJABASA, KOTA BANDAR LAMPUNG
Bibliographic record
Abstract
Kecamatan Rajabasa di Kota Bandar Lampung, dengan luas wilayah 12,93 km2 dan populasi 55.958 jiwa pada tahun 2023, menghadapi tekanan signifikan akibat pertumbuhan penduduk yang tinggi. Kepadatan populasi yang mencapai 4.365 jiwa per kilometer persegi telah memicu perubahan pola penggunaan lahan, yang berdampak pada daya tampung wilayah serta kondisi lingkungan, terutama kualitas udara. Penelitian ini bertujuan untuk menganalisis keselarasan antara penggunaan lahan, daya tampung penduduk, dan konsentrasi NO2 di Kecamatan Rajabasa. Penggunaan lahan diklasifikasikan dengan tiga metode: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), dan Random Forest, menggunakan citra satelit Sentinel-2. Daya tampung penduduk dianalisis berdasarkan PERMEN PU Nomor 41/PRT/M/2007 tentang Pedoman Kriteria Teknis Kawasan Budidaya, yang mempertimbangkan faktor kelayakan permukiman seperti kelerengan, buffer sungai, rawan banjir, buffer rel, dan jenis tanah dengan metode skoring. Konsentrasi NO2 diukur menggunakan data dari satelit Sentinel-5P melalui Google Earth Engine untuk periode 1 hingga 30 Oktober 2023. Hasilnya menunjukkan bahwa klasifikasi penggunaan lahan menghasilkan variasi luas lahan yang signifikan, dengan metode SVM menghasilkan luas permukiman tertinggi. Analisis daya tampung menunjukkan bahwa wilayah ini mampu menampung peningkatan jumlah penduduk hingga tahun 2033 tanpa melebihi kapasitas yang direncanakan. Konsentrasi NO2 berkisar antara 4,7 × 10−6 hingga 8,4 × 10−6 mol/m2 atau setara dengan 0,18 hingga 0,20 μg/m3, yang berada jauh di bawah ambang batas ISPU, menunjukkan bahwa kualitas udara di Kecamatan Rajabasa masih sangat baik. Kata Kunci: Penggunaan Lahan, Daya Tampung Penduduk, NO2, Sentinel-2, Sentinel-5P. Rajabasa Sub-district in Bandar Lampung City, where an area of 12.93 km2 and a population of 55,958 in 2023, faces significant pressure due to rapid population growth. The population density, reaching 4,365 people per square kilometer, has triggered changes in land use, affecting the area's carrying capacity and environmental conditions, particularly air quality. This study aims to analyze the Suitability between land use, carrying capacity, and NO2 concentration in Rajabasa. Land use was classified using three algorithms: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest, utilizing Sentinel-2 satellite imagery. Carrying capacity was assessed based on Ministry of Public Works Regulation Number 41/PRT/M/2007, which provides guidelines for the technical criteria of residential areas, considering factors like slope, river buffers, flood-prone areas, railway buffers, and soil type, using a scoring method. NO2 concentrations was measured using data from Sentinel-5P via Google Earth Engine for the period from October 1 to 30, 2023. The results show that land use classification produced significant variations in land area, with the SVM method showing the highest urban area. The carrying capacity analysis indicates that the area can accommodate population growth until 2033 without exceeding planned capacity. NO2 concentrations ranged from 4.7 × 10−6 to 8.4 × 10−6 mol/m2, equivalent to 0.18 to 0.20 μg/m3, well under the ISPU threshold, indicating that air quality in Rajabasa remains excellent. Keywords: Land Uses, Carrying Capacity, NO2, Sentinel-2, Sentinels-5P,
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".