Pemetaan Kebakaran Hutan Dan Lahan Kabupaten Tanah Bumbu Kalimantan Selatan Menggunakan Aplikasi Sistem Informasi Geografis
Bibliographic record
Abstract
ABSTRACT Forest and land fires are one of the main factors in forest destruction, so as in Tanah Bumbu District, South Kalimantan Province. It always occur every year especially during the dry season. This study aims to obtain the distribution of the risk area for forest and land fires in Tanah Bumbu District and to map the areas based on their level of forest and land fires vulnerability using geographic information system. Geospacial modelling to map the vulnerability of forest and land fires uses six parameters, those are hotspot distribution, land use and land cover, topography, hydrology (river accesibility), rain fall, and demographic and settlement accesibility data. The analytical method used are overlay, skoring, and descriptive method. The results of this study indicate that the vulnerability of forest and land fire in Tanah Bumbu district consists of five classes, those are secure zone of 166.570, 21 ha (32,87%), not vulnerable zone of 159.477,86 ha (31,47%), a bit vulnerable zone of 97.297,33 ha (19,2%), vulnerable zone of 59.862,88 ha (11,81%), and a verry vulnerable zone of 23.487,68 ha (4,63%). Land cover with high risk of forest and land fire are shrubs, dry land agriculture, secondary forest, plantations, and plantation forests. While Kecamatan Satui and Kecamatan Kusan Hulu area the area that very vurnerable.Keywords: forest and land fires, vurnerability, geospatial modelling, geographic information system ABSTRAKKebakaran hutan dan lahan merupakan salah satu faktor utama dalam kerusakan hutan, begitu pula di Kabupaten Tanah Bumbu Provinsi Kalimantan Selatan. Setiap tahun kebakaran hutan dan lahan selalu terjadi, terutama pada musim kemarau. Penelitian ini bertujuan untuk memperoleh sebaran daerah resiko kebakaran hutan dan lahan di Kabupaten Tanah Bumbu serta memetakan daerah rawan kebakaran hutan dan lahan berdasarkan tingkatan kerawanannya menggunakan sistem informasi geografis. Pemodelan geospasial untuk membuat peta kerawanan menggunakan enam parameter yaitu sebaran hotspot, penggunaan lahan dan tutupan lahan, topografi, hidrologi khususnya aksesibilitas terhadap sungai, curah hujan, serta data demografi dan aksesibilitas permukiman. Metode analisis yang digunakan adalah metode tumpang susun (overlay), pembobotan, dan deskriptif.Hasil penelitian ini menunjukkan bahwa kerawanan kebakaran hutan di kabupaten Tanah Bumbu terdiri dari lima kelas yaitu daerah aman seluas 166.570, 21 hektar (32,87%), daerah tidak rawan seluas 159.477,86 hektar (31,47%), daerah agak rawan seluas 97.297,33 hektar (19,2%), daerah rawan seluas 59.862,88 hektar (11,81%), dan daerah sangat rawan seluas 23.487,68 hektar (4,63%). Tutupan lahan yang paling sering terjadi kebakaran hutan dan lahan adalah belukar, pertanian lahan kering, hutan sekunder, perkebunan, dan hutan tanaman. Daerah paling rawan terhadap kebakaran hutan dan lahan adalah Kecamatan Satui dan Kecamatan Kusan Hulu.Kata Kunci: kebakaran hutan dan lahan, kerawanan, pemodelan geospasial, sistem informasi geografis
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".