ANALISIS POLA PERMUKIMAN BERDASARKAN TOPOGRAFI DI KOTA TERNATE
Bibliographic record
Abstract
Pengetahuan tentang pola distribusi titik ruang akan memudahkan untuk mencari solusi penyebab pola titik dalam ruang tersebut terbentuk. Melalui cara tersebut maka, perbandingan antara pola persebaran dapat dilakukan dengan baik. Tujuan dari penelitian ini yaitu untuk mengidentifikasi dan menganalisis pola permukiman berdasarkan topografi atau kemiringan lereng yang terdapat di Kota Ternate. Analisis data menggunakan analisis tetangga terdekat untuk mengidentifikasi dan menganalisis pola permukiman berdasarkan sebaran permukiman pada topografi yang mempengaruhi pola permukiman di Pulau Ternate. Topografi atau kemiringan lereng Pulau Ternate dihasilkan dari analisis data Digital Elevation Model Shuttle Radar Topography Mission. Ternate bagian Barat atau sebagian Kecamatan Pulau Ternate dominan dalam kelas agak curam sampai sangat curam, sehingga di daerah tersebut jarang ada permukiman. Sedangkan Ternate bagian Selatan, Timur dan Utara yang didominasi kemiringan lereng kelas datar hingga landai membuat daerah tersebut padat permukiman, khususnya Ternate bagian Timur. Hasil analisis tetangga terdekat keempat Kecamatan di Kota Ternate menghasilkan pola permukiman yang mengelompok. Pola permukiman mengelompok tersebut didominasi di daerah yang memiliki kemiringan lereng datar hingga landai, dengan taraf persentasi 0-8% kelas datar dan 8-15% kelas landai. Berdasarkan peta pola permukiman, daerah yang padat permukiman terletak di Kecamatan Ternate Utara, Ternate Tengah dan Ternate Selatan. Ketiga kecamatan tersebut merupakan area yang termasuk kategori datar yang cocok untuk permukiman dan memiliki area datar yang lebih luas dibandingkan dengan Kecamatan Pulau Ternate. ABSTRACT Knowledge of the pattern of the distribution of points in space will make it easier to find solutions to the cause of the pattern of points in the space formed. Through this method, comparisons between distribution patterns can be carried out properly. The purpose of this study is to identify and analyze settlement patterns based on topography or slopes in Ternate City. Data analysis uses nearest neighbor analysis to identify and analyze settlement patterns based on the distribution of settlements on topography that affect settlement patterns on Ternate Island. The topography or slope of Ternate Island is generated from data analysis of the Digital Elevation Model Shuttle Radar Topography Mission. The western part of Ternate or part of the Ternate Island District is dominant in a rather steep to very steep class so in that area there are rarely settlements. Meanwhile, the southern, eastern, and northern parts of Ternate, which are dominated by flat to gentle slopes, make the area densely populated, especially in the eastern part of Ternate. The results of the analysis of the closest neighbors of the four sub-districts in Ternate City produce clustered settlement patterns. The clustered settlement pattern is dominated by areas that have flat to gentle slopes, with a percentage level of 0-8% flat class and 8-15% sloping class. Based on the settlement pattern map, densely populated areas are located in the Districts of North Ternate, Central Ternate, and South Ternate. The three sub-districts are areas that are included in the flat category which are suitable for settlements and have a wider flat area compared to Ternate Island District
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 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 | 0.003 |
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".