Penentuan Prioritas Penanganan Banjir di Kecamatan Ciputat Kota Tangerang Selatan
Bibliographic record
Abstract
Abstract. Ciputat District in South Tangerang City is a flood-prone area through the Angke Watershed. There are 9 flood locations, 2 of which have been addressed and the rest have not been addressed. Limited budget for handling and the increasing number of affected victims are the reasons why it is necessary to study the handling priorities. This study aims to determine the priority of flood handling using a scoring method according to Ministerial Regulation of Public Works No. 12 of 2014 by considering flood parameters (depth, area, duration, frequency) as well as aspects of economic, social, transportation, housing, and private property losses. Data were obtained through observation, digitization, thematic maps, and field validation. The results show the order of priority for flood handling, namely Gardenia Estate (priority I), Cilalung (priority II), Serua Permai (priority III), Jl. Aria Putra and Inhutani Complex (priority IV), and Villa Dago Tol and Serua Makmur (priority V). The main factors determining priorities vary in each location, such as housing density, inundation area, and transportation disruption. These findings emphasize the importance of comprehensive flood parameter-based assessments to support spatial planning and flood mitigation strategies in flood-prone areas. Abstrak. Kecamatan Ciputat di Kota Tangerang Selatan merupakan wilayah rawan banjir yang dilalui DAS Angke. Terdapat 9 lokasi banji, 2 di antaranya sudah dilakukan penanganan dan sisanya belum dilakukan penanganan. Terbatasnya anggaran penanganan dan korban terdampak yang semakin meningkat, menjadi alasan mengapa perlu dikaji prioritas penanganannya. Penelitian ini bertujuan menentukan prioritas penanganan banjir menggunakan metode skoring sesuai Permen PU No. 12 Tahun 2014 dengan mempertimbangkan parameter banjir (kedalaman, luas, lama, frekuensi) serta aspek kerugian ekonomi, sosial, transportasi, perumahan, dan kepemilikan pribadi. Data diperoleh melalui observasi, digitasi, peta tematik, serta validasi lapangan. Hasil penelitian menunjukkan urutan prioritas penanganan banjir yaitu Gardenia Estate (prioritas I), Cilalung (prioritas II), Serua Permai (prioritas III), Jl. Aria Putra dan Komplek Inhutani (prioritas IV), serta Villa Dago Tol dan Serua Makmur (prioritas V). Faktor utama penentu prioritas berbeda di tiap lokasi, seperti kepadatan perumahan, luas genangan, maupun gangguan transportasi. Temuan ini menegaskan pentingnya penilaian komprehensif berbasis parameter banjir untuk mendukung perencanaan ruang dan strategi mitigasi banjir di wilayah banjir.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".