Arahan Lokasi Septic Tank Komunal sebagai Upaya Penanganan Permukiman Kumuh Perkotaan di RW 003 Kelurahan Pegangsaan Dua, Kecamatan Kelapa Gading, Jakarta Utara
Bibliographic record
Abstract
Abstract. By 2022, only 36.23% of households will live in a decent house. only 36.23% of households. The capital city of DKI Jakarta is the third highest in Indonesia with the percentage of households living in slums reaching 18.82%. Slums are spread across the city of Jakarta, with the highest poverty in North Jakarta at around 39%. One of the main problems in North Jakarta, especially RW 003 Kelurahan Pegangsaan Dua, is the lack of septic tanks in each house so that household waste is disposed of carelessly. Open defecation activities in RW 03 as of 2023, there were 1,235 households or around 6,175 people who still practiced open defecation. This study aims to plan the distribution of communal septic tanks and determine the ideal distribution of communal septic tank locations in accordance with the designation and minimize the quality of slum areas. Based on the percentage of open defecation and the use of shared latrines, the number of people without access to sanitation was calculated. unserved access to sanitation, then divided by the maximum capacity of 1 TSK unit. In 2024, 43 TSKs are needed to serve RW 003. Abstrak. Pada tahun 2022 yang menempati rumah layak huni hanya 36,23% rumah tangga. Ibu Kota DKI Jakarta merupakan tertinggi ketiga di Indonesia dengan persentase rumah tangga tinggal di pemukiman kumuh mencapai 18,82%. Pemukiman kumuh tersebar di kota Jakarta, dengan kemiskinan tertinggi di Jakarta Utara sekitar 39%. Salah satu masalah utama di Jakarta Utara, khususnya RW 003 Kelurahan Pegangsaan Dua adalah kurangnya septic tank di setiap rumah sehingga limbah rumah tangga dibuang sembarangan. Kegiatan BABS di RW 03 per 2023 teridentifikasi sebanyak 1.235 KK atau sekitar 6.175 jiwa yang masih melakukan kegiatan BABS. Studi ini bertujuan untuk merencanakan sebaran tangki septic tank komunal dan menentukan sebaran lokasi septic tank komunal yang ideal sesuai dengan peruntukan dan meminimalisir kualitas kawasan kumuh. Berdasarkan persentase Buang Air Besar Sembarangan (BABS) dan penggunaan jamban bersama, dihitung jumlah penduduk yang tidak terlayani akses sanitasi, kemudian dibagi dengan kapasitas maksimum 1 unit TSK. Pada 2024, dibutuhkan 43 TSK untuk melayani RW 003.
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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 | 0.000 | 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) | 0.008 | 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".