Faktor Ruang Non Ekonomi yang Mempengaruhi Kualitas Sanitasi di Kelurahan Kalibaru
Bibliographic record
Abstract
Abstract. Sanitation is an important factor to life and public health. Nearly 70 percent of drinking water in Indonesia are polluted with fecal sludge, which has causing the spread of various diseases. This sanitation problem also occurs in Kalibaru urban village, North Jakarta. This is because Kelurahan Kalibaru is included in the urban area of North Jakarta with high density and is located in a coastal area. Kelurahan Kalibaru has a number of priority issues such as water clean water and sanitation. The quality of sanitation in Kalibaru Kelurahan is poor because the cost is high. But from the data obtained, the economic data of Kelurahan Kalibaru's economy is quite good, so the economy is not the factors that affect the quality of sanitation in Kelurahan Kalibaru. Therefore, there are other spatial factors such as physical and social factors that influence the quality of sanitation in Kelurahan Kalibaru. This research uses the Analytical Hierarchy Process (AHP) method to determine the most influential priority factors according to experts. It was found that water conditions have a high based on the physical space factor, while for the social space factor education and government elements in the community are the most influential factors. Abstrak. Sanitasi merupakan faktor penting untuk menunjang kehidupan dan kesehatan masyarakat. Hampir 70 persen sumber air minum di Indonesia tercemar lembah tinja yang menyebabkan adanya penyebaran berbagai penyakit. Permasalahan sanitasi ini juga terjadi di Kelurahan Kalibaru, Kecamatan Cilincing, Jakarta Utara. Hal ini dikarenakan Kelurahan Kalibaru termasuk dalam kawasan perkotaan Jakarta Utara dengan tingkat kepadatan yang tinggi dan berada di wilayah pesisir. Kelurahan Kalibaru memiliki beberapa permasalahan prioritas seperti masalah air bersih, persampahan, dan juga sanitasi. Kualitas sanitasi di Kelurahan Kalibaru buruk karena biaya yang cukup tinggi dan masyarakat tidak dapat membayarnya. Tetapi dari data yang didapatkan, data ekonomi Kelurahan Kalibaru cukup baik sehingga ekonomi seharusnya tidak termasuk ke dalam faktor yang mempengaruhi kualitas sanitasi di Kelurahan Kalibaru. Oleh karena itu adanya faktor ruang lainnya seperti faktor fisik dan sosial yang mempengaruhi kualitas sanitasi di Kelurahan Kalibaru. Penelitian ini menggunakan metode Analisis Hirarki Proses (AHP) untuk menentukan faktor prioritas yang paling berpengaruh menurut ahli, sehingga didapatkan bahwa kondisi air mempunyai pengaruh yang tinggi berdasarkan faktor ruang fisik, sedangkan untuk faktor ruang sosial pendidikan dan unsur pemerintah dalam kemasyarakatan menjadi faktor yang berpengaruh pada kualitas sanitasi di Kelurahan Kalibaru, Jakarta Utara.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".