Analisis Willingness to Pay Dan Ability to Pay dalam Berlangganan Air Bersih di Desa Cikeruh
Bibliographic record
Abstract
Abstract. Getting optimal clean water is cultivated from good management sources, this is of course the community must be willing to pay household expenses to get good clean water. Many people in Cikeruh Village still use groundwater or wells that are not good if used continuously. The purpose of this study was to determine how much willingness (WTP) and ability (ATP) of the people who have not subscribed to piped clean water connections to pay the tariff determined by PDAM Tirta Medal. The analytical method used is quantitative method with Willingness to Pay (WTP) and Ability to Pay (ATP) analysis. In conclusion, 26% of respondents want to subscribe to PDAM with a willingness to pay the tariff for clean water per m3 at most willing to pay Rp. 2,750/m3 of the tariff price issued by PDAM of Rp. 2.750/m3 for the minimum tariff and Rp. 4.800/m3. m3 for the maximum rate. Meanwhile, from ATP calculations, the value of the community's ability to pay for a PDAM subscription is Rp. 4.277/m3 is close to the maximum tariff issued by PDAM. Abstrak. Mendapatkan air bersih yang optimal diusahakan dari sumber pengelolaan yang baik, hal ini tentu masyarakat harus rela untuk membayar pengeluaran rumah tangga untuk mendapatkan air bersih yang baik. Masyarakat Desa Cikeruh masih banyak yang mengguna air tanah atau sumur yang tidak baik jika dipergunakan secara terus menerus. Tujuan dalam penelitian ini adalah mengetahui seberapa besar kemauan (WTP) dan kemampuan (ATP) masyarakat yang belum berlanggananan sambungan air bersih perpipan dalam membayar tarif yang telah ditentukan oleh PDAM Tirta Medal. Metode analisis yang digunakan adalah metode kuantitatif dengan analisis Willingness to Pay (WTP) dan Ability to Pay (ATP). Hasil kesimpulan didapatkan 26% responden mau berlangganan PDAM dengan kemauan membayar tarif air bersih per m3 paling banyak bersedia membayar sebesar ≤Rp2.750/m3 dari harga tarif yang dikeluarkan pihak PDAM sebesar Rp2.750/m3 untuk tarif paling minimum dan Rp.4.800/m3 untuk tarif paling maksimum. Sementara dari hasil perhitungan ATP didapat nilai kemampuan membayar masyarakat dalam berlangganan PDAM sebesar Rp.4.277/m3 mendekati harga tarif maksimum yang di keluarkan oleh pihak PDAM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".