Development of Subsidized Housing Scheme with Sustainable Transportation: A Case Study of Housing in the Urban Fringe of Semarang and Kendal
Bibliographic record
Abstract
Abstract The increase in the price of buying houses by 23.77% in the first quarter of 2019 has caused severe challenges. This made the Indonesian government create a subsidized housing program through the Regulation of the Minister of Public Works and Public Housing No 21/PRT/M/2016. This is necessary because the existing subsidized housing is far from the city center and does not pay attention to integrated public transportation as well as the high use of private vehicles causing the value of the degree of saturation to reach 0.77 on housing access which makes the whole scheme environmentally unfriendly. Therefore, this research was conducted to analyze the factors influencing the community to select a subsidized housing program and determine the design of its integration with sustainable transportation using simulation methods. The findings showed the most influential factors were accessibility and choice of transportation modes while the ideal simulation reported the use of integrated transportation in the construction of subsidized housing, by making all private vehicle passengers shift to public transportation in Kendal District reduced the highest saturation level (DS) from 0.77 to 0.07 and CO2 emissions by 47.71%. Therefore, the revision of government policies on integrated transportation in subsidized housing is recommended.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".