THE INFLUENCE OF VEGETATION TO INDOOR ILLUMINANCE, AIR TEMPERATURE AND RELATIVE HUMIDITY IN DESIGN STUDIO (Case Study: Architecture Department, Petra Christian University, Surabaya)
Bibliographic record
Abstract
This research aimed to find the influence of vegetation outside the window to indoor light and thermal in Design studio, Architecture department of Petra Christian University, Surabaya. The vegetation was Shibataea kumasasa, cultivated in planter boxes, 90-100 centimeter high. The data was collected from the West and North window, for condition with and without vegetation outside the window. The measurement was taken on March until May 2019, using Hobo U12-012 data logger for measuring air temperature (°C), relative humidity (%) and illuminance (lux). Beside those physical measurement, the perception of 89 students as building user was also taken. The measurement result from windows facing West and North with the vegetation, the lowest indoor air temperature was 27°C with an average relative humidity of 70%. Meant it's at the upper edge of the comfort zoneof Bioclimatic Chart from Olgyay and Canada’s National Occupational Health & Safety Resource (CCOHS), so the indoor space was still uncomfortable and cooling was required to get into the comfort zone at warm humid climate. Result from lighting measurements obtained for west-oriented windows with vegetation outside the window, the highest illuminance was 350 lux at 02:45 pm, as well as for windows facing North at 07.30 am - 04.45 pm in range of 105 - 155 lux (highest). So, if there are vegetation outside windows, it will require electric lighting addition, because the illuminance are below the SNI 03-6197-2011which recommends 750 lux for design studio. Whilst the user respond from the questionnaire taken, stated that aesthetic vegetation was expected for natural element, blocked the penetration of solar radiation into indoor, and reduce glare to increase visual comfort without reducing luminous sufficiency for the visual activities in the design studio.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".