PERANCANGAN SMART VERTICAL GARDEN SEBAGAI STRATEGI MENINGKATKAN RUANG HIJAU DAN KENYAMANAN TERMAL
Bibliographic record
Abstract
Balikpapan, located in East Kalimantan Province, Indonesia, is experiencing rapid growth accompanied by increasingly complex environmental challenges, including the effects of climate change. Data shows a rise in air temperature, impacting not only the outdoor environment but also the indoor thermal conditions of public buildings such as offices, shopping centers, and educational institutions. At Institut Teknologi Kalimantan (ITK), the increasing demand for air conditioning systems reflects the direct impact of global temperature rise, resulting in heightened energy use and greenhouse gas emissions. In response to these issues, this research explores the design and implementation of smart vertical gardens as an innovative solution to enhance thermal comfort and energy efficiency. The smart vertical garden utilizes shading plants, sensor technology, and automation to reduce a building’s carbon footprint while improving thermal comfort. Building B at ITK is chosen as the case study due to its function as a hub of academic activities, making it a strategic location for implementing this green technology. The research adopts a comprehensive approach, including literature review, empirical data collection, thermal analysis, simulation, and design. The findings demonstrate the effectiveness of the smart vertical garden in reducing cooling energy demand, improving thermal comfort, and promoting campus greening. The implementation of this technology has the potential to serve as a sustainability model for public buildings. The results of this study provide valuable insights for academics, practitioners, and policymakers in developing green strategies and advancing sustainable development.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".