Pemanfaatan GIS untuk Perencanaan dan Optimasi PLTS Rooftop di Gedung Teknik UNG
Bibliographic record
Abstract
Penelitian ini membahas pemanfaatan Geographic Information System (GIS) untuk perencanaan dan optimasi Pembangkit Listrik Tenaga Surya (PLTS) rooftop di Gedung Fakultas Teknik Universitas Negeri Gorontalo (UNG) Kampus 4. Dengan pendekatan integratif berbasis GIS dan simulasi teknis mendalam melalui perangkat lunak HelioScope, penelitian ini berhasil mendesain sistem dengan kapasitas terpasang sebesar 368,79 kWp yang menggunakan 647 modul surya tipe Canadian Solar 570W. Berdasarkan simulasi, sistem ini diproyeksikan mampu menghasilkan energi listrik sebesar 486,5 MWh per tahun. Analisis teknis mengidentifikasi konfigurasi sistem optimal menggunakan 99 unit inverter Huawei 3kW serta faktor-faktor kehilangan energi. Hasil simulasi menunjukkan kinerja yang sangat baik dengan nilai solar access rata-rata mencapai 99,2% yang mengindikasikan hampir tidak adanya dampak bayangan pada seluruh array. Penerapan GIS dalam pemetaan spasial lokasi memberikan kontribusi signifikan dalam menentukan potensi atap, tata letak panel yang terbagi dalam 7 segmen field (C2, C3, C4, C5, C6, Civil Lab, Industry Lab), dan efisiensi sistem secara keseluruhan. Selain itu, kajian ekonomi yang didasarkan pada data kapasitas dan produksi energi aktual ini menunjukkan prospek implementasi yang layak dan berkelanjutan. Hasil penelitian ini diharapkan dapat menjadi model percontohan yang akurat dan siap implementasi bagi pengembangan PLTS rooftop di UNG serta institusi pendidikan lainnya di kawasan Indonesia Timur, mendukung pencapaian target kampus hijau dan net zero emission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".