Perencanaan Sistem Long Term Evolution di Wilayah Kota Denpasar Memanfaatkan Bale Banjar untuk Menempatkan Base Station
Bibliographic record
Abstract
Perencanaan sistem jaringan LTE dipengaruhi oleh beberapa faktor, salah satunya adalah faktor kontur wilayah yang berbeda. Kontur permukaan wilayah berpengaruh terhadap cakupan area yang dihasilkan suatu base station. Perencanaan jaringan berdasarkan analisis perhitungan, tidak mempertimbangkan kontur wilayah, maka untuk menunjang hal tersebut diperlukan simulasi menggunakan software Atoll. Metode pada penelitian ini menggunakan perhitungan dan simulasi software Atoll. Penempatan base station memanfaatkan 385 Bale Banjar sebagai titik site menara rooftop. Penempatan ini dilakukan di Bale Banjar dikarenakan mudahnya akses perijinan tempat, seperti base station pada Bale Banjar Balun. Berdasarkan hasil perhitungan dan pemodelan simulasi jarak jangkauan antena base station dengan model propagasi Cost-231 Hatta frekuensi 1800 MHz, diperoleh jarak sebesar 1,186 km. Simulasi perencanaan sistem LTE di wilayah kota Denpasar memerlukan 55 site, terdiri dari 54 site yang memanfaatkan Bale Banjar untuk menempatkan base station dan 1 site di luar kawasan Bale Banjar, serta diperlukan pengaturan electrical tilt sebanyak 40 site untuk mengatasi permasalahan cakupan area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".