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Record W4391284359 · doi:10.24843/mite.2023.v22i02.p10

Analisis Efisiensi Energi antara Lampu LED dan Lampu Konvensional (Studi kasus: Pada Hotel Cap Karoso)

2023· article· id· W4391284359 on OpenAlexaff
Lukito Pramono, Linawati Linawati, Rukmi Sari Hartati

Bibliographic record

VenueMajalah Ilmiah Teknologi Elektro · 2023
Typearticle
Languageid
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Lampu LED merupakan salah satu contoh metode efisiensi dari lampu konvensional karena hasil efisiensi energinya yang lebih tinggi. Pada penelitian ini dilakukan analisis perbandingan efisiensi listrik antara lampu LED dan lampu konvensional. Penelitian ini mengumpulkan data dari sejumlah lampu yang digunakan pada studi perbandingan lampu LED dan lampu konvensional pada Hotel Cap Karoso dan diuji dalam kondisi yang sama. Jenis lampu LED yang digunakan adalah recessed downlight, track light, pendant light dan wall scone. Sementara jenis lampu konvensional yang digunakan adalah incandescent, halogen, fluorescent dan HID. Dilakukan perbandingan dan perhitungan dari sisi energi watt dan biaya listrik. Berdasarkan parameter tersebut dapat dilihat energi yang dihasilkan dan biaya yang dibutuhkan dari kedua jenis lampu LED dan lampu konvensional. Berdasarkan perhitungan dan perbandingan kedua jenis lampu yang digunakan, didapatkan perhitungan bahwa lampu LED dapat menghemat energi dan penghematan biaya sebesar 27%.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.006

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.

Opus teacher head0.014
GPT teacher head0.224
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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