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Record W7018803892

EFEK MUSIK KLASIK DAN MUROTTAL TERHADAP
\nPERKECAMBAHAN BENIH MAHONI (Swietenia mahagoni (L.) Jacq.)

2021· other· id· W7018803892 on OpenAlexaboutno aff

Bibliographic record

VenueDigilib Repository Unila (Lampung University) · 2021
Typeother
Languageid
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHabitParaphernaliaPopulation
DOInot available

Abstract

fetched live from OpenAlex

Suara musik memiliki frekuensi yang dikenal tidak hanya mampu
\nmemberikan dampak positif terhadap kesehatan manusia, juga terhadap
\npertumbuhan tanaman. Namun, tidak semua tanaman mampu merespon baik jenis
\nmusik yang sama. Penelitian ini bertujuan untuk melihat pengaruh suara musik
\nklasik dan murottal terhadap perkecambahan benih mahoni (Swietenia mahagoni
\n(L.) Jacq.). Penelitian ini dilaksanakan di Rumah Kaca Laboratorium Lapang
\nTerpadu dan Laboratorium Silvikultur dan Perlindungan Hutan Fakultas Pertanian
\nUniversitas Lampung pada bulan Januari–Maret 2021. Perlakuan disusun dengan
\nmenggunakan Rancangan Acak Lengkap (RAL) yang terdiri atas 3 perlakuan dan
\n3 ulangan. Perlakuan suara yang diberikan adalah musik klasik karya Mozart:
\nEine kleine Nachtmusik: McGill Symphony Orchestra Montreal conducted by
\n
\nAlexis Hauser dengan rentang level suara 73,4–102,3 dBA dan murottal surah Al-
\nHadid (57:1–29) oleh Ammar Fathani dengan rentang level suara 80,1–107,6
\n
\ndBA. Data hasil pengamatan dianalisis dengan Uji Anova dan Uji Beda Nyata
\nTerkecil (BNT) pada taraf 5%. Hasil penelitian menunjukkan bahwa (1)
\nperlakuan musik klasik dinilai lebih baik dibandingkan perlakuan murottal dan
\nkontrol karena memberikan pengaruh yang nyata terhadap beberapa parameter
\npengamatan, (2) perlakuan musik klasik berpengaruh terhadap rerata bobot basah
\n(2,72 g), bobot kering (2,26 g) dan menghasilkan jumlah kecambah abnormal
\nsebanyak 3 dan 4 kali lebih rendah dibandingkan perlakuan murottal dan kontrol.
\nSedangkan perlakuan murottal berpengaruh paling baik terhadap rerata jumlah
\ndaun (4,16 helai).
\nKata kunci: musik klasik; murottal; perkecambahan; Swietenia mahagoni (L.)
\nJacq.
\nThe sound of music has a frequency that is known not only to have a
\npositive impact on health, but also on plant growth. However, not all plants can
\nrespond to either of the same kind of music. This study aims to look at the
\ninfluence of classical and muottal music sounds on the germination of mahogany
\nseeds (Swietenia mahagoni (L.) Jacq.). This research was conducted in Integrated
\nField Laboratory Greenhouse and Silviculture Laboratory and Forest Protection
\nFaculty of Agriculture, the University of Lampung in January–March 2021. The
\ntreatment was prepared using a Completely Group Plan consisting of three
\ntreatments and three replays. The treatment that given to the plant using classical
\nmusic by Mozart: Eine Kleine Nachtmusik: McGill Symphony Orchestra
\nMontreal conducted by Alexis Hauser with a sound level range of 73.4–102.3
\ndBA and murottal surah Al-Hadid (57:1–29) by Ammar Fathani with a sound
\nlevel range of 80.1–107.6 dBA.The observation data were analyzed with Anova
\ncalculation and Least Significance Different (LSD) at a 5% level. The results
\nshowed that (1) classical music treatment was rated better than control and
\nmurottal treatment because it exerted a noticeable influence on several
\nobservation parameters, (2) classical music treatment had the best effect on the
\naverage wet weight (2.72 g), dry weight (2.26 g) and resulted in abnormal
\namounts of sprouts 3 and 4 times lower than murottal and control treatments. In
\ncomparison murottal treatment has the best effect on the average number of leaves
\n(4.16 strands).
\nKeywords: classical music; murottal; germination; Swietenia mahagoni (L.) Jacq.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.951
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.005
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0050.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.189
Teacher spread0.180 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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