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Record W4381683632 · doi:10.37641/jiakes.v10i3.1597

Analisis Perbandingan Profitabilitas Perusahaan Jasa Sebelum dan Selama Pandemi COVID-19

2022· article· en· W4381683632 on OpenAlexaboutno aff
Rahmadani Rahmadani

Bibliographic record

VenueJurnal Ilmiah Akuntansi Kesatuan · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProfit marginStock exchangeProfitability indexBusinessCoronavirus disease 2019 (COVID-19)Gross marginPandemicReturn on equityOperating marginNonprobability samplingQuarter (Canadian coin)RevenueReturn on assetsAccountingPopulationFinanceGeographyMedicine

Abstract

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This paper aims to find out how significant the comparison of company profitability is before and during the Covid-19 pandemic.The population in this study were all companies in the hospitality industry, restaurant and tourism sub-sector listed on the Indonesia Stock Exchange (BEI) for the period 2019 - 2020. The sample in this study used the purposive sampling method, namely companies that had complete financial reports for the 3rd quarter of 2019 and Quarter 3 of 2020, the number of samples of this study were 31 companies. The results prove that the Gross Profit Margin (GPM), Net Profit Margin (NPM), Operating Profit Margin (OPM), and Return On Asset (ROA) tested with the test wicoxon have a significant effect on the Covid-19 pandemic on GPM, NPM, OPM and ROA. The Return On Equity (ROE) analyzed using the mann whitney also experienced significant differences before and during the Covid-19 pandemic. That way there is a significant difference in company profitability as measured by GPM, NPM, OPM, ROA, and ROE, the hospitality industry, restaurants and tourism sub-sectors listed on the Indonesia Stock Exchange (IDX) before the Covid-19 pandemic and during the Covid-19 pandemic. It is hoped that this research can help investors and interested parties in responding to the Covid-19 pandemic.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.231
Teacher spread0.207 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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