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Record W3203856493 · doi:10.31958/ab.v1i2.2660

Manajemen Risiko Pada Bank Pembangunan Daerah Jawa Barat dan Banten Tbk

2021· article· en· W3203856493 on OpenAlexaboutno aff
Lutfi Alif Tiyani, Diah Febriyanti, Siti Ummi Munawaroh, Ulin Ni’mah

Bibliographic record

VenueAl-bank Journal of Islamic Banking and Finance · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsJavaBusinessLiquidity riskQuarter (Canadian coin)Operational riskMarket riskRisk managementMarket liquidityFinanceFinancial systemGeographyComputer science

Abstract

fetched live from OpenAlex

This study aims to identify and analyze risk management in banking, namely risk, operational risk, market risk, liquidity risk, legal risk, compliance risk, yield risk and investment risk. And to analyze the financial ratio reports for the first quarter and second quarter of 2020 at the West Java and Banten (BJB) Tbk development bank. This research uses descriptive quantitative method at PT. West Java and Banten Regional Development Bank Tbk (BJB) Tbk which are listed on the Indonesia Stock Exchange during the period 2018 to 2020. The sample in this study is PT. West Java Regional Development Bank and Banten Tbk (BJB) which are registered on the IDX and are still operating in the 2018-2020 period. Data collection tools in  this study are to use the method of observation on financial data at the bank and internet  research. The results of this study indicate the existence of smooth and bad conditions in management analysis and financial ratio analysis at BJB banks which consist of analysis of CAR, non-performing assets, CKPN, gross NPF, NPF Net, ROA, ROE, NI, NOM, BOPO, and FDR. The results of the overall calculation at PT. West Java Regional Development Bank and Banten Tbk, in the 2018-2020 period experienced an increase and decrease every year.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2021
Admission routes1
Has abstractyes

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Same venueAl-bank Journal of Islamic Banking and FinanceSame topicSMEs Development and Digital MarketingFrench-language works237,207