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Record W4385510589 · doi:10.59342/jpkm.v1i1.8

Peran Generasi Milenial Terhadap Perkembangan Fintech Studi Kasus Mahasiswa Perbankan Syariah STAI Syekh H. Abdul Halim Hasan Al-Ishlahiyah Binjai

2021· article· en· W4385510589 on OpenAlexaff
Raja Sakti Putra Harahap, Ridha Rachmadita, Erlina Erlina, Juanda Risja

Bibliographic record

VenueAltafani · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsTerahertz Technology Solutions (Canada)
Fundersnot available
KeywordsSocializationDocumentationBusinessData collectionFinTechPolitical scienceMarketingPsychologySociologyFinancial servicesSocial scienceFinanceComputer science

Abstract

fetched live from OpenAlex

The continuous development of technology that makes all fields rapidly change including changes in the education sector, industry, as well as the economic, financial and banking sectors. The link between the millennial generation and the development of fintech, one of which is because the millennial generation is a technology literate generation that is able to accept a financial technology innovation. The purpose of the study was to determine the extent of the role of the millennial generation in the development of fintech. This research was conducted on Islamic banking students at STAI Al-Ishlahiyah Binjai Stambuk 2016, and the data collection in this study was done through a qualitative descriptive method. The method that the author uses in analyzing the data in this study is inductive where the data that the researcher managed to collect from the research location, then analyzed and then presented in writing in the report, namely in the form of data found from interviews, and documentation. The results of research on Islamic banking students at STAI Al-Ishlahiyah Binjai regarding the role of the millennial generation in the development of fintech are very important, this is shown by the results of interviews that researchers have conducted, namely 7 out of 10 students have understood fintech and they participate in the development of fintech which in this they have played a role in the development of financial technology today, and the rest of them do not understand and are not interested in using fintech due to lack of education and socialization about fintech.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 designNot applicable
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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