Peran Generasi Milenial Terhadap Perkembangan Fintech Studi Kasus Mahasiswa Perbankan Syariah STAI Syekh H. Abdul Halim Hasan Al-Ishlahiyah Binjai
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".