The role of financial technology on development of MSMEs
Bibliographic record
Abstract
The purpose of this research is to describe the role of Financial Technology in enhancing financial inclusion in the Micro, Small and Medium Enterprises (MSMEs) industry through accessibility and assistance. MSMEs play a very important role in increasing regional and national economic growth. There are various types of MSMEs that are scattered throughout Indonesia with the main problem being capital. The rapid growth of FinTech's financing business is currently an alternative that can be accessed by all levels of society through financial inclusion, which is one way to socialize the financial sector specially to facilitate financial access services for the public. The population in this study were members of Dekranasda (Dewan Kerajinan Nasional Daerah) Denpasar assisted and the determination of samples was based on purposive sampling method which includes people involved in a weaving craft business and have been fostered for at least 3 years. The method of data collection is by questionnaires, documentation and interviews. The method of data analysis in this study is the instrument test, classic assumption test, and hypothesis testing with the SPSS program. Based on the results of the analysis of accessibility and assistance, financial technology has a significant positive effect on capital development. By funding MSMEs, lenders get investment alternatives with attractive returns. On the other hand, MSMEs borrowers get business capital loans without collateral with an easy and fast online process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".