PENDAMPINGAN PENGELOLAAN KEUANGAN DAN PENYUSUNAN LAPORAN KEUANGAN BERBASIS ANDROID PADA UMKM KELURAHAN MANGUNHARJO KECAMATAN TEMBALANG KOTA SEMARANG
Bibliographic record
Abstract
Despite its large contribution to the economy, MSMEs players face various problems, including access to capital, poor financial management and the absence of an adequate medium and long term financial management strategy. This community service is carried out with the general objective of empowering Micro, Small and Medium Enterprises to be able to survive and develop with their own uniqueness. The specific objectives of this activity are for the target MSMEs to have adequate financial management and for the target MSMEs to be able to prepare simple financial reports. The existence of a pandemic has made mentoring activities carried out online with a zoom platform. Financial management assistance is provided by providing some knowledge / skills related to financial management, especially during times of crisis. The accounting system used uses a mobile application platform in collaboration with the Indonesian Institute of Accountants which is available for free at the appstore, namely Si Apik. Activities of assistance in financial management and preparation of android-based financial reports for MSMEs are very beneficial. Assistance activities are very useful and practical for MSME entrepreneurs. Assistance needs to be carried out continuously to instill a good accounting and financial mindset in the management of MSME business finances.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.016 |
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".