PENGARUH JUMLAH ANGGOTA DAN STRUKTUR MODAL KOPERASI TERHADAP JUMLAH SISA HASIL USAHA PADA KOPERASI PEGAWAI REPUBLIK INDONESIA DI PURWOKERTO
Bibliographic record
Abstract
The purpose of this study was to determine the effect of the number of members and the capital structure of cooperatives on the amount of Remaining Operations (SHU) on the Cooperative of Employee Republic of Indonesia (KPRI). Samples of 15 KPRI in Purwokerto were obtained by purposive sampling technique. Based on 3 years of observation, the amount of research data is 45. Furthermore, the data were analyzed using panel data regression analysis techniques. The results showed that: (1) The number of members had no effect on SHU; (2) Own capital has a positive effect on the Remaining Results of Operations; and 3) Loan capital has no effect on the Remaining Results of Business. Theoretically, this research only supports the theory of stewardship in terms of own capital. Whereas in terms of number of members and no loan capital, this is because the cooperative managers are none other than a small number of existing members so the existence of loan capital is not a priority to be managed. Practically, this research can be considered for cooperative managers and cooperative members to better manage cooperative cooperatives through increasing the number of members, and utilizing their own capital and existing loan capital. Furthermore, the government is expected to be able to pay more attention and supervise cooperatives so that their existence benefits the Indonesian people.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".