TINGKAT KEPUASAN ANGGOTA DAN FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP PARTISIPASI ANGGOTA KUD MINA JAYA KECAMATAN BUMI WARAS DI KOTA BANDAR LAMPUNG
Bibliographic record
Abstract
This research aims to analyze the level of satisfaction members, and the factors that affect on the level of participation members of KUD Mina Jaya in Bumi Waras subsdistrict of Bandar Lampung City. This research is condutedby survey method. Respondents of this research consist of fishermen (ship owner, sailor, ship crew, and ship machine) and nonfisherman (fish merchant). The research respondents constitutes of 87 people, consist of 68 fishermen and 19 nonfisherman and the research data is colledcted inJanuary to May 2017. The results of this research showed that the satisfaction felt by members of the inner ministry RAT, mandatory savings, and utilization of business unit services in categories “satisfied”. Based on the results of IPA, the main priority attributes were sufficient physical infrastructure, staff support, and availability of information boards. The age, length of membership, gender, jobs, and member’s satisfaction of cooperation services were positively correlated to the level of member participation of RAT attending. The length of membership was positively correlatedto the level of member participation in purchasing obligatory deposits. Family burden, jobs, and member’s satisfaction of cooperation services alsopositively correlatedto the level of member’s participation on untilised the business unit services.Key words: cooperative, participation, satisfaction
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".