ANALISIS DETERMINAN BIAYA TRANSAKSI (Studi Kasus Tambak Bandeng Kecamatan Juwana Kabupaten Pati)
Bibliographic record
Abstract
Kabupaten Pati diketahui sebagai salah satu daerah penghasil ikan budi daya (khusus produksi Bandeng) terbesar di Propinsi Jawa Tengah Kabupaten yang secara geografis terletak di sepanjang pantai utara ini mempunyai kemampuan pengembangan usaha perikanan yang sangat besar, baik perikanan budi daya atau perikanan tangkap. Perikanan di kabupaten Pati cukup potensial untuk dikembangkan dan diharapkan menjadi salah satu sektor andalan dalam pengembangan kemampuan daerah di masa yang akan datang. Penelitian ini dilakukan di Kecamatan Juwana dan bertujuan untuk mengidentifikasi biaya transaksi yang muncul pada petani tambak di Kecamatan Juwana serta menganalisis determinan biaya transaksi yang muncul pada petani tambak Kecamatan Juwana. Penelitian ini menggunakan data cross section dengan sampel 98 petani tambak bandeng. Pendekatan analisis menggunakan statistik deskriptif dan analisis regresi berganda OLS (ordinary least square). Hasil penelitian menunjukkan biaya transaksi yang paling banyak muncul di kalangan petani tambak adalah biaya transportasi, sedangkan determinan biaya transaksi yang terdiri dari ketidakpastian, dan frekuensi yang berpengaruh negatif terhadap biaya transaksi. Jaringan sosial dan jaringan pertemanan berpengaruh positif terhadap biaya transaksi, trust berpengaruh negatif terhadap biaya transaksi, kelembagaan berpengaruh negatif terhadap biaya transaksi dan penyuluhan berpengaruh positif terhadap biaya transaksi. Tittle: Analysis of Transaction Cost Determinants (Study of Tambak Bandeng in Juwana District Pati Regency)Pati Regency is well-known as one of the largest aquaculture area (especially milkfish production) in Central Java Province. The district is geographically located along the north coast. It is potentially developed with a considerable fishery business both in aquaculture and capture fisheries. Its fisheries resource becomes a prospect sector for the future growth of the area. The research was conducted in Juwana Sub-district. It aims to identify transaction costs among the pond farmers in Juwana Sub-district and analyze the determinants of the transaction costs. The study used cross sectional data from 98 milkfish farmers. The analysis approach used descriptive statistics and OLS multiple regression analysis (ordinary least square). Results of the study showed that transaction costs that most frequently occur among pond farmers are transportation costs, while the determinants of transaction costs which consist of uncertainty and frequency negatively affect transaction costs. Friendship and social networks have positive effect on transaction costs, trust has negative effect on transaction costs, institution has negative effect on transaction costs and counseling has positive effect on transaction costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".