ANALISIS KEUNTUNGAN USAHATANI KELAPA DI KECAMATAN PADANGSIDIMPUAN BATUNADUA
Bibliographic record
Abstract
As a traditional crop, coconut is a versatile plant (tree of life) and has an economic value that is good enough to be developed particularly in the context of community economic development. This study aims to determine the effect of input variables X1 (Wide Land), X2 (Labor Costs), and X3 (Cost Fertilizer) against the benefits of coconut farm in the district Padangsidimpuan Batunadua. Analytical model used is the Cobb-Douglas profit function with the help of Eviews v5.1 application.The results showed that simultaneous variables X1, X2, and X3 affect the benefits of coconut farm with F-stat is 1728,765. partially each variable as: X1 shows a positive and significant impact on profits by ilai tcount 53.811 and Prob. Of 0.000., X2 showed positive and significant influence on profits by the value of 21.503 tcount and Prob. Of 0.000., and X3 shows a negative influence to the value of -2.511 tcount and Prob. Of 0.0138. Furthermore, from the obtained values for the regression coefficient of 0.9834 X1 means any addition of land area per ha will increase the gain of 0.9864 rupiah per Ha, X2 of 0.9757 means that any additional labor costs / yields would increase the profit of 0.9575 rupiah and -0.0651 for X3 which means every addition 1 rupiah of fertilizer costs will reduce profits 0,0651 rupiah. From the analysis it can be concluded, that the coconut farm production and profits in Kecataman Padangsidimpuan Batunadua still can be improved by optimizing the use of variable inputs of fertilizer and land.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".