TINGKAT PENDAPATAN ANGGOTA LMDH “LANCAR JAYA” DARI SEKTOR PERTANIAN HORTIKULTURA DI DESA NGANCAR KECAMATAN NGANCAR KABUPATEN KEDIRI
Bibliographic record
Abstract
Abstraction This research was conducted on 10 November 2018 - 31 January 2019 in Ngancar Village, Ngancar District, Kediri Regency. The study was intended to determine the level of income of LMDH "Current Jaya" members in Ngancar Village. In addition, to find out the factors that influence the success of the Forest Village Society Institute (LMDH) program that has been carried out in increasing the income of members of farmer groups. The location of Ngancar Village is due to the fact that the area is one of the tourist areas which has a relatively large number of poor people. Methods of data collection in the form of primary data collected by direct observation techniques in the field with interviews, questionnaires, and documentation studies of respondents (farmer group members) obtained by purposive sampling method. Secondary data is collected by the technique of recording data that already exists in related institutions. The data obtained will be processed by calculation and tabulation. While in the method of data analysis, researchers used two ways, namely an analysis of economic success (income), and the success factor of LMDH. For data processing methods, the income questionnaire uses the farm income formula (π) which is the difference between total receipt (TR) and total cost (TC), while the questionnaire success factor LMDH uses a Likert Scale. Based on the results of the research conducted, Chili (Capsicum annum L) commodity was obtained 68 respondents with a total income of Rp. 3,337,850,000, with an average income per hectare of Rp. 49,086,030 in one planting season. While the commodity Tomato (Solanum lycopersicum L) obtained 47 respondents with a total income of Rp. 1,368,899,000, with an average income per hectare of Rp. 29,125,510 in one planting season.Keywords: income, factor, LMDH
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".