ANALISIS KESEPAKATAN PENINGKATAN PRODUKTIVITAS KOPI ARABIKA PADA PENGEMBANGAN KAWASAN DI KABUPATEN TORAJA UTARA
Bibliographic record
Abstract
AbstrakPeningkatan produktivitas kopi merupakan upaya untuk memberikan penambahan mutu atau kualitas produk, melalui penerapan teknologi sesuai pedoman pengelolaan tanaman dengan baik dan benar. Pencapaian produktivitas kopi di Indonesia masih rendah yaitu baru mencapai 700 kg/ha/tahun. sedangkan potensi yang dimilikinya yaitu 1.200 kg/ha/thn. Petani sebagai pelaku utama usahatani kopi Arabika memiliki peranan yang sangat kuat dalam mengambil keputusan pelaksanaan kegiatan usahatani kopi Arabika. Penelitian ini dilaksanakan pada bulan Januari hingga Desember 2018. Pelaksanaannya di Kecamatan Kapalapitu Kabupaten Toraja Utara. Penentuan lokasi ini berdasarkan bahwa lokasi kegiatan sebagai lokasi pengembangan tanaman kopi Arabika. Jumlah petani yang diambil sebagai sampel sebanyak 60 petani yaitu Kelompok Tani Harapan (30 petani) dan Kelompok Tani Marannu (30 petani). Metode analisis data yang digunakan untuk mengetahui biaya pada tahun berjalan. Sedangkan analisis respon petani terhadap peningkatan produksi kopi Arabika, menggunakan analisis uji kesepakatan konkordansi kendall’s. Hasil penelitiaan menujukkan Karakteristik petani kopi Arabika di wilayah Kabupaten Toraja Utara memiliki kelompok usia produktif. Tingkat pendidikan yang dimiliki sebagian besar pada pendidikan 7-9 tahun. Anggota rumah tangga tani sebagian besar berkisar 3-5 anggota/kk. Penerapan kegiatan usahatani kopi Arabika yang dilakukan oleh petani belum optimal. Seperti penggunaan bahan tanam petani masih menggunakan bibit yang tumbuh disekitar tanaman kopi dari biji yang dipetik jatuh. Petani menilai terhadap kesubutan tanaman kopi Arabika pada kelompok sedang. Sedangkan tingkat produktivitas tanaman kopi dikelompokkan sedang. Petani mengenal terhadap pupuk organik sudah bagus. Upaya peningkatan produktivitas kopi Arabika petani sepakat melalui: penyuluhan/pelatihan yang intensif dan penyebaran informasi teknologi produksi kopi Arabika. Usahatani kopi dengan penerapan teknologi dapat meningkatkan produksi dan pendapatan petani dengan MBCR 2,01. Kata Kunci : Kopi Arabika, produktivitas, kesepakatan. abstractIncreased coffee productivity is an effort to provide additional quality or product quality, through the application of technology in accordance with the guidelines for managing plants properly and correctly. The achievement of coffee productivity in Indonesia is still low, reaching only 700 kg/ha/year. While its potential is 1,200 kg/ha/year. Farmers as the main actors of Arabica coffee farming have a very strong role in making decisions regarding the implementation of Arabica coffee farming activities. This research was conducted from January to December 2018. The implementation was in Kapalapitu District, North Toraja Regency. Determination of this location is based on the location of the activity as a location for developing Arabica coffee plants. The number of farmers taken as a sample is 60 farmers, namely the Harapan Farmers Group (30 farmers) and the Marannu Farmers Group (30 farmers). Data analysis method used to determine costs in the current year. While the analysis of farmers' responses to the increase in Arabica coffee production, using a test analysis of Kendall's agreement. The results of the research show the characteristics of Arabica coffee farmers in the North Toraja Regency region that have a productive age group. The level of education held is mostly for education 7-9 years. Most members of farm households range from 3-5 members/family. The application of Arabica coffee farming activities carried out by farmers is not optimal. Like the use of planting material, farmers still use seeds that grow around the coffee plant from the seeds that are picked down. Farmers assess the fertility of Arabica coffee plants in the medium group. While the productivity level of coffee plants is classified as medium. Farmers know about organic fertilizer is good. Efforts to increase the productivity of Arabica coffee farmers agree through: intensive counseling/training and information dissemination on Arabica coffee production technology. Coffee farming with the application of technology can increase the production and income of farmers with MBCR 2.01.Keywords: Arabica coffee, productivity, agreement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".