ANALISIS USAHA PENGOLAHAN KERIPIK PISANG COKLAT DI KABUPATEN WAY KANAN PROVINSI LAMPUNG
Bibliographic record
Abstract
Keripik pisang coklat merupakan jenis makanan ringan terbuat dari buah pisang yang diiris dan digoreng diberi rasa coklat. Penelitian ini bertujuan untuk menentukan total biaya, total penerimaan, total pendapatan, nilai BEP dan nilai R/C pada usaha pengolahan keripik pisang coklat di Kabupten Way Kanan Provinsi Lampung. Metode pada penelitian ini adalah deskriptif analitis. Penentuan lokasi menggunakan metode purposive sampling. Pengambilan sampel responden menggunakan metode sensus. Data yang digunakan merupakan data primer dan data sekunder. Hasil yang diperoleh penelitian ini adalah total biaya sebesar Rp 10.151.719/bulan, total penerimaan sebesar Rp14.422.500/bulan, dan total pendapatan sebesar Rp4.362.892/bulan. Nilai Break Even Point (BEP) atas dasar unit adalah sebesar 810 bungkus/bulan dengan masing-masing berat perbungkus 250gr. Nilai ini lebih kecil dari pada rata-rata produksi 1163/bulan dan nilai Break Even Point (BEP) atas dasar harga sebesar Rp. 8.650/bungkus. Nilai ini lebih kecil dari harga produk yang sebesar Rp. 12.417. Nilai R/C usaha pengolahan keripik pisang coklat sebesar 1,43. Dari nilai R/C tersebut disimpulkan bahwa usaha pengolahan keripik pisang coklat layak untuk diusahakan karena besar nilai R/C >1. Kata kunci: Keripik Pisang Coklat, Pengolahan, Kabupaten Way Kanan
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".