PENGARUH SUHU PENGERINGAN TERHADAP KARAKTERISTIK BUAH KERING PEPAYA (Carica papaya L.) MENGGUNAKAN \nMETODE TRAY DRYING
Bibliographic record
Abstract
Pengaruh Suhu Pengeringan Terhadap Karakteristik Buah \nKering Pepaya (Carica Papaya L.) Menggunakan \nMetode Tray Drying \n \n \nAriska Anandra, Ismed, Kesuma Sayuti \n \nABSTRAK \n \nPenelitian ini bertujuan untuk mengetahui pengaruh suhu pengeringan terhadap karakteristik buah kering pepaya menggunakan metode tray drying serta memperoleh suhu pengeringan yang optimum. Rancangan percobaan yang digunakan pada penlitian ini adalah rancangan acak lengkap (RAL) dengan 5 perlakuan dan 3 ulangan. Perlakuan pada penelitian ini adalah buah kering dengan perlakuan A (suhu pengeringan 50ºC), B (suhu pengeringan 55ºC), C (suhu pengeringan 60ºC), D (suhu pengeringan 65ºC), E (suhu pengeringan 70ºC). Data yang diperoleh kemudian dianalisis dengan ANOVA dan jika berbeda nyata dilanjutkan dengan uji Duncan’s New Multiple Range Test (DNMRT). Hasil penelitian menunjukkan bahwa perbedaan suhu pengeringan berpengaruh nyata terhadap rendemen, kekerasan, kadar air, kadar abu, nilai pH, total karotenoid, dan organoleptik tekstur serta berpengaruh tidak nyata terhadap warna, aktivitas antioksidan, organoleptik rasa, organoleptik warna dan organoleptik aroma. Perlakuan terbaik berdasarkan pengamatan fisik, kimia, dan organoleptik adalah buah kering pepaya pada perlakuan B (suhu pengeringan 55ºC) selama 9 jam dengan hasil nilai rata-rata kesukaan terhadap warna 3,80 (suka), rasa 3,80 (suka), aroma 3,04 (suka), tekstur 3,32 (suka), rendemen (13,28%), kekerasan (88,71 N/cm²), nilai ºHue 47,38 dengan warna (merah), kadar air (15,53%), kadar abu (3,32%), nilai pH (5,61), aktivitas antioksidan (39,19%), total karotenoid (0,14 mg/100g) dan angka lempeng total (9,0 x 10⁴ CFU/g). \n \nKata kunci: pepaya, suhu pengeringan, tray drying, buah kering.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".