Electronic Theses and Dissertation

Ketergantungan terhadap bahan bakar fosil dan meningkatnya emisi karbon mendorong pengembangan energi terbarukan berbasis biomassa lignoselulosa, salah satunya bioetanol dari ampas kelapa. penelitian ini bertujuan mengkaji pengaruh penggunaan enzim selulase terhadap peningkatan kadar bioetanol dari ampas kelapa serta mengevaluasi hasil destilasi dari kondisi optimum yang diperoleh melalui optimasi menggunakan response surface methodology (rsm) dengan rancangan boxbehnken design (bbd). variabel yang dioptimasi meliputi suhu fermentasi (2835°c), konsentrasi ragi (3–7%), dan massa urea (1–7 g/l). kadar bioetanol diukur menggunakan refraktometer berdasarkan kurva kalibrasi regresi linear dengan nilai r² = 0,9921. hasil uji anova menunjukkan model signifikan dengan p-value sebesar 0,009, di mana suhu dan massa urea merupakan faktor paling berpengaruh secara linear. model regresi kuadratik memiliki r² sebesar 95,07%, r²-adj sebesar 86,18%, dan r²-pred sebesar 81,36%, dengan uji lack of fit menghasilkan p-value 0,963 yang mengkonfirmasi kesesuaian model. kondisi optimum fermentasi diperoleh pada suhu 28°c, konsentrasi ragi 5,06%, dan massa urea 1 g/l, dengan prediksi kadar bioetanol maksimum sebesar 27,71%. hasil ini meningkat signifikan dibandingkan penelitian sebelumnya tanpa enzim yang hanya mencapai 19,842% pada kondisi serupa, menunjukkan peningkatan sebesar 39,82%. proses destilasi dari kondisi optimum menghasilkan volume destilat sebesar 60 ml dari 250 ml sampel fermentasi, dengan volume recovery sebesar 24%. hasil ini membuktikan bahwa pendekatan rsm berbasis enzim efektif dalam mengoptimalkan produksi bioetanol dari ampas kelapa sebagai bahan baku lignoselulosa non-pangan.

The dependence on fossil fuels and increasing carbon emissions have driven the development of renewable energy from lignocellulosic biomass, including bioethanol derived from coconut pulp. This study aimed to investigate the effect of cellulase enzyme application on bioethanol yield from coconut pulp and to evaluate the distillation results obtained from the optimum conditions determined through Response Surface Methodology (RSM) with a Box-Behnken Design (BBD). The optimized variables included fermentation temperature (28–35°C), yeast concentration (3–7%), and urea mass (1–7 g/L). Bioethanol content was measured using a refractometer based on a linear regression calibration curve with R² = 0.9921. ANOVA results demonstrated a significant model with a p-value of 0.009, where temperature and urea mass were the most linearly influential factors. The quadratic regression model achieved R² of 95.07%, adjusted R² of 86.18%, and predicted R² of 81.36%, with a lack-of-fit p-value of 0.963 confirming adequate model fit. Optimal fermentation conditions were identified at 28°C, yeast concentration of 5.06%, and urea mass of 1 g/L, with a predicted maximum bioethanol content of 27.71%. This result represents a significant improvement over a previous study without enzyme application, which achieved only 19.842% under similar conditions, corresponding to a 39.82% relative increase. Distillation of the optimum fermentation sample yielded 60 mL of distillate from 250 mL of fermentation broth, with a volume recovery of 24%. These results confirm that the enzyme-assisted RSM approach is effective in optimizing bioethanol production from coconut pulp as a non-food lignocellulosic feedstock.