Identification of Bioethanol Quality for Motorcycle Fuel

Tuti Angraini (1), Zas Ressy Aidha (2), Anton (3), Dedi Kurniadi (4), Cipto Prabowo (5)
(1) Department of Electronics Engineering, Politeknik Negeri Padang
(2) Department of Electronics Engineering, Politeknik Negeri Padang
(3) Department of Electronics Engineering, Politeknik Negeri Padang
(4) Department of Electronics Engineering, Politeknik Negeri Padang
(5) Department of Information Technology, Politeknik Negeri Padang
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How to cite (IJASEIT) :
Angraini, T., Aidha, Z. R., Anton, Kurniadi, D., & Prabowo, C. (2023). Identification of Bioethanol Quality for Motorcycle Fuel. International Journal of Advanced Science Computing and Engineering, 5(3), 278–286.

The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, while the minimum temperature recorded was 87°C. This study utilized a backpropagation artificial neural network method to identify the detected bioethanol. The architecture of the artificial neural network included 2 input nodes, 4 neuron nodes, and 2 output nodes. The training and testing results showed that the formed backpropagation was able to identify and differentiate the detection of bioethanol according to the given inputs with a success rate of 88.86% for detected bioethanol and 94.86% for undetected bioethanol..

A. G. Pradana, “Performance of a 2012 Honda Supra X 125 Motorcycle Running on Biopremium Fuel Made from Discarded Biscuits by PT. UBM Waru Sidoarjo,” pp. 1–23, 2016.

B. Hahn-Hägerdal, M. Galbe, M. F. Gorwa-Grauslund, G. Lidén, and G. Zacchi, “Bio-ethanol - the fuel of tomorrow from the residues of today,” Trends Biotechnol., vol. 24, no. 12, pp. 549–556, 2006, doi: 10.1016/j.tibtech.2006.10.004.

Z. I. Sholeq and I. W. Susila, “Analysis of Engine Performance and Exhaust Emissions of a Motorcycle Fuelled with a Blend of Bioethanol from Sugarcane Bagasse and Premium Gasoline,” J. Tek. Mesin, vol. 07, no. 03, pp. 121–126, 2019.

K. A. Gray, L. Zhao, and M. Emptage, “Bioethanol,” Curr. Opin. Chem. Biol., vol. 10, no. 2, pp. 141–146, 2006, doi: 10.1016/j.cbpa.2006.02.035.

M. A. M. Putra, “Analysis of E100 Bioethanol Fuel from Rice Washing Water Waste on the Performance of a 4-Stroke Automatic Motorcycle Engine.,” Semin. Nas. Rekayasa Teknol. …, vol. 01, pp. 28–33, 2021, [Online]. Available:

G. Jackson de Moraes Rocha, C. Martin, I. B. Soares, A. M. Souto Maior, H. M. Baudel, and C. A. Moraes de Abreu, “Dilute mixed-acid pretreatment of sugarcane bagasse for ethanol production,” Biomass and Bioenergy, vol. 35, no. 1, pp. 663–670, 2011, doi: 10.1016/j.biombioe.2010.10.018.

S. D. Nugraheni and M. Mastur, “Bioprocess Improvement for Enhanching Bioethanol Production of Sugarcane Molase,” Perspektif, vol. 16, no. 2, p. 69, 2017, doi: 10.21082/psp.v16n2.2017.69-79.

D. Khatiwada, B. K. Venkata, S. Silveira, and F. X. Johnson, “Energy and GHG balances of ethanol production from cane molasses in Indonesia,” Appl. Energy, vol. 164, pp. 756–768, 2016, doi: 10.1016/j.apenergy.2015.11.032.

W. Adriantono, T. Setiawan, and B. Ariwibowo, “The Influence of Adding ECO Racing to Pertalite Fuel and Engine Speed Variation on the Exhaust Gas Emission Levels of a Four-Cylinder Engine,” J. Vocat. Educ. Automot. Technol., vol. 2, no. 2, pp. 43–50, 2020.

M. T. Fermentasi-ekstraktif, F. A. Rosyadi, K. P. Prasavitri, and T. Widjaja, “Optimization of Ethanol Production Process from Molasses.,” vol. 3, no. 2, pp. 2–3, 2013.

A. K. Wardani and F. N. Eka Pertiwi, “Production of Ethanol from Sugarcane Molasses by Flocculating Saccharomyces cerevisiae. (NRRL – Y 265),” agriTECH, vol. 33, no. 2, pp. 131–139, 2013, doi: 10.22146/agritech.9810.

R. Nurjanah et al., “THE VARIATIONS OF BIOETHANOL PRODUCTION FROM BAGASSE: A REVIEW,” J. Kinet., vol. 12, no. 02, pp. 64–67, 2021, [Online].Available:

I. G. Wiratmaja and E. Elisa, “A Study on the Opportunity to Utilize Bioethanol as the Primary Fuel for Future Vehicles in Indonesia.,” J. Pendidik. Tek. Mesin Undiksha, vol. 8, no. 1, pp. 1–8, 2020, doi: 10.23887/jptm.v8i1.27298.

J. S. Purba and J. F. H. Saragi, “Production of Bioethanol from Sugarcane.,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 410–416, 2021, doi: 10.24176/simet.v11i2.5349.

A. F. Permata Cika, Y. Uztamila, S. Effendy A, A. Syarif, and I. Hajar, “The Influence of Fermentation pH and Agitation Speed on Molasses Fermentation for Bioethanol Production,” J. Pendidik. dan Teknol. Indones., vol. 2, no. 1, pp. 561–567, 2022, doi: 10.52436/1.jpti.107.

A. Rochani, S. Yuniningsih, and Z. Ma’sum, “The Influence of Sugar Concentration in Molasses Solution on Ethanol Content in the Fermentation Process,” J. Reka Buana, vol. 1, no. 1, pp. 43–48, 2015.

P. WIBOWO and D. A. Prasetya, “Design and Build a Multi-Channel Data Logger Connected to the Internet of Things (IoT) as a Temperature Measurement Device with Thermocouple Sensors,” Emit. J. Tek. Elektro, vol. 21, no. 2, pp. 87–94, 2021, doi: 10.23917/emitor.v21i2.13773.

S. S. Mukrimaa et al., “Alcohol Detection System Based on MQ-3 Sensor and Internet of Things (IoT),” vol. 6, no. August, p. 128, 2016.

A. A. Rosa, B. A. Simon, and K. S. Lieanto, “Portable Air Pollution Detection System Using MQ-7 and MQ-135 Sensors.,” Ultim. Comput. J. Sist. Komput., vol. 12, no. 1, pp. 23–28, 2020, doi: 10.31937/sk.v12i1.1611.

J. Amrutha and A. S. Remya Ajai, “Performance analysis of backpropagation algorithm of artificial neural networks in verilog,” 2018 3rd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. RTEICT 2018 - Proc., pp. 1547–1550, 2018, doi: 10.1109/RTEICT42901.2018.9012614.

H. R. Maier and G. C. Dandy, “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications,” Environ. Model. Softw., vol. 15, no. 1, pp. 101–124, 2000, doi: 10.1016/S1364-8152(99)00007-9.

K. Fitryadi and S. Sutikno, “Blood Type Identification Using Perceptron Artificial Neural Network,” J. Masy. Inform., vol. 7, no. 1, pp. 1–10, 2017, doi: 10.14710/jmasif.7.1.10794.

N. P. Sakinah, I. Cholissodin, and A. W. Widodo, “Newspaper Demand Prediction Using Backpropagation Artificial Neural Network Method.,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 7, pp. 2612–2618, 2018.

W. Maharani, “Data Classification Using JST Backpropagation with Adaptive Learning Rate.,” J. Fis. Unand, vol. 8, no. 2, pp. 46–58, 2020, [Online]. Available:

J. Yu, S. M. Sharpe, A. W. Schumann, and N. S. Boyd, “Detection of broadleaf weeds growing in turfgrass with convolutional neural networks,” Pest Manag. Sci., vol. 75, no. 8, pp. 2211–2218, 2019, doi: 10.1002/ps.5349.

A. Sudarsono, “Artificial Neural Network for Predicting Population Growth Rate Using Backpropagation Method (Case Study in Bengkulu City).,” J. Media Infotama, vol. 12, no. 1, pp. 61–69, 2016, doi: 10.37676/jmi.v12i1.273.

M. S. Wibawa, “The Influence of Activation Function, Optimization, and Number of Epochs on the Performance of Artificial Neural Networks.,” J. Sist. dan Inform., vol. 11, no. December, pp. 167–174, 2017, doi: 10.13140/RG.2.2.21139.94241.

E. P. Cynthia and E. Ismanto, “Backpropagation Algorithm Artificial Neural Network in Predicting the Availability of Food Commodities in Riau Province,” RABIT J. Teknol. dan Sist. Inf. Univrab, vol. 2, no. 2, pp. 83–98, 2017.

M. Thoriq, “Production Demand Forecasting Using Artificial Neural Networks with Backpropagation Algorithm.,” J. Inf. dan Teknol., vol. 1, no. 2, pp. 27–32, 2022, doi: 10.37034/jidt.v4i1.178.

R. Susanti, A. Hidayat, N. Alfitri, and M. Ilhamdi, “Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network,” vol. 7, no. September, pp. 659–664, 2023.

Julpan, E. B. Nababan, and M. Zarlis, “Bipolar Dalam Algoritma Backpropagation Pada,” J. Teknovasi, vol. 02, pp. 103–116, 2015.

R. Susanti, R. Nofendra, M. Syaiful, and M. Ilhamdi, “The Use of Artificial Neural Networks in Agricultural Plants The Use of Artificial Neural Networks in Agricultural,” vol. 2, pp. 62–68, 2022.

R. Susanti, Z. Ressy Aidha, M. Yuliza, and S. Yondri, “Artificial Neural Network Application for Aroma Monitoring on The Coffe Aorma Profile and Intensity,” 2021.