Article Per Year (5 Year)
p-Index From 2021 - 2026
1.99
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Rekayasa Mesin Enthalpy : Jurnal Ilmiah Mahasiswa Teknik Mesin Jurnal Rekayasa Mesin Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Mesin Sinergi Dedikasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Medika: Medika Advance Sustainable Science, Engineering and Technology (ASSET)
La Ode Ichlas Syahrullah Yunus
Prodi Teknik Mesin, Universitas Sembilanbelas November Kolaka
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Rekayasa Mesin

 1 
FUEL-INJECTED MOTORCYCLE PERFORMANCE OPTIMIZATION UTILISING PERTALITE-ETHANOL BLENDS AND DEEP NEURAL NETWORK-BASED ECU FOR EFFICIENCY IMPROVEMENT AND EMISSION REDUCTION Yunus, La Ode Ichlas Syahrullah; Putri, Farika Tono; Ismail, Rima Ruktiari
Jurnal Rekayasa Mesin Vol. 16 No. 2 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i2.1946

Abstract

This study aims to optimize the performance of fuel-injected motorcycles through the application of a Deep Neural Network (DNN) in the Electronic Control Unit (ECU) and the use of ethanol-pertalite fuel blends. The ethanol blends used in the study were 0%, 5%, 10%, 15%, and 20%. Fuel consumption tests were conducted using the standard ECE/324 driving cycle, and emission tests were performed according to Euro 4 standards. Tests were conducted on a real track to evaluate fuel consumption performance and exhaust gas emissions. The results indicate that the 15% ethanol blend (E15) provided optimal engine efficiency, while the 20% ethanol blend (E20) resulted in the largest reduction in carbon monoxide (CO) and hydrocarbon (HC) emissions. Furthermore, the DNN model with 50 neurons and a Sigmoid activation function demonstrated the best balance between accuracy (R=0.9868) and generalization (MSE=0.3843) in optimizing ignition timing and injection timing. In conclusion, the ethanol blends and the application of DNN in the ECU have proven effective in enhancing fuel efficiency and reducing exhaust emissions, supporting the development of more sustainable transportation technologies.
Co-Authors Ahmad Zaeni, Ahmad Ahmad, La Ode Alwahab Alwahab Ashari, Dedy Darman Darman Eko, La Fitra, Ramad Arya Golok Jaya, La Ode Muhammad Habriani, Habriani Haluru, Asfa Husaeni, Yusuf Ahmad Ibrahim, Akhmad Fadli Ismail, Rima Ruktiari Istianandar, Muhammad Iqbal Sya'bani Jandipo, Rajab Kasim, Ma'ruf Laode Abdul Gamsir mubarak, azhar aras Mutmainnah Muchtar Putri, Farika Tono Ramadhan, Zulfan Robby Sudarman Saflia, Ismiliani Salim, Mamat Maulana Sarimuddin, Sarimuddin Syah, Gahrudin Wa Ode Novi Haryanti Yocing, Panci Yuvita Satriani Djuli Zainab, Wa Ode safira