Publication

@article{graha2026scalable,

title = {A Scalable V2X Architecture for Predictive Maintenance Utilizing Federated Learning in Smart Mobility},

author = {Ega Rudy Graha and Parth Jitendra Vaya and Behshad Azizian and Hendro Wicaksono},

url = {https://doi.org/10.1016/j.procs.2026.02.381},

doi = {10.1016/j.procs.2026.02.381},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Procedia Computer Science},

volume = {277},

pages = {3453–3462},

publisher = {Elsevier},

keywords = {e-mobility, predictive maintenance, transportation},

pubstate = {published},

tppubtype = {article}

}

@article{yuniaristanto2024estimating,

title = {Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs},

author = {Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S0301421524003653},

doi = {https://doi.org/10.1016/j.enpol.2024.114345},

year  = {2024},

date = {2024-12-01},

urldate = {2024-01-01},

journal = {Energy Policy},

volume = {195},

pages = {114345},

publisher = {Elsevier},

abstract = {Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.



},

keywords = {e-mobility, sustainability, system dynamics, technology adoption, transportation},

pubstate = {published},

tppubtype = {article}

}

@article{rahmawati2024investment,

title = {Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia},

author = {Tasya Santi Rahmawati and Wahyudi Sutopo and Hendro Wicaksono},

url = {https://www.mdpi.com/2032-6653/15/8/334},

doi = {https://doi.org/10.3390/wevj15080334},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {World Electric Vehicle Journal},

volume = {15},

number = {8},

pages = {334},

publisher = {MDPI AG},

abstract = {The issue of carbon emissions can be addressed through environmentally friendly technological innovations, which contribute to the journey towards achieving net-zero emissions (NZE). The electrification of transportation by converting internal combustion engine (ICE) motorcycles to converted electric motorcycles (CEM) directly reduces the number of pollution sources from fossil-powered motors. In Indonesia, numerous government regulations support the commercialization of the CEM system, including the requirement for conversion workshops to be formal entities in the CEM process. Every CEM must pass a test to ensure its safety and suitability. Currently, the CEM testing process is conducted at only one location, making it inefficient and inaccessible. Therefore, most conversion workshops in Indonesia need to take investment steps in procuring CEM-type test tools. This research aims to determine the best alternative from several investment alternatives for CEM-type test tools. In selecting the investment, three criteria are considered: costs, operations, and specifications. By using the investment decision-making model, a hierarchical decision-making model is obtained, which is then processed using the analytical hierarchy process (AHP) and the technique for order of preference by similarity to the ideal solution (TOPSIS). Criteria are weighted to establish a priority order. The final step involves ranking the alternatives and selecting Investment 2 (INV2) as the best investment tool with a relative closeness value of 0.6279. Investment 2 has the shortest time process (40 min), the lowest electricity requirement, and the smallest dimensions. This research aims to provide recommendations for the best investment alternatives that can be purchased by the conversion workshops.

},

keywords = {e-mobility, multi criteria decision making, technology adoption, TOPSIS, transportation},

pubstate = {published},

tppubtype = {article}

}