Publication

@article{nokey,

title = {Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study},

author = {Tamas Fekete and Girum Mengistu and Hendro Wicaksono },

doi = {https://doi.org/10.1016/j.scs.2025.106240},

year  = {2025},

date = {2025-03-15},

journal = {Sustainable Cities and Society},

volume = {122},

pages = {106240},

abstract = {The importance of developing sustainable urban transportation systems to protect the environment is increasingly recognized worldwide, particularly within the European Union. In the era of digitalization, data-driven approaches are crucial for informed decision-making. This study introduces a methodology leveraging causal artificial intelligence (causal AI) to uncover cause-and-effect relationships in urban transport data. Unlike traditional methods relying on correlations, causal AI identifies the true drivers of transport dynamics. A case study using MOL Bubi bike-sharing data from Budapest demonstrates how the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm revealed complex temporal dependencies within the data, with temperature emerging as the strongest causal factor positively influencing bike usage. Additionally, the reopening of the Chain Bridge led to a 10.7% increase in bike trips, as quantified by Causal Impact analysis. This case study can be extended to more complex scenarios with unpredictable outcomes. The insights gained provide policymakers with a deeper understanding, enabling them to design policies fostering sustainable urban mobility. These results showcase the potential of causal AI to guide policies that enhance sustainable urban mobility.},

keywords = {artificial intelligence, causal AI, machine learning, sustainability, transportation},

pubstate = {published},

tppubtype = {article}

}

@article{Shah2024,

title = {Leveraging Machine Learning for Power Consumption Prediction of Multi-Step Production Processes in Dynamic Electricity Price Environment},

author = {Muhammad Abdullah Shah and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2212827124012332},

doi = {https://doi.org/10.1016/j.procir.2024.10.080},

year  = {2024},

date = {2024-11-27},

urldate = {2024-11-27},

journal = {Procedia CIRP},

volume = {130},

pages = {226-231},

abstract = {Rising energy costs drive a compelling demand for energy-efficient manufacturing across sectors, paralleled by increasing consumer preferences for eco-friendly products. To remain competitive, companies are actively enhancing their energy efficiency. Integrating dynamic pricing in manufacturing, aimed at optimizing renewable energy use, requires strategic adjustments in production planning for sustainability. This research highlights the importance of incorporating dynamic pricing into production planning, emphasizing the need to shift processes to time slots when the energy prices are low or optimal. This study focuses on predicting the power consumption of multi-step CNC machine operations within a production cycle. Utilizing advanced Machine Learning (ML), including neural networks, statistical, and additive models, this research found unique time series characteristics influencing model performance across production steps. A practical use case within a German manufacturing Small and Medium Enterprises (SME) demonstrates how prediction results can optimize production processes in a dynamic pricing environment, providing a blueprint for diverse machinery forecasting models. This research’s insights extend to any industry managing production schedules for multiple machines with various steps in a process cycle. Industries with high energy consumption will benefit significantly through aligning operational efficiency with environmental sustainability goals.},

keywords = {energy management, machine learning, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration},

author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Alamsyah Muhammad Juwono and Cahyo Crysdian and Muhammad Aziz Muslim and Hendro Wicaksono},

url = {https://ieeexplore.ieee.org/abstract/document/10697160},

doi = {https://doi.org/10.1109/ACCESS.2024.3469637},

year  = {2024},

date = {2024-09-27},

urldate = {2024-09-27},

journal = {IEEE Access},

volume = {12},

abstract = {Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.},

keywords = {artificial intelligence, data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2024artificial,

title = {Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes},

author = {Hendro Wicaksono and Martin Trat and Atit Bashyal and Tina Boroukhian and Mine Felder and Mischa Ahrens and Janek Bender and Sebastian Groß and Daniel Steiner and Christoph July and others},

url = {https://link.springer.com/article/10.1007/s00170-024-13372-7},

doi = {https://doi.org/10.1007/s00170-024-13372-7},

year  = {2024},

date = {2024-03-22},

urldate = {2024-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

pages = {1–25},

publisher = {Springer London},

abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.



},

keywords = {artificial intelligence, demand response system, energy management, machine learning, manufacturing, ontologies, reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@article{thapaliya2024power,

title = {Power Consumption and Processing Time Estimation of CNC Machines Using Explainable Artificial Intelligence (XAI)},

author = {Suman Thapaliya and Omid Fatahi Valilai and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050924000863},

doi = {https://doi.org/10.1016/j.procs.2024.01.086},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

journal = {Procedia Computer Science},

volume = {232},

pages = {861–870},

publisher = {Elsevier},

abstract = {Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most.

},

keywords = {energy management, explainable AI, machine learning, manufacturing},

pubstate = {published},

tppubtype = {article}

}

@article{almais2023principal,

title = {Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters},

author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Cahyo Crysdian and Imam Tazi and Mokhamad Amin Hariyadi and Muhammad Aziz Muslim and Puspa Miladin Nuraida Safitri Abdul Basid and Yunifa Miftachul Arif and others},

url = {https://ieeexplore.ieee.org/abstract/document/10123944},

doi = {https://doi.org/10.1109/ACCESS.2023.3275852},

year  = {2023},

date = {2023-05-12},

urldate = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {74590-74601},

publisher = {IEEE},

abstract = {Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $text{n} < 0$ ), cluster 2 ( $0le text{n} < 2$ ), and cluster 3 ( $text{n}ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0le text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $text{n}ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing.},

keywords = {data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2021accelerating,

title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence},

author = {Hendro Wicaksono},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

publisher = {OSF Preprints},

keywords = {artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{fritz2021automatic,

title = {Automatic information extraction from text-based requirements},

author = {Simon Fritz and Vethiga Srikanthan and Ryan Arbai and Chenwei Sun and Jivka Ovtcharova and Hendro Wicaksono},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Int. J. Knowl. Eng},

volume = {7},

number = {1},

pages = {8–13},

keywords = {machine learning, natural language processing},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2020data,

title = {Data analytics in supply chain management},

author = {Hendro Wicaksono},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

publisher = {OSF},

keywords = {data science, machine learning, supply chain management},

pubstate = {published},

tppubtype = {article}

}