Publication

@article{bashyal2025open,

title = {Open innovation in industrial demand response: A computing continuum approach to overcoming technological barriers},

author = {Atit Bashyal and Pakin Veerachanchi and Tina Boroukhian and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.joitmc.2025.100678},

year  = {2025},

date = {2025-11-06},

urldate = {2025-11-06},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

issue = {4},

pages = {100678},

publisher = {Elsevier},

abstract = {The rise of Industrial IoT (IIoT) alongside cloud, edge, and fog computing is transforming industrial operations and enabling new Demand Response (DR) opportunities in the Smart Grid. DR allows end-users to adjust energy consumption in response to external signals. Still, its adoption in industry is limited by challenges such as communication, security, interoperability, and computing constraints, especially in environments requiring real-time decision-making. This paper explores how the Computing Continuum can help overcome these barriers and support scalable, flexible, and responsive Industrial Demand Response (IDR) systems. We propose a reference architecture that integrates key IIoT and energy management trends to support real-time processing and system interoperability. A central focus is the role of aggregators and the importance of open innovation and cloud service providers in enabling adaptive and collaborative IDR solutions. Our findings offer a roadmap for aligning technological advancements with IDR needs, contributing to more effective and sustainable energy management in Industry 4.0 settings.},

keywords = {artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, ontologies, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{boroukhian2025integrating,

title = {Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems},

author = {Tina Boroukhian and Kritkorn Supyen and Jhealyn Bautista Samson and Atit Bashyal and Hendro Wicaksono},

doi = {https://doi.org/10.1007/s00170-025-16548-x},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

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

volume = {140},

number = {9},

pages = {4679–4711},

publisher = {Springer London},

abstract = {The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.},

keywords = {data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web},

pubstate = {published},

tppubtype = {article}

}

@article{vijaya2025esgont,

title = {ESGOnt: An ontology-based framework for Enhancing Environmental, Social, and Governance (ESG) assessments and aligning with Sustainable Development Goals (SDG)},

author = {Annas Vijaya and Faris Dzaudan Qadri and Linda Salma Angreani and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.resenv.2025.100262},

year  = {2025},

date = {2025-08-25},

urldate = {2025-08-25},

journal = {Resources, Environment and Sustainability},

volume = {22},

pages = {100262},

publisher = {Elsevier},

abstract = {This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.},

keywords = {data management, data science, ESG, interoperability, ontologies, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{bashyal2025multi,

title = {Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems},

author = {Atit Bashyal and Tina Boroukhian and Pakin Veerachanchai and Myanganbayar Naransukh and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.apenergy.2025.125990},

year  = {2025},

date = {2025-08-15},

urldate = {2025-01-01},

journal = {Applied Energy},

volume = {392},

pages = {125990},

publisher = {Elsevier},

abstract = {Energy-centric decarbonization of heavy industries, such as steel and cement, necessitates their participation in integrating Renewable Energy Sources (RES) and effective Demand Response (DR) programs. This situation has created the opportunities to research control algorithms in diverse DR scenarios. Further, the industrial sector’s unique challenges, including the diversity of operations and the need for uninterrupted production, bring unique challenges in designing and implementing control algorithms. Reinforcement learning (RL) methods are practical solutions to the unique challenges faced by the industrial sector. Nevertheless, research in RL for industrial demand response has not yet achieved the level of standardization seen in other areas of RL research, hindering broader progress. To propel the research progress, we propose a multi-agent reinforcement learning (MARL)-based energy management system designed to optimize energy consumption in energy-intensive industrial settings by leveraging dynamic pricing DR schemes. The study highlights the creation of a MARL environment and addresses these challenges by designing a general framework that allows researchers to replicate and implement MARL environments for industrial sectors. The proposed framework incorporates a Partially Observable Markov Decision Process (POMDP) to model energy consumption and production processes while introducing buffer storage constraints and a flexible reward function that balances production efficiency and cost reduction. The paper evaluates the framework through experimental validation within a steel powder manufacturing facility. The experimental results validate our framework and also demonstrate the effectiveness of the MARL-based energy management system.},

keywords = {artificial intelligence, data science, deep learning, demand response system, energy management, green energy, machine learning, manufacturing, operation research, reinforcement learning, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{gupta2025comparative,

title = {A comparative assessment of causal machine learning and traditional methods for enhancing supply chain resiliency and efficiency in the automotive industry},

author = {Ishansh Gupta and Adriana Martinez and Sergio Correa and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.sca.2025.100116},

year  = {2025},

date = {2025-06-01},

urldate = {2025-06-01},

journal = {Supply Chain Analytics},

volume = {10},

pages = {100116},

publisher = {Elsevier},

abstract = {Efficient supplier escalation is crucial for maintaining smooth operational supply chains in the automotive industry, as disruptions can lead to significant production delays and financial losses. Many companies still rely on traditional escalation methods, which may lack the precision and adaptability offered by modern technologies. This study presents a comparative analysis of decision-making strategies for supplier escalation, evaluating causal machine learning (CML), traditional machine learning (ML), and current escalation practices in a leading German automotive company. The study employs an explanatory sequential mixed method, integrating the Analytical Hierarchy Process (AHP) with in-depth interviews with 25 industry experts. These methods are assessed based on several performance metrics: accuracy, business impact, explanation capability, human bias, stress test, and time-to-recover. Findings reveal that CML outperforms traditional ML and existing approaches, offering superior risk prediction, interpretability, and decision-making support Additionally, the research explores the internal acceptance of these technologies through the Technology Acceptance Model (TAM). The results highlight the transformative potential of CML in enhancing supply chain resilience and efficiency. By bridging the gap between predictive analytics and explainable AI, this research offers valuable guidance for firms seeking to optimize supplier management using advanced analytics.},

keywords = {artificial intelligence, causal AI, causal inference, data science, decision support systems, industry 4.0, industry 5.0, machine learning, multi criteria decision making, resillience, supply chain management, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@article{priyandari2025desain,

title = {Desain Dashboard untuk Penyajian Informasi Publik Rumah Sakit berbasis Indikator Kinerja dan Evaluasi Dashboard Menggunakan Short-User Experience Questionnaire.},

author = {Yusuf Priyandari and Rio Detri Setyoko and Eko Pujiyanto and Hendro Wicaksono},

doi = {10.62411/tc.v24i2.12804},

year  = {2025},

date = {2025-05-01},

urldate = {2025-01-01},

journal = {Techno. com},

volume = {24},

number = {2},

abstract = {Law Number 14 of 2008 on Public Information Disclosure mandates that every public institution, including government hospitals, provide access to public information. Unfortunately, hospital performance information is only available in PDF and spreadsheet formats through internal hospital websites and certain provincial-level websites, and it is not well-organized. Therefore, this study proposes a standardized dashboard design for all hospitals in a province. The design process consists of four stages: system diagnosis, needs analysis, dashboard development, and evaluation of the design. The design methodology follows the dashboard development approach using Power BI, with the Short-User Experience Questionnaire (UEQ-S) specifically employed in the evaluation phase to assess the design outcomes. The resulting dashboard is capable of visualizing six groups of hospital performance indicators as public information for three user categories: university academics, executives/legislative staff, and independent researchers/media. Based on user experience responses, the dashboard successfully fulfills its purpose as a public information presentation tool, with users expressing satisfaction and comfort while using the dashboard. Overall, users have a very positive impression of the dashboard. UEQ-S results indicate that the dashboard's pragmatic quality is rated as good, its hedonic quality is rated as excellent, and its overall rating is excellent.},

keywords = {data science, decision support systems, healthcare},

pubstate = {published},

tppubtype = {article}

}

@article{almashaleh2025framework,

title = {A framework for social media analytics in textile business circularity for effective digital marketing},

author = {Omaymah Almashaleh and Hendro Wicaksono and Omid Fatahi Valilai},

doi = {https://doi.org/10.1016/j.joitmc.2025.100544},

year  = {2025},

date = {2025-05-01},

urldate = {2025-01-01},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

issue = {2},

pages = {100544},

publisher = {Elsevier},

abstract = {In the contemporary era of digital transformation, organizations are increasingly aligning their operations with sustainability objectives, particularly within the framework of circular economy (CE) principles in production and consumption systems. While the concept of circularity has been extensively explored through theoretical research, a notable gap remains in empirical studies that analyze user-generated content related to circularity in the textile industry. This study aims to bridge the gap by proposing a novel Instagramⓒ analytics framework that seamlessly integrates content and network analyses. A mixed-method approach is adopted, merging qualitative insights derived from unstructured data with quantitative techniques. To analyze content, unsupervised machine learning methods, including topic modeling and sentiment analysis, are employed. In parallel, Social Network Analysis (SNA) and hashtag co-occurrence analysis are applied to investigate the dynamics within the network. The findings demonstrate a significant level of interest and engagement in discussions surrounding Textile Circularity (TC). Moreover, consumer responses to sustainability initiatives show considerable variation, underscoring the necessity of strategies that foster meaningful interactions. Notably, content emphasizing positive sentiments and tangible benefits, such as cost savings and environmental improvements, consistently achieves higher engagement levels. This paper contributes to the field by integrating social media data with advanced data analytics techniques. Together, these approaches offer an unparalleled opportunity to investigate customer drivers within the context of TC. Additionally, the study presents a comprehensive analytical model and delivers actionable insights. These findings hold the potential to refine digital marketing strategies and enhance customer engagement, particularly by deepening the understanding of factors that motivate consumers to TC.},

keywords = {circular economy, data science, decision support systems, sustainability},

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  = {2025},

date = {2025-05-01},

urldate = {2025-05-01},

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

volume = {138},

pages = { 247–271},

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, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{ghribi2025comparative,

title = {Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry},

author = {Youssef Ghribi and Ega Rudy Graha and Hendro Wicaksono},

doi = {https://doi.org/10.1016/j.procir.2025.02.209},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Procedia CIRP},

volume = {134},

pages = {849–854},

publisher = {Elsevier},

abstract = {Demand forecasting is a crucial instrument in the business strategy. The medical devices in the healthcare system are further significant as critical roles. Multiple businesses rely on traditional forecasting techniques due to their simplicity and understandable algorithm’s easy-to-use nature characteristics. The research conducted for each model analyzes how traditional statistical, Machine Learning (ML), and Deep Learning (DL) models can be used to make demand forecasting more accurate and valuable in the medical device industry. The work expands beyond prior research to demonstrate the enhanced effectiveness of DL models compared to statistical and ML models across multiple areas. However, research still needs to identify studies that adopt a business-centric perspective on the practical applicability of these models. Research utilizing SARIMAX, Exponential Smoothing, Linear Regression, Average, Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbour Regression (KNR), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution 1D (CONV1D) models to forecast what people demand to order. The data comes from a German medical device manufacturer’s past sales record. We evaluated the model’s performance using the weighted Mean Absolute Percentage Error (wMAPE) method. These showed that DL models needed a lot of knowledge and preprocessing, but they were the most accurate at predicting what would happen. The LSTM model exhibited outstanding performance, achieving an average wMAPE of 0.3102, surpassing all other models. The research results for more sophisticated models surpass traditional statistical models despite limited datasets, recommending that medical device businesses consider investing in advanced demand forecasting models.



  publisher={Elsevier}

}},

keywords = {artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management},

pubstate = {published},

tppubtype = {article}

}

@article{nasrabadi2024shopping,

title = {Shopping marketplace analysis based on customer insights using social media analytics},

author = {Negar Nasrabadi and Hendro Wicaksono and Omid Fatahi Valilai},

url = {https://www.sciencedirect.com/science/article/pii/S2215016124003200},

doi = {https://doi.org/10.1016/j.mex.2024.102868},

year  = {2024},

date = {2024-12-01},

urldate = {2024-01-01},

journal = {MethodsX},

volume = {13},

pages = {102868},

publisher = {Elsevier},

abstract = {Using the recent advances in data analytics, Companies can leverage sentiment analysis to identify trends and areas for improvement of their market strategy and operation planning. This analysis allows them to understand the sentiments expressed by customers and accurately predict customer behavior. Social media platforms have fundamentally altered the field of digital marketing. The findings of this research try to provide:

•

Potential implications for businesses aiming to optimize their product offerings and enhance customer satisfaction within specific cultural contexts.



•

A case study has been designed for understanding customers' perceptions and satisfaction levels toward various shops, including local ethnic stores and big chain stores.



The paper has conducted a literature review around main pillars like application of data analytics on social media, and sales strategies for establishing a marketplace using data analytics. Exploring the utilization of social media and customer feedback, the paper has proposed a conceptual model for creating insights for extraction of shopping experiences and factors used for customer purchasing decision making.},

keywords = {data science, sentiment analysis, text analytics},

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{sutopo2024exploring,

title = {Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT},

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

url = {https://www.sciencedirect.com/science/article/pii/S1369847823002772},

doi = {https://doi.org/10.1016/j.trf.2023.12.012},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Transportation research part F: traffic psychology and behaviour},

volume = {100},

pages = {475–492},

publisher = {Pergamon},

keywords = {data science, sustainability, technology adoption},

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{wicaksono2022production,

title = {Production Planning and Control: Theories and Best Practices},

author = {Hendro Wicaksono},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

publisher = {OSF Preprints},

keywords = {data science, manufacturing},

pubstate = {published},

tppubtype = {article}

}

@article{farooq2021advancing,

title = {Advancing on the analysis of causes and consequences of green skepticism},

author = {Yousuf Farooq and Hendro Wicaksono},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Journal of Cleaner Production},

volume = {320},

pages = {128927},

publisher = {Elsevier},

keywords = {data science, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{wicaksono2020automated,

title = {An Automated Information System for Medium to Short-Term Manpower Capacity Planning in Make-To-Order Manufacturing},

author = {Hendro Wicaksono and Tianran Ni},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Procedia Manufacturing},

volume = {52},

pages = {319–324},

publisher = {Elsevier},

keywords = {data science, manufacturing},

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}

}