2024
Nasrabadi, Negar; Wicaksono, Hendro; Valilai, Omid Fatahi
Shopping marketplace analysis based on customer insights using social media analytics Journal Article
In: MethodsX, vol. 13, pp. 102868, 2024.
Abstract | Links | BibTeX | Tags: data science, sentiment analysis, text analytics
@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}
}
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.
•
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.
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Juwono, Alamsyah Muhammad; Crysdian, Cahyo; Muslim, Muhammad Aziz; Wicaksono, Hendro
Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration Journal Article
In: IEEE Access, vol. 12, 2024.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, machine learning
@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}
}
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.
Yuniaristanto,; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Exploring the determinants of intention to purchase electric motorcycles: the role of national culture in the UTAUT Journal Article
In: Transportation research part F: traffic psychology and behaviour, vol. 100, pp. 475–492, 2024.
Links | BibTeX | Tags: data science, sustainability, technology adoption
@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}
}
2023
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Crysdian, Cahyo; Basid, Puspa Miladin NSA; Hariyadi, Mokhamad Amin; Tazi, Imam; Arif, Yunifa Miftachul; Wicaksono, Hendro
SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks Proceedings Article
In: 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), pp. 96–101, IEEE 2023.
Abstract | Links | BibTeX | Tags: data science, machine learning
@inproceedings{almais2023sassd,
title = {SASSD: A Smart Assessment System For Sector Damage Post-Natural Disaster Using Artificial Neural Networks},
author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Cahyo Crysdian and Puspa Miladin NSA Basid and Mokhamad Amin Hariyadi and Imam Tazi and Yunifa Miftachul Arif and Hendro Wicaksono},
url = {https://ieeexplore.ieee.org/abstract/document/10249540},
doi = {https://doi.org/10.1109/COSITE60233.2023.10249540},
year = {2023},
date = {2023-08-02},
urldate = {2023-08-02},
booktitle = {2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE)},
pages = {96–101},
organization = {IEEE},
abstract = {Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.
},
keywords = {data science, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model’s ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.
Almais, Agung Teguh Wibowo; Susilo, Adi; Naba, Agus; Sarosa, Moechammad; Crysdian, Cahyo; Tazi, Imam; Hariyadi, Mokhamad Amin; Muslim, Muhammad Aziz; Basid, Puspa Miladin Nuraida Safitri Abdul; Arif, Yunifa Miftachul; others,
Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters Journal Article
In: IEEE Access, vol. 11, pp. 74590-74601, 2023.
Abstract | Links | BibTeX | Tags: data science, machine learning
@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}
}
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.
Sarafanov, Egor; Valilai, Omid Fatahi; Wicaksono, Hendro
Causal Analysis of Artificial Intelligence Adoption in Project Management Proceedings Article
In: Intelligent Systems Conference, pp. 245–264, Springer Nature Switzerland Cham 2023.
BibTeX | Tags: artificial intelligence, causal inference, data science, project management, technology adoption
@inproceedings{sarafanov2023causal,
title = {Causal Analysis of Artificial Intelligence Adoption in Project Management},
author = {Egor Sarafanov and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Intelligent Systems Conference},
pages = {245–264},
organization = {Springer Nature Switzerland Cham},
keywords = {artificial intelligence, causal inference, data science, project management, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
Yuniaristanto, Yuniaristanto; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro
Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis Proceedings Article
In: E3S Web of Conferences, pp. 02035, EDP Sciences 2023.
BibTeX | Tags: data science, sustainability, technology adoption
@inproceedings{yuniaristanto2023factors,
title = {Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis},
author = {Yuniaristanto Yuniaristanto and Wahyudi Sutopo and Muhammad Hisjam and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {E3S Web of Conferences},
volume = {465},
pages = {02035},
organization = {EDP Sciences},
keywords = {data science, sustainability, technology adoption},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Wicaksono, Hendro
Production Planning and Control: Theories and Best Practices Journal Article
In: 2022.
BibTeX | Tags: data science, manufacturing
@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}
}
2021
Wicaksono, Hendro
Data Driven Manufacturing: Challenges and Opportunities Presentation
01.01.2021.
BibTeX | Tags: data science, machine learning, manufacturing
@misc{wicaksono2021data,
title = {Data Driven Manufacturing: Challenges and Opportunities},
author = {Hendro Wicaksono},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
publisher = {OSF},
keywords = {data science, machine learning, manufacturing},
pubstate = {published},
tppubtype = {presentation}
}
Farooq, Yousuf; Wicaksono, Hendro
Advancing on the analysis of causes and consequences of green skepticism Journal Article
In: Journal of Cleaner Production, vol. 320, pp. 128927, 2021.
BibTeX | Tags: data science, sustainability
@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}
}
Ahmadi, Elham; Wicaksono, Hendro; Valilai, O Fatahi
Extending the last mile delivery routing problem for enhancing sustainability by drones using a sentiment analysis approach Proceedings Article
In: 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 207–212, IEEE 2021.
BibTeX | Tags: data science, logistics, operation research
@inproceedings{ahmadi2021extending,
title = {Extending the last mile delivery routing problem for enhancing sustainability by drones using a sentiment analysis approach},
author = {Elham Ahmadi and Hendro Wicaksono and O Fatahi Valilai},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {207–212},
organization = {IEEE},
keywords = {data science, logistics, operation research},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Wicaksono, Hendro; Ni, Tianran
An Automated Information System for Medium to Short-Term Manpower Capacity Planning in Make-To-Order Manufacturing Journal Article
In: Procedia Manufacturing, vol. 52, pp. 319–324, 2020.
BibTeX | Tags: data science, manufacturing
@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}
}
Wicaksono, Hendro
Data analytics in supply chain management Journal Article
In: 2020.
BibTeX | Tags: data science, machine learning, supply chain management
@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}
}