2025
Priyandari, Yusuf; Setyoko, Rio Detri; Pujiyanto, Eko; Wicaksono, Hendro
In: Techno. com, vol. 24, no. 2, 2025.
Abstract | Links | BibTeX | Tags: data science, decision support systems, healthcare
@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}
}
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.
Ghribi, Youssef; Graha, Ega Rudy; Wicaksono, Hendro
Comparative Analysis of Statistical and Machine Learning Models for Enhancing Demand Forecasting Accuracy in the Medical Device Industry Journal Article
In: Procedia CIRP, vol. 134, pp. 849–854, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data science, deep learning, demand forecasting, healthcare, machine learning, manufacturing, supply chain management
@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}
}
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}
}
publisher={Elsevier}
}