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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Zhang, Huayi Feng, Hao Rundensteiner, Elke Worcester Polytechnic Institute Data Science Program Worcester United States Tsinghua University Vanke School of Public Health Beijing China ByteDance San Jose United States North Carolina A&T State University College of Ag&Environ Sciences Greensboro United States Worcester Polytechnic Institute Computer Science/Data Science Program Worcester United States

ISBN: (纸本)9798350324457

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. © 2023 IEEE.

关键词： foodborne illness learning with noisy labels semi-supervised learning social media text classification

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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Internet of Things for Smart Furniture: A Systematic Review 11

Internet of Things for Smart Furniture: A Systematic Review

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Kurniawan, Bambang Kartono Pramono, Andi Purnomo, Agung Abdulah, Muhamad Ezran Bin Zainal Mohamed, Mustafa School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Universiti Malaysia Kelantan Faculty of Creative Technology and Heritage Department of Design and Creative Technology Kelantan Bachok16300 Malaysia School of Technology of the Arts Republic Polytechnic Singapore

ISBN: (纸本)9798350389654

The research entitled 'Internet of Things for Smart Furniture: A Systematic Review' explores integrating IoT technology into smart furniture to improve functionality and user experience. By synthesizing existing literature, this research aims to discover how the Internet of Things (IoT) is leveraged to meet specific user needs with smart furniture and what hardware and sensor technologies are proposed for smart furniture design, considering factors such as scalability and need for users. Current research trends in securing user communications and data in the context of IoT-enabled smart furniture. This research involved a literature search which included journal articles and reviews from the Web of science (WOS) and the Scopus database (core collection) from 2014 to 2023, and the final results were 9 documents that would be reviewed. Several impactful means must be used to meet specific user needs, such as health monitoring, enhanced security, enhanced convenience and mobility, integration with smart home systems, and personalization and adaptability. Drawing insights from IoT data quality, machine learning applications in IoT, and smart furniture design for elderly care, this research provides a comprehensive picture of the current IoT-enabled smart furniture landscape. This research can explore existing IoT solutions for smart furniture, with a focus on cutting-edge technologies and design principles that can shape the future of this domain. © 2024 IEEE.

关键词： internet of things literature review scopus smart furniture web of science

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Trends in Scientific Research in Smart Furniture: A Bibliometric Analysis in Decade from 2014-2023 11

Trends in Scientific Research in Smart Furniture: A Bibliome...

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11th International Conference on ICT for Smart Society, ICISS 2024

ISBN: (纸本)9798350389654

Smart furniture has emerged as a significant area of interest in scientific research over the past decade. This bibliometric analysis aims to explore the trends in scientific research related to smart furniture from 2014 to 2023. By examining a range of scholarly articles, this study delves into the evolution of smart furniture technologies, applications, and implications. The research reveals a growing interest in integrating intelligent systems into furniture design, enabling network connectivity and user-controlled operations. Despite the growing number of studies, the research landscape remains fragmented, necessitating a comprehensive bibliometric analysis to consolidate existing research and identify trends. Data selection from the Scopus database yielded a total of 97 documents from 69 sources, with average citations per doc is 8.041. On the corresponding author's countries, China is the highest country. Moreover, terms like 'smart furniture' that expand in size and extend to the right of the chart indicate a growing emphasis in recent literature, signaling potential areas of increased research activity. Smart furniture is characterized as furniture that is connected to a network and equipped with intelligent technologies that make use of user data and energy sources. The study underscores the importance of addressing issues such as ease of use, privacy, security, and affordability in implementing smart furniture solutions. This study provides valuable insights into the current state and future prospects of smart furniture research, guiding further investigation and advancement in this rapidly evolving sector. © 2024 IEEE.

关键词： bibliometric Rstudio scopus smart furniture VOSviewer

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Privacy Preserving Electronic Health Record with Consortium Blockchain 6

Privacy Preserving Electronic Health Record with Consortium ...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Gautama, Adri Rochim, Adian Fatchur Bayuaji, Luhur Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

ISBN: (纸本)9798350399615

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance. © 2022 IEEE.

关键词： Consortium Blockchain e-health EHR Privacy

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning

Features Reduction with PCA Technique for Malware Detection ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Teddy Mantoro Agung Ginanjar Media Anugerah Ayu Umar Aditiawarman Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688.

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Sentiment Analysis With N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language

Sentiment Analysis With N-Gram Preprocessing for Online-Shop...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Jelita Asian Oktavianus Tegar N.K Putra Media Anugerah Ayu Teddy Mantoro Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection

Enhancing Security Information and Event Management (SIEM) b...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Media Anugerah Ayu Dimas Erlangga Teddy Mantoro Dini Handayani Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University - Sukabumi Indonesia

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it’s practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953.

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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