Developing Ill-defined problem-solving for the context of “South Sumatera”

Arifin, Sujinal; Zulkardi, Zulkardi; Putri, Ratu Ilma Indra; Hartono, Yusuf; Susanti, Eka

doi:10.1088/1742-6596/943/1/012038

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…The well-defined tasks have specific goals, clear expected solutions, and, oftentimes, a single solution path. On the contrary, ill-defined tasks do not have clear goals, expected solutions, or solution paths [83].…”

Section: Design Process and Participantsmentioning

confidence: 99%

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Abdullah

Rostamzadeh

Sedig

et al. 2020

Data

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Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

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Section: Design Process and Participantsmentioning

confidence: 99%

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Abdullah

Rostamzadeh

Sedig

et al. 2020

Data

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“…However, it is an ill-defined problem. According to Arifin et al (2017), "it does not have clear goals, solution paths or expected solution." There is no universal definition or closed form formula of what kind of objects one should be looking for (Fortunato & Hric, 2016), and consequently, there is no golden standard to assess the quality of a community structure and the performance of a community detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Community structure: A comparative evaluation of community detection methods

Dao¹,

Bothorel²,

Lenca³

2020

Net Sci

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Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

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“…Another effort to prepare Indonesian students for PISA is to develop questions (Nusantara et al, 2021;Arifin et al, 2017). In this case, the PISA model questions in the context of historical buildings in the Karawang Regency will be developed.…”

mentioning

confidence: 99%

Developing PISA-like math problems in the content of space and shape through the context of historical buildings

et al. 2023

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The essential purpose of developing PISA-like questions is to train students' reasoning abilities so that students' ability to solve PISA-like questions increases. Therefore, this research aims to produce PISA-like math problems in the context of historical buildings in the Karawang Regency that are valid, practical, and potentially affect mathematics learning. The subjects of this study were junior high school students at SMPN 2 Karawang Barat. This research uses design research with development studies, consisting of preliminary and prototyping phases. The preliminary stage consists of two stages: needs analysis and design, while the prototyping stage consists of 5 phases: self-evaluation, expert review, one-to-one, small group, and field tests. The data collection techniques in this study were in the form of PISA-like math problems in the context of shape and space with the context of Historic Buildings in Karawang, observations, questionnaires, and interviews. All data obtained were analyzed descriptively. The results show that this study resulted in nine PISA-like math problems in the context of shape and space with the context of Historic Buildings in Karawang that are valid, practical, and potentially affect learning mathematics. The potential effect is related to students' interest in PISA-like questions and students' ability to understand and answer PISA-like questions. Finally, the results of this study have an impact on students who are getting trained in solving PISA-like math problems.

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Developing Ill-defined problem-solving for the context of “South Sumatera”

Cited by 14 publications

References 8 publications

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Community structure: A comparative evaluation of community detection methods

Developing PISA-like math problems in the content of space and shape through the context of historical buildings

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