Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

Rahman, Fauziah Abdul; Desa, Mohammad Ishak; Wibowo, Antoni; Haris, Norhaidah Abu

doi:10.11591/eecsi.v1.357

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Classical statistics have limitations for performing large amounts of data analysis or complex relationships between data variables. The solution for this problem and its limitations is to develop computerbased data analysis tools with more excellent capabilities and are automatic [20]. With the development of semi-automatic approaches in various fields of science, in recent decades, there has been an increase and across disciplines, such as AI, statistics, and information systems.…”

Section: Data Miningmentioning

confidence: 99%

Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

Rifai¹

2022

Artificial Intelligence

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Natural disasters can occur anytime and anywhere, especially in areas with high disaster risk. The earthquake that followed the tsunami and liquefaction in Palu, Indonesia, at the end of 2018 had caused tremendous damage. In recent years, rehabilitation and reconstruction projects have been implemented to restore the situation and accelerate economic growth. A study is needed to determine whether the rehabilitation and reconstruction that has been carried out for three years have met community satisfaction. The results of further analysis are expected to predict the level of community satisfaction for the implementation of rehabilitation and other reconstruction. The method used in this paper is predictive modeling using a data mining (DM) approach. Data were collected from all rehabilitation and reconstruction activities in Palu, Sigi, and Donggala with the scope of the earthquake, tsunami, and liquefaction disasters. The analysis results show that the Artificial Neural Network (ANN) and the support vector machine (SVM) with a DM approach can develop a community satisfaction prediction model to implement rehabilitation and reconstruction after the earthquake-tsunami and liquefaction disasters.

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Section: Data Miningmentioning

confidence: 99%

Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

Rifai¹

2022

Artificial Intelligence

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“…Data mining is a process to explore the excess value of information that was not previously known to exist in a database. The patterns that are very useful and have more value than the data contained in the database are obtained by recognizing the information obtained [11]. From the explanation above it can be concluded that data mining is a step of analyzing the process of knowledge discovery in the database.…”

Section: Issn: 2252-8938mentioning

confidence: 99%

A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students

Nurhachita

Negara

2021

IJ-AI

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<span id="docs-internal-guid-5a78994c-7fff-41c1-c57f-91661e44674c"><span>The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.</span></span>

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“…The analysis of the current international practice of the subject area shows the following global factors that have a significant and permanent impact on the information management of modern logistics 3PL (Fauziah Abdul Rahman et al, 2014):…”

Section: Introductionmentioning

confidence: 99%

Methodology of effective application of Big Data and Data Mining technologies as an important anti-crisis component of the complex policy of logistic business optimization

Krasnyuk¹,

Hrashchenko²,

Kustarovskiy³

et al. 2018

E'H

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Krasnyuk M. T., Hrashchenko I. S., Kustarovskiy O. D., Krasniuk S. O. Methodology of effective application of Big Data and Data Mining technologies as an important anti-crisis component of the complex policy of logistic business optimization Економічні горизонти № 3(6)'2018Abstract. The purpose of the research is divided into two, sequential and related phases: a) development and configuration of a comprehensive policy of optimization of the logistic business as an integral part of the anti-crisis measures, taking into account the domestic national specifics, sector specifics of the transport industry in the current systemic political and macroeconomic crisis; b) considering the importance of optimizing the information management of the logistic company (as an important component within the framework of the developed anti-crisis optimization policy). The next goal of the article is to develop a methodology for hybrid effective use of innovative technologies of Big Data and Data Mining within the framework of adaptive logistic information system for logistic companies. Methodology. Special attention is paid to optimization of information management of a logistic company (8th and 9th stages within the framework of the above-mentioned policy of optimization of logistic business) with the use of innovative technologies Big Data and Data Mining. Results. Taking into account the negative influence of the global, national and sectoral factors of management, the authors, taking into account the current political and macroeconomic crisis, offered a comprehensive policy of optimizing logistic business (consisting of 9 detailed stages) as an integral part of the anti-crisis policy of the logistic company. Taking into account the specialty chosen by the authors (Mathematical methods, models and information technologies in the economy), within the framework of which scientific research is carried out, special attention is paid to optimization of information management of a logistic company (8th and Математичні методи, моделі та інформаційні технології в економіці ISSN 2522-9273; e-ISSN 2616-5236 122 http://eh.udpu.edu.ua № 3(6)'2018 Економічні горизонти9th stages within the framework of the above-mentioned policy of optimization of logistic business) with the use of innovative technologies Big Data and Data Mining. Practical meaning. The obtained results are relevant and applicable not only for local logistic companies, but also for international applications in the context of projected global macroeconomic and current national crisis phenomena. Prospects for further research. The prospect of further practical use of the obtained results confirms that the above results have already been used by the authors in designing an adaptive logistic information system for domestic companies, and the manuscript of authors' design decisions has already been submitted for publication and review.

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Knowledge Discovery Database (KDD)-Data Mining Application in Transportation

Cited by 7 publications

References 11 publications

Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students

Methodology of effective application of Big Data and Data Mining technologies as an important anti-crisis component of the complex policy of logistic business optimization

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