Development of a data mining-based analysis framework for multi-attribute construction project information

2012 ◽  
Vol 26 (3) ◽  
pp. 574-581 ◽  
Author(s):  
Seokho Chi ◽  
Sung-Joon Suk ◽  
Youngcheol Kang ◽  
Stephen P. Mulva
Keyword(s):  
Data Mining ◽  
Construction Project ◽  
Analysis Framework ◽  
Attribute Construction

scholarly journals Data mining applied for earthworks optimisation of a toll road construction project

2018 ◽  
Vol 195 ◽  
pp. 04019 ◽  
Author(s):  
Andri Irfan Rifai ◽  
Yusuf Latief ◽  
Leni Sagita Rianti
Keyword(s):  
Data Mining ◽  
Construction Project ◽  
Road Construction ◽  
Support Vector ◽  
Toll Roads ◽  
Real World Data ◽  
Toll Road ◽  
Data Mining Approach ◽  
Significant Acceleration ◽  
Machine Analysis

The length of the toll roads operating in Indonesia is still less than in other countries. Significant acceleration is needed to keep up with the country’s traffic needs. Acceleration of development should be supported by the development capacities of road operators, one such capacity being earthworks. Data on earthworks can be utilised as a knowledge base and processed via a data mining approach, the results of which form the basis for interpretation and productivity predictions. The aim of this study is to develop a decision support system for the earthworks of a toll road construction project using the approach of data mining historical cases. The data mining approach used an artificial neural network and support vector machine analysis methods. The result is multi-objective optimisation with a genetic algorithm using real-world data from previous Indonesian toll road construction. This work aims to present a practical alternative for the optimisation of earthworks.

scholarly journals Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining

2015 ◽  
Vol 16 (2) ◽  
pp. 350
Author(s):  
MD. Hussain Khan ◽  
G. Pradeepini
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mobile Apps ◽  
Forensic Analysis ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Analysis Framework ◽  
Security Issues ◽  
The People ◽  
Using Data

<p>Phone is a device which provides communication between the people through voice, text, video etc. Now a day’s people may leave without food but not without using phones. No of operating systems are working with various versions and various security issues are working. Security is very important task in Mobiles and mobile apps. To improve the security status of mobiles, existing methodology is using cloud computing and data mining. Out traditional method is named as MobSafe to identify the mobile apps antagonism or graciousness. In the proposed system, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF).In this paper, our proposed system works on machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.</p>

scholarly journals PREDIKSI PROFIT PADA PERUSAHAAN DENGAN KLASIFIKASI ALGORITMA C4.5

2018 ◽  
Vol 5 (2) ◽  
pp. 179
Author(s):  
Erlin Elisa
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Construction Project ◽  
Professional Service ◽  
Data Mining Algorithm ◽  
Management Skills ◽  
Process Data ◽  
Consulting Services ◽  
Id3 Algorithm ◽  
Technical Specifications

<p><em>In the construction project activities, planning is used as a reference for job implementers and becomes the standard of project implementation, including: documents, technical specifications, schedule and budget. Inappropriate planning, inaccurate project realization investigations, inadequate project management skills and lack of professional service providers, are closely related to the outcome of a construction project process. CV.XYZ Abadi which is a company engaged in construction consulting services. At the present time CV.XYZ Abadi has done many construction planning projects both from government and private, this research will discuss how data mining with algorithm C4.5 process data from budget plan consultant planner cost to predict company profit. Data mining is a technique for extracting new information from piles or data warehouses, as we know information is seen as something that is very important and valuable because by mastering information it is easy to achieve a desired goal, this makes everyone race to while C4.5 algorithm is one of induction algorithm of decision tree that is ID3 (Iterative Dichotomiser 3). ID3 was developed by J. Ross Quinlan. In the ID3 algorithm procedure, the inputs are training samples, training labels and attributes. which will illustrate the profit prediction, the results of this study will result in the rules of profit and loss decisions company.</em></p><p><em><strong>Keywords</strong>: Profit, Data Mining, Algorithm C4.5, Tree Decision.</em></p><p><em>Dalam kegiatan proyek konstruksi, perencanaan dipergunakan sebagai bahan acuan bagi pelaksana pekerjaan dan menjadi standar pelaksanaan proyek, meliputi: dokumen, spesifikasi teknik, jadwal dan anggaran. Perencanaan yang tidak tepat, investigasi realisasi proyek yang tidak sempurna, kurang memadainya kemampuan pengelolaan proyek dan kurang profesionalnya penyedia jasa, berkaitan erat terhadap hasil suatu proses proyek konstruksi. CV.XYZ Abadi yang merupakan sebuah perusahaan yang bergerak dalam bidang jasa konsultan kontruksi. Pada saat sekarang ini CV.XYZ Abadi telah banyak mengerjakan proyek perencanaan konstruksi baik dari pemerintah maupun swasta,penelitian ini akan membahas bagaimana data mining dengan algoritma C4.5 mengolah data-data dari rencana anggaran biaya konsultan perencana untuk memprediksi profit perusahaan. Data mining merupakan sebuah teknik untuk menggali informasi baru dari tumpukan atau gudang data, sebagaimaya yang kita ketahui informasi dipandang sebagai sesuatu hal yang sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah untuk mencapai sebuah tujuan yang diinginkan, hal ini membuat setiap orang berlomba untuk memperoleh informasi.sedangkan algoritma C4.5 adalah salah satu algoritma induksi pohon keputusan yaitu ID3 (Iterative Dichotomiser 3). ID3 dikembangkan oleh J. Ross Quinlan. Dalam prosedur algoritma ID3, input berupa sampel training, label training dan atribut. yang akan menggambarkan prediksi profit, hasil dari penelitian ini akan menghasilkan rule-rule keputusan profit dan kerugian perusahaan.</em></p><p><em><strong>Kata kunci</strong>: Profit,Data Mining, Algoritma C4.5, Pohon Keputusan.</em></p><p> </p>

scholarly journals Genetic Programming for Attribute Construction in Data Mining

2003 ◽  
pp. 384-393 ◽  
Author(s):  
Fernando E. B. Otero ◽  
Monique M. S. Silva ◽  
Alex A. Freitas ◽  
Julio C. Nievola
Keyword(s):  
Data Mining ◽  
Genetic Programming ◽  
Attribute Construction

scholarly journals Penerapan Algoritma C4.5 Untuk Memprediksi Keuntungan Pada PT SMOE Indonesia

2019 ◽  
Vol 9 (1) ◽  
pp. 39
Author(s):  
Tukino Tukino
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Construction Project ◽  
Professional Service ◽  
Management Skills ◽  
Process Data ◽  
Consulting Services ◽  
Id3 Algorithm ◽  
Training Samples ◽  
Technical Specifications

In the construction project activities, planning is used as a reference for job implementers and becomes the standard of project implementation, including: documents, technical specifications, schedule and budget. Inappropriate planning, inaccurate project realization investigations, inadequate project management skills and lack of professional service providers, are closely related to the outcome of a construction project process. PT SMOE Indonesia which is a company engaged in construction consulting services. At the present time PT SMOE Indonesia has done many construction planning projects both from government and private, this research will discuss how data mining with algorithm C4.5 process data from budget plan consultant planner cost to predict company profit. Data mining is a technique for extracting new information from piles or data warehouses, as we know information is seen as something that is very important and valuable because by mastering information it is easy to achieve a desired goal, this makes everyone race to while C4.5 algorithm is one of induction algorithm of decision tree that is ID3 (Iterative Dichotomiser 3). ID3 was developed by J. Ross Quinlan. In the ID3 algorithm procedure, the inputs are training samples, training labels and attributes. which will illustrate the profit prediction, the results of this study will result in the rules of profit and loss decisions company.

Application of Data Mining in Construction of Corruption Risks Prevention System

2014 ◽  
Vol 513-517 ◽  
pp. 2165-2169
Author(s):  
Jun Qing Su ◽  
Su Dan
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Construction Project ◽  
Warning Signs ◽  
Data Mining Algorithms ◽  
Early Warning Signs ◽  
Prevention System ◽  
Risks Prevention ◽  
Mining Algorithms

There are various types of corruption risks in the processes of public powers operation. This paper aims at the occupational corruption risks emerging from the exercise of public powers. Combined with the research on the construction project of the system and the introduction of data mining, this paper will probe into the scientific and effective data mining algorithms. By doing so, an electric cage is weaved to curb the abuse of powers by screening and selecting the early warning signs, thus guard against occupational corruption risk problems at the source.

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