A Hybrid Data Model for Prediction of Disaster using Data Mining Approaches

2016 ◽  
Vol 41 (7) ◽  
pp. 384-392
Author(s):  
Arjun Singh ◽  
◽  
Amit Saxena
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Hybrid Data ◽  
Using Data

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

scholarly journals Mapping Patterns Achievement Based on CRISP-DM and Self Organizing Maps (SOM) Methods

2017 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Santi Ika Murpratiwi ◽  
A.A Ngurah Narendra ◽  
Made Sudarma
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Government Programs ◽  
Self Organizing Maps ◽  
Data Assessment ◽  
Good Government ◽  
Catch Up ◽  
The Government ◽  
Using Data ◽  
Self Organizing

Successful of regional development is a reflection of the good government policy. Many programs created by the local government every period but many programs stop without results. Therefore, we need to evaluation its government programs. Evaluation of government programs must do to prevent the failure of the programs. One way to evaluate the government programs is to collect the support data to see the data model and analyzed the data. In this research will conduct an evaluation on the achievement of RPJMD performance of Bali Province by using data mining. The process of data mining used the CRISP-DM method and Self Organizing Maps (SOM) for mapping the achievement patterns. The used data is RPJMD data in the 2013-2018 period. but the data used for data mining process only 3 years data that is 2014-2016 as a middle evaluation. That data clustered into five clusters. The final result of this research are 78% of assessment indicators in the RPJMD program are inconsistent position and 22% are in an inconsistent position. Moreover, there are 84 assessment indicators that have no reached the target. From that results of data, assessment mapping can use as the guidance of Bali Province to catch up the achievement of RPJMD programs and prepare the next strategies to support the success of the RPJMD program during the RPJMD period.

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2017 ◽  
Vol 13 (2) ◽  
pp. 45-62 ◽  
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Information System ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Hidden Patterns ◽  
Using Data ◽  
Analyze Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

Forecasting Automobile Retail Sales Using Data Mining The Case of Ranchi, Jharkhand, India

2018 ◽  
Vol 6 (9) ◽  
pp. 572-574
Author(s):  
Gyaneshwar Mahto ◽  
Umesh Prasad ◽  
Rajiv Kumar Dwivedi
Keyword(s):  
Data Mining ◽  
Retail Sales ◽  
Using Data
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