scholarly journals Analisis Data mining dengan Metode C.45 pada Klasifikasi Kenaikan Rata-Rata Volume Perikanan Tangkap

2021 ◽  
Vol 2 (2) ◽  
pp. 74-81
Author(s):  
Muhammad Ridho Matondang ◽  
Muhammad Ridwan Lubis ◽  
Heru Satria Tambunan
Keyword(s):  
Data Mining ◽  
Natural Resources ◽  
Training Data ◽  
Average Volume ◽  
Volume Increase ◽  
Data Mining Techniques ◽  
Data Comparison ◽  
Resource Needs ◽  
Testing Data

Increasing the amount of demand for natural resource needs is increasing. One of them is natural resources in the sea and coast. The current condition of capture fisheries in Indonesia is not yet optimal. This is indicated by the increase in the volume of capture fisheries production which is very slow. The purpose of this study is to make data classification for the prediction of the average volume increase in capture fisheries with data mining techniques. Data mining techniques are applied to determine the data patterns of the capture fisheries dataset, so the results of the classification can be applied to evaluate the factors that affect the volume of capture fisheries. The classification algorithm used is C45. The results of the classification were tested with rapidminer in classifying data. The level of performance is indicated by the accuracy value. The accuracy value is obtained by testing the results of the classification of training data and testing data. Comparison of accuracy values between the algorithms used can be seen the best algorithm in making the classification of capture fisheries data.

scholarly journals A Systematic Classification Investigation of Rapid Intensification of Atlantic Tropical Cyclones with the SHIPS Database

2016 ◽  
Vol 31 (2) ◽  
pp. 495-513 ◽  
Author(s):  
Ruixin Yang
Keyword(s):  
Data Mining ◽  
Tropical Cyclones ◽  
Model Building ◽  
Probability Of Detection ◽  
Training Data ◽  
Rapid Intensification ◽  
Systematic Classification ◽  
Improvement Project ◽  
Data Mining Techniques ◽  
Testing Data

Abstract In hopes of better understanding the rapid intensification (RI) of tropical cyclones, the classification technique as a data mining process is used in this mining experiment. The mining results are expected to increase accurate forecasting abilities for RI through exhaustive data distillation. In this work, the Statistical Hurricane Intensity Prediction Scheme (SHIPS) database for the Atlantic basin during the period 1982–2009 is used as the data source and the Waikato Environment for Knowledge Analysis (WEKA) software is used for various classifier implementations. As in most classification applications, accuracies in model building with training data may be high. However, accuracies with testing data usually deteriorate. Various special steps are carried out in an effort to improve the accuracy. These steps include setting the cost parameters for overcoming the unbalanced RI samples, temporal averages of variable values for more accurate environmental estimation, feature filtering for irrelevant feature removal, and subset feature selections. The best performance measures of the training results are above 90% for probability of detection (POD) with 10%–20% false alarm ratios (FARs) for cases of RI within 24 h. However, the performance on the testing data is not as good. The reported RI forecasting accuracies in this work are lower than the goals set by NOAA in their Hurricane Forecast Improvement Project. Nevertheless, this work sheds light on the future direction of RI investigations using data mining techniques. Many more studies are needed before we can fully understand the potential and/or limitations of data mining techniques in RI investigations.

scholarly journals South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Yoga Religia ◽  
Gatot Tri Pranoto ◽  
Egar Dika Santosa
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Large Data ◽  
Classification Algorithm ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
Testing Data ◽  
Credit Data

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.

Classification of Intrusion Detection Using Data Mining Techniques

2018 ◽  
pp. 753-764 ◽  
Author(s):  
Roma Sahani ◽  
Shatabdinalini ◽  
Chinmayee Rout ◽  
J. Chandrakanta Badajena ◽  
Ajay Kumar Jena ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Data Mining Techniques ◽  
Using Data

Classification of Product Rating Using Data Mining Techniques

2017 ◽  
pp. 27-36 ◽  
Author(s):  
Pinku Deb Nath ◽  
Sowvik Kanti Das ◽  
Fabiha Nazmi Islam ◽  
Kifayat Tahmid ◽  
Raufir Ahmed Shanto ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data ◽  
Product Rating

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

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