ArsyHakiki

CLASSIFICATION METHOD (ASSIGNMENT 6) Data mining There are several major  data mining  techniques   have been developing and using in data mining projects recently including  association ,  classification ,  clustering ,  prediction,   sequential patterns  and  decision tree . We will briefly examine those data mining techniques in the following sections. Classification Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. Clustering Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique. The clustering technique defines the classes and put...

EREADER SCORING AND EREADER TRANING ANALYSIS USING RAPIDMINER (ASSIGNMENT 5) I want to try to make decision tree of ereader scoring and training analysis using rapidminer. 1. Add the data ereader scoring.csv to rapidminer 2. Add the data ereader training.csv to rapidminer 3. After we add data above rapidminer will show this 4.  Click Design beside result 5. Drag Scoring and Training 6.  Make 2 Set Role operators to both your training and scoring streams. In the Parameters area on the right hand side of the screen, set the role of the   User_ID  attribute to  id . And then make the another set role for Training Streams and set the role of the   eReader_Adoption  attribute to  label . 7.  Next, search in the Operators tab for  Decision Tree . Select the basic  Decision Tree operator and add it to your training stream. 8.  And then drag the  Apply Model O...

PREDICTION MODEL USING ORANGE Assignment 4 I try to make Decision Tree, Naive bayes, KNN with data Pemilu and make it in Orange application 1. Decision Tree 2. Naive Bayes 3. KNN Conclusionn: I conclude that systemathic from this three (KNN, Naive Bayes, and Decision tree) first, i use Decision tree's tools to compare the data than Naive Bayes. After that measured by AUC (Area Under the receiver operating Characteristic curve) at the end for the final we can see that KNN algorithms can show verywell for the result of Data Pemilu. Than, decision tree's that we can see too many difficullty to read it and than of course not for naive bayes, because naive bayes show us uncompletly result.