Learning Data-Driven Decisions for Managers in New Style Companies with Professor Dutta from USA Part 6.1

The development of information technology today is very helpful in the company's business. However, if we don't understand the type of technology needed, we might make the wrong choice of technology. Especially in the field of decision making for companies, there is one information technology product that is very helpful, namely a decision support system.


STEKOM university's efforts to have a global reach include holding webinars on an international scale. On this occasion we will discuss an international webinar held by STEKOM University in which one of the speakers is a professor from the United States. The resource person is Kaushik Dutta who is a Professor and School Director at the University of South Florida. Professor Dutta in his presentation delivered material on decision support systems which are IT products that are very useful in corporate business.


The material presented by Professor Dutta includes Framework, Applications for Business, Techniques, and Infrastructure. Because the material presented is quite long, the news article that discusses Professor Dutta's presentation is divided into several parts. We are currently entering part 6.1. If the reader wants to know the previous presentation, please see some of the previous chapters in the title of the same article.


Continuing from the previous part, then Professor Dutta explained about various machine learning tools. Among them are Weka, Rapidminer, R, and Python. This article will discuss the material presented by Professor Dutta about WEKA.


WEKA is a software that applies various machine learning algorithms to perform several processes related to information retrieval systems or data mining. Some of the excellent features that WEKA has are:
- Classification
In WEKA there are many algorithms that support the process of classifying an object and it is easier for users to implement directly. The user can load the dataset, select the algorithm for classification, then be given several data representations that represent the results of the accuracy, error rate of the classification process.
- Regression
Regression is a process that can make predictions on various pre-formed patterns that are used as data models. The purpose of regression is to create a new variable that represents a representation of data development in the future. WEKA supports regression processes and this is made easier with a simple user interface/user experience.
 - Clustering
Clustering is one of the conceptual branches of the unsupervised method of machine learning that aims to group data and also explain the relationships that exist between the data and maximize similarities between classes/clusters but minimize similarities between classes/clusters. Clustering is used for data analysis and is expected to produce a data representation that represents a pattern formed due to the existing relationships between data.

In WEKA there are several algorithmic approaches to deal with clustering problems and in this feature there is also a conclusion section of the data clustering process that provides an outline of the calculations and results given in the implementation of the clustering algorithm.
- Association Rules
Association Rules is a method used to find various relationships between the large number of variables contained in a database.
- Visualization
WEKA has a feature to provide a data representation of the results of a data mining process in the form of images or charts which can also be used to select various parameters that support the formation of data representations in the WEKA application.
Data Preprocessing
WEKA provides features in terms of data preprocessing, namely stemming and stopword removal. The stemming and stopword removal processes in the WEKA software are based on English, so that for the implementation of languages ​​outside of English, it is required to carry out data preprocessing processes outside the WEKA application. Several stemming algorithms that have been provided by WEKA are Iterated Lovins Stemmer, Lovins Stemmer and Snowball Stemmer.


The data used in Weka is in the .arff extension format. This format is very flexible for editing with various types of text editors. You can open files with this extension with various text editors, such as Notepad.


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