Human Development Index (HDI) of Jawa Timur, Indonesia (Jawa Timur Indeks Pembangunan Manusia (IPM)) – Final Full Version

Full paper (ACADEMIA)
Human Development Index (HDI) of Jawa Timur, Indonesia (Jawa Timur Indeks Pembangunan Manusia (IPM)) – Final Full Version

Abstract
Human Development Index (HDI) classification or clustering refers to application of machine learning techniques to study trends between variables of collected statistical data. In this paper we explore different classification and clustering methods to the dataset originally from survey to categorize geographical area (cities) based on four level of development. We use data from Badan Pusat statistiks of Indonesia 2004-2012; we use sample of 342 out of 22,366 full data. If you are looking about how machine learning techniques is used in social sciences then you are in the right track; we hope through this paper you will get to know the way out.

Jawa Timur Indeks Pembangunan Manusia (IPM) Human Development Index (HDI) of Jawa Timur, Indonesia – Introduction

Introduction: Human Development Index
The Human Development Index (HDI) is a composite statistic of life expectancy, education, and per capita income indicators, which are used to rank countries into four tiers of human development.
Four tiers of human development; very high, high, medium, and low human development.
Indeks Pembangunan Manusia (IPM)

Credit German Data, Implemented with K-Means in Weka

Credit German Data, Implemented with K-Means in Weka
Kholed Langsari
Student ID : 5115201701
Department of Informatics Engineering
Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Dataset Description

Application of the Naive Bayesian Classifier to optimize treatment decisions

Application of the Naive Bayesian Classifier to optimize treatment decisions

Kholed Langsari
Student ID : 511520171
Informatics Engineering,
Institute Technology Sepuluh Nopember

Introduction
Artificial Intelligence systems supporting treatment decisions still require careful study and remain a challenge. A simple, user friendly classifier can assist clinician in decision making process.
The aim of our work is to create and test an expert system which would combine simplicity of operation, credibility and user friendly interface.
Construction of the classifier is based on the Bayes Theorem, Naive Bayesian Classifier.
Implemented in WEKA environment.

Credit Approval Dataset Implemented(Classified) with Weka

Credit Approval Dataset Implemented(Classified) with Weka
Kholed Langsari
Student ID : 5115201701
Department of Informatics Engineering
Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Introduction to Weka
Weka, Waikato Environment of Knowledge Analysis
Data mining workbench
Machine learning algorithms for data mining tasks
100+ algorithms for classification
75 for data preprocessing
25 to assist with feature selection
20 for clustering, finding association rules, etc
Credit Approval Dataset
Dataset concerns credit card applications
All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data
This dataset is interesting because there is a good mix of attributes — continuous, nominal with small numbers of values, and nominal with larger numbers of values, there are also a few missing value