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ITS » Master Theses » Jaringan Cerdas Multimedia S2
Posted by dee@its.ac.id at 11/01/2012 10:42:21  •  166 Views

TEXT MINING DALAM GAME TUTOR CERDAS `FISIKA DASAR` MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES

TEXT MINING IN INTELLIGENT TUTOR GAME `BASIC PHYSICS` USING NAÏVE BAYES CLASSIFICATION METHOD

Created by :
RATNASARI, DWI  ( 2209205209 )



SubjectPemrograman komputer
Permainan komputer
Alt. Subject Machine learning Intelligent tutoring systems Data mining
KeywordMachine learning
Text mining
Klasifikasi
Naïve bayes
Precision recall

[ Description ]

Dalam proses belajar di kelas, siswa sering mengalami kesulitan dalam memahami materi belajar yang diberikan oleh guru. Untuk dapat memudahkan proses pemahaman murid, dibuatlah suatu sistem pembelajaran dengan menggunakan metode permainan (game). Sistem Tutor Cerdas (STC) atau Intellegent Tutorial System (ITS) adalah sebuah software yang menyediakan instruksi-instruksi untuk seorang pelajar. Sistem ini bekerja dengan cara membimbing siswa untuk memahami materi belajar sebagaimana yang dilakukan oleh guru. Game tutor cerdas ini, menggunakan metode untuk mengklasifikasi soal berdasarkan kemampuan siswa. Pengklasifikasian soal berupa kumpulan teks dimasukkan ke dalam kategori atau kelas dokumen-dokumen yang sudah disediakan. Text mining diperlukan untuk melakukan proses analisis soal berupa kumpulan teks untuk menemukan informasi baru (unknown information) berdasarkan keywords atau terms (istilah) yang sering muncul. Penelitian ini berisikan tentang salah satu implementasi dari pembelajaran mesin (machine learning) dalam pengklasifikasian dokumen teks (text classification) soal ”fisika dasar” menggunakan metode klasifikasi Naïve Bayes. Pengklasifikasian dokumen teks membentuk golongan-golongan (kelas-kelas) dari dokumen berdasarkan pada kelompok yang sudah diketahui sebelumnya (supervised). Metode klasifikasi Naïve Bayes dipilih karena selain performanya cukup baik untuk klasifikasi teks, juga karena algoritma ini memiliki tingkat keakuratan tinggi. Berdasarkan hasil uji coba, menghasilkan nilai precision and recall sebesar 0.929.


Alt. Description

In the learning process, students often have difficulties in understanding learning materials given by the teacher. In order to facilitate the student\\\\\\\'s understanding of a material, it was made a method or a learning system that uses game method. Intelligent Tutorial System (ITS) is a software that provides instructions for a student. The system works by guiding a student to understand the learning material like what a teacher does. This intelligent tutoring game of learning, uses a method to classify question based on the student\\\\\\\'s ability. The question classification in the form of a texts collection was included into categories or document classes provided. Text mining is required to perform an analysis process of questions about a collection of text to discover new information (unknown information) based on keywords or terms that often arise. This research discuss about an implementation of machine learning in the text classification of \\\\\\\"basic physics\\\\\\\" questions using Naïve Bayes classification method. Classification of text documents forms classes of the document based on the group already known before (supervised). Naïve Bayes classification method was chosen because besides it has a good performance for text classification, this algorithm also has a high degree of accuracy. Based on the experiments, it produces precision and recall value 0.929.

Contributor:
  1. Mochammad Hariadi, ST., M.Sc., Ph.D.
Date Create:12/08/2011
Type:Text
Format:pdf
Language:Indonesian
Identifier:ITS-Master-3100011045209
Collection ID:3100011045209
Call Number:RTE 006.31 Rat t


Source :
Master Thesis of Electrical Engineering, RTE 006.31 Rat t, 2011

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  1.  ITS-Master-17171-2209205209-Cover_id.pdf - 76 KB pdf files
  2.  ITS-Master-17171-2209205209-Cover_en.pdf - 52 KB pdf files
  3.  ITS-Master-17171-2209205209-Approval_Sheet.pdf - 320 KB pdf files
  4.  ITS-Master-17171-2209205209-Abstract_id.pdf - 272 KB pdf files
  5.  ITS-Master-17171-2209205209-Abstract_en.pdf - 224 KB pdf files
  6.  ITS-Master-17171-2209205209-Preface.pdf - 286 KB pdf files
  7.  ITS-Master-17171-2209205209-Table_of_Content.pdf - 197 KB pdf files
  8.  ITS-Master-17171-2209205209-Illustrations.pdf - 175 KB pdf files
  9.  ITS-Master-17171-2209205209-Tables.pdf - 177 KB pdf files
  10.  ITS-Master-17171-2209205209-Chapter1.pdf - 221 KB pdf files
  11.  ITS-Master-17171-2209205209-Conclusion.pdf - 179 KB pdf files
  12.  ITS-Master-17171-2209205209-Bibliography.pdf - 177 KB pdf files
  13.  ITS-Master-17171-2209205209-Biography.pdf - 168 KB pdf files
  14.  ITS-Master-17171-2209205209-Presentation.pdf - 328 KB pdf files

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  2. ITS-Master-17171-2209205209-Chapter3.pdf - 488 KB pdf files
  3. ITS-Master-17171-2209205209-Chapter4.pdf - 826 KB pdf files
  4. ITS-Master-17171-2209205209-Paper.pdf - 587 KB pdf files


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Klasifikasi , Machine , Machine learning , Naïve , Naïve bayes , Precision , Precision recall , Text , Text mining , bayes , learning , mining , recall




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