IMPLEMENTASI ALGORITMA C5.0 UNTUK MENGANALISA GEJALA PRIORITAS PADA ANAK YANG MENGALAMI BULLYING
| Main Author: | Rahmayanti, Nabillah Annisa |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/41266/1/PENDAHULUAN.pdf http://eprints.umm.ac.id/41266/2/BAB%20I.pdf http://eprints.umm.ac.id/41266/3/BAB%20II.pdf http://eprints.umm.ac.id/41266/4/BAB%20III.pdf http://eprints.umm.ac.id/41266/5/BAB%20IV.pdf http://eprints.umm.ac.id/41266/6/BAB%20V.pdf http://eprints.umm.ac.id/41266/7/LAMPIRAN.pdf http://eprints.umm.ac.id/41266/ |
Daftar Isi:
- Bullying often occurs in children, especially teenagers and unsettles parents. The rise of cases of bullying in this country even caused casualties. This can be prevented by knowing the symptoms of a child who has bullying. The condition of a child who cannot express his complaints, certainly makes parents and teachers at school difficult to understand what is happening to them. This could be because the child is experiencing bullying by his friends. Therefore, researchers have a goal to produce selected features using the C5.0 algorithm. So using the selected features can ease the work in filling out questionnaires and also shorten the time in determining whether a child is exposed to bullying or not based on the symptoms in each question in the questionnaire. To support the data in this study, the researcher used a questionnaire to get answers to questions that contained the symptoms of children who were victims of bullying. The answer from the respondent will be processed into a data collection which will later be divided into training data and test data for further research using the C5.0 Algorithm. The evaluation method used in this study is 10 fold cross validation and to assess accuracy using confusion matrix. This study also carried out a comparison with several other classification algorithms, namely Naive Bayes and KNN which aimed to see how accurate the C5.0 algorithm was in feature selection. The test results show that the C5.0 algorithm is capable of feature selection and also has a better accuracy compared to the Naive Bayes and KNN algorithms with accuracy results before using feature selection of 92.77% and after using feature selection of 93.33%.