Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin
The successful rate of the poor families empowerment can be classified by characteristic patterns extracted from the database that contains the data of the poor families empowerment. The purpose of this research is to build a classification model to predict the level of success from poor families, w...
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2017-01-31.
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| Online adgang: | Get Fulltext Get Fulltext |
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| LEADER | 02591 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_15927 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Khomsah, Siti |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Winarko, Edi |e author |
| 245 | 0 | 0 | |a Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2017-01-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/15927 | ||
| 520 | |a The successful rate of the poor families empowerment can be classified by characteristic patterns extracted from the database that contains the data of the poor families empowerment. The purpose of this research is to build a classification model to predict the level of success from poor families, who will receive assistance empowerment of poverty. Classification models built with WARM, which is combining two methods, they are HITS and WIT-tree. HITS is used to obtained the weight of the attributes from the database. The weights are used as the attributes's weight on methods WIT-tree. WIT-tree is used to generate the association rules that satisfy a minimum weight support and minimum weight confidence. The data used was 831 sample data poor families that divided into two classes, namely poor families in the standard of "developing" and poor families in the level of "underdeveloped". The performance of classification model shows, weighting attribute using HITS approaches the accuracy of 86.45% and weighted attributes defined by the user approaches the accuracy of 66.13%. This study shows that the weight of the attributes obtained from HITS is better than the weight of the attributes specified by the user. | ||
| 540 | |a Copyright (c) 2017 IJCCS - Indonesian Journal of Computing and Cybernetics Systems | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a poverity reduction, Association Rule Classifier, Weighted Asociation Rule Classifier, WIT-tree, HITS | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 11, No 1 (2017): January; 31-42 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/15927/11720 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/15927 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/15927/11720 |z Get Fulltext |