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Pemanfaatan Algoritma WIT-Tree dan HITS untuk Klasifikasi Tingkat Keberhasilan Pemberdayaan Keluarga Miskin
https://doi.org/10.22146/ijccs.15927 Siti Khomsah(1), Edi Winarko(2*)
(1)
(2) Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta Sekip Utara Yogyakarta
(*) Corresponding Author
Abstract
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.
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Syari’udin, A, Artiani, L.E., Gusaptono, H., 2011, Efektivitas Program Pengentasan Kemiskinan :Studi Kasus Kabupaten Bantul, Provinsi Daerah Istimewa Yogyakarta, Laporan Penelitian, LPPM Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta.
Tan, P., Steinbach, M., dan Kumar, V., 2006, Data Mining Concept and Technique, Morgan Kaufman Publisher, San Francisco.
Agrawal, R. dan Srikant, R., 1994, Fast Algorithms for Mining Association Rules, VLDB’94, pp. 487- 499.
Liu, B., Hsu, W., & Ma, Y. (1998). Integrating Classification And Association Rule Mining. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York.
Liu, B., Ma, Y., dan Wong, C.-K., 2001, Classification Using Association Rules:Weaknesses and Enhancements, Data Mining for Scientific and Engineering Applications, Kluwer Academic Publishers, New York.
Yin, X. dan Han,J., 2003, CPAR: Classification Based On Predictive Association Rules, Proceedings of the Society for Industrial and Applied Mathematics International Conference on Data Mining, San Francisco, Calif, USA
Yang, Z., Tang, W.H., Shintemirov, A., Wu., Q.H., 2009, Association Rule Mining Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformer, IEEE, Vol 39
Dua,S., Singh,H., Thompson, H.W. , 2009, Associative Classification of Mammograms using Weighted Rules, Expert Systems with Applications, Volume 36, Issue 5, Pages 9250–9259.
Soni, S, Pillai, J, Vyas, O.P, 2009, An Associative Classifier Using Weighted Association Rule, IEEE, 978-1-4244-5612-3/09.
Tao, F., Murtagh, F. dan Farid,M.,Weighted Association Rule Mining using Weighted Support and Significance Framework, 2003, SIGKDD, August 24-27, Washington, DC, USA.
Kumar, P and V.S., Ananthanarayana, 2010, Discovery of Weighted Association Rule Mining, IEEE , volume 5, 978-1-4244-5586-7.
Mary, S.A. dan Malarvizhi,M., 2014, A New Improved Weighted Association Rules Mining With Dynamic Programming Approach For Predicting A User’s Next Access, Computer Science & Information Technology.
Wang, K. dan Su, T., 2002, Item Selection By "HubAuthority" Profit Ranking, SIGKDD ’02 , Canada, ACM 158113567X/02/0007.
Sun, K dan Bai,F., 2008, Mining Weighted Association Rules without Preassigned Weights, IEEE Transactions On Knowledge And Data Engineering, Vol. 20, No. 4, April 2008.
Ibrahim, S.P.S, dan Chandran, K.R., 2011, Compact Weight Class Association Rule Mining Using Information Gain, International Journal of Data Mining & Knowledge Management Process, Vol.1, No.6 November 2011.
Padmavalli, M., Dan Rao, Sreenivasa, 2013, An Efficient Interesting Weighted Association Rule Mining , International Journal Of Advanced Research In Computer Science And Software Engineering, Volume 3, Issue 10, October 2013 ISSN: 2277 128X.
Le, B., Nguyen, T.A. Cao, B. Vo, 2009, A Novel Algorithm for Mining High Utility Itemsets, IEEE, pp. 13 – 16.
Le, B., Nguyen, H., Vo, B., 2010, Efficient Algorithms for Mining Frequent Weighted Itemsets from Weighted Items Databases, IEEE, 978-1-4244-8075-3/10.
DOI: https://doi.org/10.22146/ijccs.15927
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