International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 10, No. 10, 8 Oct. 2018
Cover page and Table of Contents: PDF (size: 524KB)
Determination of Status of Family Stage Prosperous of Sidareja District Using Data Mining Techniques
Full Text (PDF, 524KB), PP.1-10
Views: 0 Downloads: 0
Author(s)
Index Terms
Family stage prosperous, data mining, K Nearest Neighbor, Naive Bayes, Principal Component Analysis
Abstract
Family welfare is a family formed in legitimate marriage, spiritual needs and material worthy, devoted to God YME, have a harmonious relationship, harmonious and balanced with society and the environment. The government has implemented various family development programs prosperous. To support this, every year the government implements the family data collection process. Family data collection is considered an important step because it has many functions, primarily to understand the target group and to determine solutions to solve the problems of each target group. The search or discovery process of information and knowledge contained in the number of data can be done with data mining technology. Data mining is a term used to describe the discovery of knowledge in a database. In this case data mining can be used to determine the status of the prosperous family stage. The K-Nearest Neighbor (KNN) method, the Naive Bayes method and the Principal Component Analysis (PCA) are used for the proper classification of status stages. Based on the test results, the performance test of classification algorithm for case of determining status of prosperous family of Sidareja District for Naïve Bayes method using confusion matrix obtained 98.12% accuracy after added PCA feature selection to 97.73% while KNN method obtained accuracy of 98.86%, then after added PCA feature selection increased to 98.96%.
Cite This Paper
R. Bagus Bambang Sumantri, Ema Utami, " Determination of Status of Family Stage Prosperous of Sidareja District Using Data Mining Techniques", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.10, pp.1-10, 2018. DOI:10.5815/ijisa.2018.10.01
Reference
[1]BKKBN, “Pedoman Tata Cara Pencatatan dan Pelaporan Pendataan Keluarga Tahun 2007”, BKKBN Provinsi Jawa Tengah, 2009.
[2]N. Jain, V. Srivastava, “Data Mining Techniques: A Survey Paper”, International Journal of Research in Engineering and Technology (IJRET), ISSN: 2319-1163, Vol. 2, Issue: 11, pp. 116-119, Nov. 2013.
[3]Swathi V, S. S. Kumar, P. Perumal, “A Novel Fuzzy-Bayesian Classification Method for Automatic Text Categorization”, International Journal of Scientific Research in Science & Technology (IJSRST), ISSN: 2395-602X, Vol. 3, Issue: 1, pp. 233-239, 2017.
[4]E. Setiawan, A. Muttaqin, "Implementation of K Nearest Neighbors Face Recognition on Low-power Processor", Telkomnika, ISSN: 1693-6930, Vol. 3, No. 3, pp. 949-954, September, 2015.
[5]Ayman E. Khedr, S.E.Salama, Nagwa Yaseen, “Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis” International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.7, pp.22-30, 2017.
[6]B. Narendra, K. Uday Sai, G. Rajesh, K. Hemanth, M. V. Chaitanya Teja, K. Deva Kumar, “Sentiment Analysis on Movie Reviews : A Comparative Study of Machine Learning Algorithms and Open Source Technologies,” International Journal of Intelligent Systems and Applications (IJISA), Vol. 8, No.8, pp. 66–70, 2016.
[7]P. Kalaivani, K. L. Shunmuganathan, “Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches”, Indian Journal of Computer Science and Engineering (IJCSE), ISSN: 0976-5166, Vol. 4, No. 4, pp. 285-292, Aug-Sep. 2013.
[8]L. Dey, S. Chakraborty, A. Biswas, B. Bose, S. Tiwari. “Sentiment Analysis of Review Datasets using Naïve Bayes‘ and KNN Classifier”, I.J. Information Engineering and Electronic Business (IJIEEB), ISSN: 2074-9023, Vol. 8, No. 4, pp. 54-62, 2016.
[9]Jozette V. Roberts, Dr. Isaac Dialsingh, “On the Performance of Classification Techniques with Pixel Removal Applied to Digit Recognition,” International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.8, pp.38-47, 2016.
[10]E. Y. Kalafi, W. B. Tan, C. Town, S. K. Dhillon, “Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches”, International Conference On Bioinformatics (InCoB), Vol: 17, Supplement: 19:511, pp. 260-295, 2016.
[11]Savithri.V, Aruna, “Comparative Study of Knn/Pcs with Naïve Bayes/Rsa”, International Journal of Scientific & Engineering Research (IJSER), ISSN 2229-5518, Vol. 7, Issue: 6, pp. 815-821, June, 2016.
[12]M. S. B. PhridviRaj, C. V. GuruRao, “Data mining – past, present and future – a typical survey on data streams”, The 7th International Conference Interdisciplinarity in Engineering (INTER-ENG), Procedia Technology, Vol. 12, No. 2014, pp. 255–263, 2013.
[13]T. Karthikeyan, N. Ravikumar, “A Survey on Association Rule Mining”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN: 2278-1021, Vol. 3, Issue: 1, pp. 5223-5227, January, 2014.
[14]M. S. Chen, J. Han, P. S. Yu, “Data Mining: An Overview from Database Perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, Issue: 6, pp. 866-883, Dec. 1996.
[15]G. Dimitoglou, J. A. Adams, C. M. Jim, “Comparison of the C4.5 and a Naïve Bayes Classifier for the Prediction of Lung Cancer Survivability”, Journal of Computing, ISSN: 2151-9617, Vol. 4, Issue: 8, pp. 1-9, August. 2012.
[16]N. Bhatia, Vandana, “Survey of Nearest Neighbor Techniques”, International Journal of Computer Science and Information Security (IJCSIS), ISSN: 1947-5500, Vol. 8, No. 2, pp. 302-305, 2010.
[17]K. A. Vidhza, G. Aghila, “A Survey of Naïve Bayes Machine Learning Approach in Text Document Classification”, International Journal of Computer Science and Information Security (IJCSIS), ISSN: 1947-5500, Vol. 7, No. 2, pp. 206-211, 2010.
[18]C. F. Chang, W. Y. Ling, S. S. Hou, “Preparation and Characterization of Superparamagnetic Nanocomposites of Aluminosilicate/Silica/Magnetite”, Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 336, Issues 1–3, pp. 159-166, 20 March. 2009.
[19]Y. Yiming, L. Xin, “A Re-examination of Text Categorization Methods”, SIGIR '99 Proceedings of The 22nd annual international ACM SIGIR conference on Research and Development in Information Retrieval, ISBN: 1-58113-096-1, pp. 42-49, August. 2009.
[20]K.R. Lakshmi, M. Veera Krishna, S. Prem Kumar, “Utilization of Data Mining Techniques for Prediction and Diagnosis of Tuberculosis Disease Survivability” I.J. Modern Education and Computer Science (IJMECS), Vol. 5, No. 8, pp. 8-17, 2013.
[21]T. Hastie, R. Tibshirani, J. Friedman, “The Elements of Statistical Learning Data Mining, Inference, and Prediction”, New York : Springer, 2008.
[22]M. Journ'ee, Y. Nesterov, P. Richt'arik, R. Sepulchre, “Generalized Power Method for Sparse Principal Component Analysis”, Journal of Machine Learning Research, ISSN: 1532-4435, Vol. 11, pp. 517-553, March. 2010.
[23]M. Bramer, “Principles of Data Mining”, Second Edition, New York : Springer, ISBN: 978-1-4471-4884-5, 2013
[24]J. Han, M. Kamber, J. Pei, “Data Mining Concept and Techniques”, Third Edition, Elsevier Inc, ISBN: 978-0-12-381479-1, 2012.
[25]G. Kesavaraj, S. Sukumaran, “A Study on Classification Techniques in Data Mining,” Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT), DOI: 10.1109, pp. 1–7, 4-6 July. 2013.