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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PARAMETER ASOSIASI UNTUK MENENTUKAN KORELASI JURUSAN DAN INDEKS PRESTASI KUMULATIF
Keywords:
Association Rule, Support, Confidence, Correlation
Abstract
One of the problems in higher education is the mistake of prospective students in majors selection. This is caused by not paying attention to the suitability of the major in the original school with the chosen major in higher education so that it impacts not only non optimal processing and learning outcomes, such as the low GPA, but also on social life, such as increasing unemployment. The selection of the right major is very important and to help prospective students in choosing it requires an online system that can be accessed by everyone and select original school majors to see conformity with majors in higher education. This system uses association rules and parameters of support and confidence in data mining. The purpose of this research is to determine the correlation between majors in the original school, majors in higher education and the achievement of the GPA through the use of support and confidence parameters that process the knowledge base in the form of an alumni database on the online system created. Training or testing was conducted on 10,254 data in the database and produced new information and knowledge that between the majors of the original school, the choice of majors in higher education and GPA had a strong correlation with the value of confidence reaching 100%.
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References
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Tseng, V. S., Shie, B. E., Wu, C.-W., & Yu, P. S. (2013). Efficient Algorithms for Mining Top-K High Utility Itemsets from Transactional Database. IEEE Transactions on Knowledge and Data Engineering, 28(8), 1772–1787. https://doi.org/10.1109/tkde.2015.2458860
Yabing, J. (2013). Research of an Improved Apriori Algorithm in Data Mining Association Rules. International Journal of Computer and Communication Engineering, 2(1), 25–27. https://doi.org/10.7763/ijcce.2013.v2.128
Ahuja, R., Garg, A. G., Jain, D., & Sachdeva, D. (2018). Predicting B.Tech student admission decisions by data mining algorithms. International Journal of Engineering & Technology, 7(1–3), 90–94. https://doi.org/10.14419/ijet.v7i1.3.9664
Ait-Mlouk, A., Gharnati, F., & Agouti, T. (2017). An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety. European Transport Research Review, 9(40), 1–13. https://doi.org/10.1007/s12544-017-0257-5
Al Syahdan, S., & Sindar, A. (2018). Data Mining Penjualan Produk Dengan Metode Apriori Pada Indomaret Galang Kota. Jurnal Nasional Komputasi Dan Teknologi Informasi (JNKTI), 1(2), 56–63. https://doi.org/10.32672/jnkti.v1i2.771
Anitha, G., A. Karthika, R., Bindu, G., & V. Sriramakrishnan, G. (2018). Modified classic a priori algorithm for association rule mining. International Journal of Engineering & Technology, 7(2.21), 414–416. https://doi.org/10.14419/ijet.v7i2.21.12455
Astudillo, C., Bardeen, M., & Cerpa, N. (2014). Editorial: Data mining in electronic commerce confidence - Support vs. Confidence. Journal of Theoretical and Applied Electronic Commerce Research, 9(1), 1–7. https://doi.org/10.4067/S0718-18762014000100001
Buaton, R., Jollyta, D., Mawengkang, H., Zarlis, M., & Effendi, S. (2019). Laporan Akhir Penelitian Mandiri. Kota Medan.
Chauhan, A., & Cerpa, N. (2001). A Comparison of Procurement Models for B2B Electronic Commerce. In Optima (pp. 1–14).
Correa Bahnsen, A., Stojanovic, A., Aouada, D., & Ottersten, B. (2013). Cost sensitive credit card fraud detection using bayes minimum risk. Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, 12, 333–338. https://doi.org/10.1109/ICMLA.2013.68
Ding, S., Shi, Z., Chen, K., & Taher Azar, A. (2015). Mathematical Modeling and Analysis of Soft Computing. Mathematical Problems in Engineering (Vol. 2015). https://doi.org/10.1155/2015/578321
Harahap, R. F. (2014). Duh, 87% Mahasiswa Indonesia Salah Jurusan! Okexone.Com, p. News Kampus. Retrieved from https://news.okezone.com/read/2014/02/24/373/945961/duh-87-mahasiswa-indonesia-salah-jurusan
Istrat, V., & Lalić, N. (2017). Association Rules as a Decision Making Model in the Textile Industry. Fibres and Textiles in Eastern Europe, 25(4), 8–14. https://doi.org/10.5604/01.3001.0010.2302
Jafarzadeh, H., Rahmati Torkashvand, R., Asgari, C., & Amiry, A. (2015). Provide a new approach for mining fuzzy association rules using Apriori algorithm. Indian Journal of Science and Technology, 8(S7), 127–134. https://doi.org/10.17485/ijst/2015/v8i8/63627
Kamsu-Foguem, B., Rigal, F., & Mauget, F. (2013). Mining association rules for the quality improvement of the production process. Expert Systems with Applications, 40(4), 1034–1045. https://doi.org/10.1016/j.eswa.2012.08.039
Kwon, J. H., Lee, S. B., Park, J., & Kim, E. J. (2017). Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things. In Journal of Physics: Conference Series (Vol. 892, pp. 1–8). https://doi.org/10.1088/1742-6596/892/1/012008
Orriols-Puig, A., Martínez-López, F. J., Casillas, J., & Lee, N. (2013). A soft-computing-based method for the automatic discovery of fuzzy rules in databases: Uses for academic research and management support in marketing. Journal of Business Research, 66(2013), 1332–1337. https://doi.org/10.1016/j.jbusres.2012.02.033
Pulakkazhy, S., & Balan, R. V. S. (2013). Data mining in banking and its applications- A review. Journal of Computer Science, 9(10), 1252–1259. https://doi.org/10.3844/jcssp.2013.1252.1259
Sánchez, D., Vila, M. A., Cerda, L., & Serrano, J. M. (2009). Association rules applied to credit card fraud detection. Expert Systems with Applications, 36, 3630–3640. https://doi.org/10.1016/j.eswa.2008.02.001
Santhosh, S., & Francis, M. (2015). Clinic + - A Clinical Decision Support System Using Association Rule Mining. International Journal of Innovative Research in Computer and Communication Engineering, 3(4), 3585–3590.
Tseng, V. S., Shie, B. E., Wu, C.-W., & Yu, P. S. (2013). Efficient Algorithms for Mining Top-K High Utility Itemsets from Transactional Database. IEEE Transactions on Knowledge and Data Engineering, 28(8), 1772–1787. https://doi.org/10.1109/tkde.2015.2458860
Yabing, J. (2013). Research of an Improved Apriori Algorithm in Data Mining Association Rules. International Journal of Computer and Communication Engineering, 2(1), 25–27. https://doi.org/10.7763/ijcce.2013.v2.128
Published
2019-03-15
How to Cite
Buaton, R., Jollyta, D., Mawengkang, H., Zarlis, M., & Effendi, S. (2019). PARAMETER ASOSIASI UNTUK MENENTUKAN KORELASI JURUSAN DAN INDEKS PRESTASI KUMULATIF. Jurnal Pilar Nusa Mandiri, 15(1), 111-118. https://doi.org/10.33480/pilar.v15i1.285
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