Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm

Mohammad Imron, Satia Angga Kusumah

Abstract


The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.


Article Metrics

Abstract: 303 Viewers PDF: 180 Viewers

Keywords


Data mining; Classification; K-NN algorithm; Graduation; Timely

Full Text:

PDF


References


F. Gorunescu, “Data Mining Concept Model and Techniques”. Berlin:Springer, 2011.

Akbarinia, R., Pacitti, E., & Valduriez, P. (2007). Processing top-k queries indistributed hash tables. InEuro-Par(pp. 489–502).

Balke, W. -T., Nejdl, W., Siberski, W., & Thaden, U. (2005). Progressive distributedtop-kretrieval in peer-to-peer networks. InICDE.

Bao, J., Zheng, Y., & Mokbel, M .F. (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. InProceedings of the 20th international conference on advances in geographic information systems(SIGSPATIAL), 2012.

Bast, H., Majumdar, D., Schenkel, R., Theobald, M., & Weikum, G. (2006). Io-top-k:index-access optimized top-k query processing. In VLDB(pp. 475–486).

Chang, Y.-J., Liu, H.-H., Chou, L.-D., Chen, Y.-W.,& Shin, H.-Y. (2006). A general architecture of mobile social network services. In7th International conference onmobile data management (MDM).

Chen, L., Zeng, W., & Yuan, Q. (2013). A unified framework for recommending items groups and friends in social media environment via mutual resource fusion.Expert Systems with Applications, 40(8), 2889–2903.

Cao, L., & Krumm, J. (2009). From GPS traces to a routable road map. InProceedings of the 17th ACM SIGSPATIAL international conference on advances in geographicinformation systems.

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines.ACMTransactions on Intelligent Systems and Technology, 2(3), 1–27.

Condie, T., Conway, N., Alvaro, P., Hellerstein, J.M., Elmeleegy, K., & Sears, R. (2010).Mapreduce online. InNSDI(pp. 313–328).

Dean, J., & Ghemawat, S. (2010). Mapreduce: A flexible data processing tool.Communication of the ACM, 53(1), 72–77.

Dong, Z. -B., Song, G. -J., Xie, K. -Q. & Wang, J. -Y. (2009). An experimental study of large-scale mobile social network. In Proceedings of the 18th internationalconference on world wide web (WWW ’09)(pp. 1175–1176).

Franklin, M. J., Kossmann, D., Kraska, T., Ramesh, S., & Xin, R. (2011). Crowddb:answering queries with crowdsourcing. InSIGMOD conference(pp. 61–72).

Getoor, L., & Diehl, C. P. (2005). Link mining: A survey.SIGKDD Explorer Newsletter,7(2), 3–12.

Ilyas, I. F., Beskales, G., & Soliman, M. A. (2008). A survey of top-k query processing techniques in relational database systems. Computing Survey, 40(4),11:1–11:58


Refbacks

  • There are currently no refbacks.


barcodeInternational Journal of Informatics and Information Systems (IJIIS)
ISSN: 2579-7069 (online)
Organized by Information System Department - Universitas Amikom Purwokerto - Indonesia, Laboratoire Signaux Et Systèmes (L2s) - Université Paris 13 - France, and Bright Publisher
Published by Bright Publisher
Website : http://ijiis.org
Email : info@ijiis.orgtaqwa@ijiis.org, andhika@ijiis.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0