Recommendation System for Major University Determination Based on Student’s Profile and Interest

Desi Purwanti Kusumaningrum, Noor Ageng Setiyanto, Erwin Yudi Hidayat, Khafiizh Hastuti

Abstract


Failure study on university students is one of the serious problems we face today. Data from the Centre for Education Statistics Research and Development of the Ministry of National Education Republic of Indonesia showed that the percentage of students graduate on time from 2001 to 2011 only reached 51.97%. In addition, cases of students dropping out at the beginning of the semester is also quite significant. One of the causes of failure of this study was the selection of major’s errors when applying to university. This study offers a selection subject recommendation system that builds on the profile data and student’s interest using the technique of Association Rule. Results of the rules of the relationship will then be matched with prospective students using questionnaires dynamic, so expect new students get recommendations more valid subject fit the profile and interest respectively.

The system built on this research utilizes student data stored on the academic system of Dian Nuswantoro University. This model however can be adapted by all the universities that has a system of academic information. At the end of this system is expected to be used to minimize failures caused students study majors election mistakes


Full Text:

PDF

References


Hastuti, K., 2012. Analisis Komparasi Algoritma Klasifikasi Data Mining until Prediksi

Mahasiswa Notif. Seminar Nasional Teknologi Informasi & Komunikasi Terapan (Semantic)

Khoirunnisak, M., 2011. Pemodelan Faktor-Faktor yang Mempengaruhi Mahasiswa

Berhenti Studi (Drop Out) di Institut Teknologi Sepuluh Nopember Surabaya

Menggunakan Analisis Bayesian Mixture Survival. Skripsi S1 Statistika Institut Teknologi

Sepuluh November

DO Akibat Salah Jurusan, 2013, dikes pada 10 Juni 2013,

http://edukasi.kompas.com/read/2010/11/10/05360848/DO.akibat.Salah.Jurusan

Awas, Salah Jurusan Fatal Akibatnya!, 2013, accessed on 11 Juni 2013

http://campuslifemagz.beritasatu.com/landing.php?kategori=study&id=388

El-Haley, A., 2008. Mining Students Data to Analyze Learning Behavior: A Case Study. In

Proceeding of The 2008 International Arab Conference on Information Technology

(ACIT2008)

Witten, I. H., Frank, E., & Hall, M. A., 2011. Data Mining: Practical Machine Learning Tools

and Techniques. Morgan Kaufmann

Huang, Z., Zeng, D., & Chen, H., 2006. A Comparative Study of Recommendation

Algorithms for E-Commerce Applications. IEEE Intelligent Systems

Özseyha, C., Badur, B., & Darcan, O. N., 2012. An Association Rule-Based

Recommendation Engine for an Online Dating Site. Communications of the IBIMA

Mican, D., & Tomai, N., 2010. Association-Rules-Based Recommender System for

Personalization in Adaptive Web-Based Applications, 10th International Conference on

Web Engineering ICWE. Springer-Verlag

Smith, B., McCarthy, K., Reilly, J., O’Sullivan, D., McGinty, L., & Wilson, D. C., 2005. Case

Studies in Association Rule Mining for Recommender Systems. In Proc. of International

Conference on Artificial Intelligence (ICAI’05)

Lin, W., Alvarez, S. A., & Ruiz, C., 2000. Collaborative Recommendation via Adaptive

Association Rule Mining. In Proceedings of the International Workshop on Web Mining for

E-Commerce (WEBKDD)

Nurfitriana, N., Darmawan, I., & Akbar, R. E., 2012. Rancang Bangun Aplikasi Sistem Pakar

Berbasis Web untuk Rekomendasi Pemilihan 12 Jurusan di Universitas Siliwangi. Jurnal e-

Informatika Siliwangi vol. 1, no. 1, 2012

Diahpangastuti, N., 2012. Sistem Rekomendasi Bidang Minat Siswa dengan Metode

Association Rule dan Algoritma Apriori. Skripsi S1 Teknik Informatika FTIF Institut

Teknologi Sepuluh November

Marquez-Vera, C., Romero, C., & Ventura, S., 2011. Predicting School Failure using Data

Mining. In Proceedings of the 4th International Conference on Educational Data Mining

(pp. 271-276)

Kotsiantis, S., 2010. Educational Data Mining: A Case Study for Predicting Dropout-Prone

Students. Int. J. of Knowledge Engineering and Soft Data Paradigms, (pp 101-111)

Kovacic, Z. J., 2010. Early Prediction of Student Success: Mining Students Enrolment Data.

In Proceedings of Informing Science & IT Education Conference (InSITE) (pp. 647-665)




DOI: https://doi.org/10.33633/jais.v2i1.1389

Article Metrics

Abstract view : 987 times
PDF - 911 times

Refbacks

  • There are currently no refbacks.


Flag Counter

 

 

 

 

Journal of Applied Intelligent System (e-ISSN : 2502-9401, p-ISSN : 2503-0493) is published by Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS.

  

 

Journal of Applied Intelligent System indexed by :


This journal is under licensed of Creative Commons Attribution 4.0 International License.

Visitor Stats