A multi-study program recommender system using ELECTRE multicriteria method

A multi-study program recommender system using ELECTRE multicriteria method

• By Marlinda, Linda; Baidawi, Taufik; Durachman, Yusuf; Akmaludin;
• Seminar Internasional
• 460
• 273-277

Selection of study program plays an important role in the success of a person to determine his future. One of the risks associated with the selection of study is the incompatibility with the needs of the current job vacancies in companies that significantly affect the future of these students. Since there are many criteria that must be considered, then through this recommender system, students are able to know what fields are the most appropriate for them. This system is built based on Electre method. When a student fills out a questionnaire, he must be consistent with his/her answer to obtain the best output based on his/her will and characteristics. This research uses descriptive analytical method and presents a summary of the results of surveys and interviews of 310 colleges in accordance with the codification which connect with Job Career so it can be a reference to prospective students in finding employment in the future company.

• Katakunci Multi-Study Program, Recommender System, ELECTRE, Multicriteria
• URL https://ieeexplore.ieee.org/abstract/document/8089271
• Penerbit Jurnal Aktiva- CMED STIE Kusuma Negara, Jakarta Timur DKI Jakarta

REFERENSI

[1] Marlinda, Linda., et al, “A Multi Study Program Recommender System Using Content-Based Filtering and Analytical Hierarchy process (AHP) Methods”, IJCSI International journal Of Computer ScienceIssue 3. No 2, May 2012. ISSN (Online): 1694-0814. [2] Marlinda, Linda., “Sistem Pendukung Keputusan Pemilihan Tempat Wisata Yogyakarta Menggunakan Metode Elimination Et Choix Traduisant La Realita (ELECTRE) “, Prosiding Semnastek 2016. P-ISSN 2407-1846, E-ISSN 2460-8416 [3] Adomavicius, G., et al. “Toward the Next Generation of Recommender Systems: A Survey of the State of the Art and Possible Extensions”, 2005, IEEE Transactions on Knowledge and Data Engineering. [4]Manouselis, N., et al, “Analysis and Classification of Multi-Criteria Recommender System”. World Wide Web: Internet and Web Information Systems, Special Issue on Multi-channel Adaptive Information System on the World Wide Web. 2007, Springerlink [5] Montainer, M., et al, “A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence”, Kluwer Academic Publisher. Netherlands, 2003, Review 19: 285-330. [6] Burke, R. “Hybrid Recommender Systems: Survey and Experiments”. User Adapt. Inter, 2003, Vol. 12 pp. 331-370[7] Ziller, Annette, Michaela Wörndl, and Andrea Bichler, 2008, Multi_criteria_decision_making_Ju ne_2008_ [8] Janko, Wolfgang dan Bernroider, Edward, “ Multi-Criteria Decision Making An Application Study of ELECTRE & TOPSIS”. 2005. [9] Fulop, J. “Introduction to Decision Making Methods”, Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences, 2007. [10] Marlinda. “Sistem Basis Data”. Andi Offset Yogyakarta, Indonesia, 2004. [11] Triantaphyllou, E., et al. “A Computational Evaluation of The Original and Revised Analytic Hierarchy Process”, Computer ind. Engng Vol. 26, No.3, pp.609-618. Elsevier Science Ltd, 1994. [12] Montainer, M., et al, “A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence”, Kluwer Academic Publisher. Netherlands, Review 19: 285-330, 2003.