Kombinasi Metode Pembobotan Rank Reciprocal dan TOPSIS dalam Seleksi Penerimaan Staff IT
Abstract
The selection of IT staff recruitment is a critical stage in forming a competent team and in accordance with the needs of the organization in the era of information technology that continues to grow. This process involves an in-depth evaluation of the prospective employee's technical skills, work experience, and interpersonal abilities. One of the problems that occurs is a lack of technical skills that match the specific needs of the company or project. In addition, intense competition in the IT industry often leads to difficulties in finding candidates who meet the criteria with high qualifications. With this study, it will provide recommendations in the IT staff recruitment selection process by applying the reciprocal rank weighting method for criteria weighting and the TOPSIS method for the IT staff recruitment selection process which will produce rankings in the IT staff recruitment selection. The ranking results showed that the final results of candidate Fransiska with a total value of 0.791 got rank 1, candidate Fandi with a total value of 0.5366 got rank 2, candidate Arini with a total value of 0.533 got rank 3.
References
W. I. Safitri, M. Mesran, and S. Sarwandi, “Penerapan Metode Preference Selection Index (PSI) Dalam Penerimaan Staff IT,” Bull. Informatics Data Sci., vol. 1, no. 1, pp. 1–5, 2022.
F. S. Mawinar, R. D. Gunawan, and A. T. Priandika, “Sistem Pendukung Keputusan Pemilihan Pegawai Honorer Terbaik Menggunakan Metode Visekriterijumsko Kompromisno Rangiranje,” J. Data Sci. Inf. Syst., vol. 1, no. 4, pp. 182–191, 2023.
Z. Yani, D. G. Gusmita, and N. Pohan, “Sistem pendukung keputusan penerimaan karyawan menggunakan metode topsis,” J. Sci. Soc. Res., vol. 5, no. 2, pp. 205–210, 2022.
M. F. Rozi, E. Santoso, and M. T. Furqon, “Sistem Pendukung Keputusan Penerimaan Pegawai Baru menggunakan Metode AHP dan TOPSIS,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 3, no. 9, pp. 8361–8366, 2019.
H. Sulistiani, Setiawansyah, P. Palupiningsih, F. Hamidy, P. L. Sari, and Y. Khairunnisa, “Employee Performance Evaluation Using Multi-Attribute Utility Theory (MAUT) with PIPRECIA-S Weighting: A Case Study in Education Institution,” in 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS), 2023, pp. 369–373. doi: 10.1109/ICIMCIS60089.2023.10349017.
G. R. Putra, “Sistem Rekomendasi Pemilihan Smartphone Gaming Menggunakan Metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS),” J. Ilm. Comput. Sci., vol. 1, no. 1, pp. 41–48, 2022, doi: 10.58602/jics.v1i1.5.
L. G. Ramón‐Canul et al., “Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques,” J. Sci. Food Agric., vol. 101, no. 8, pp. 3298–3307, 2021.
Y. Çelikbilek and F. Tüysüz, “An in-depth review of theory of the TOPSIS method: An experimental analysis,” J. Manag. Anal., vol. 7, no. 2, pp. 281–300, 2020.
A. D. Wahyudi, “Penentuan Lokasi Gudang Baru Menggunakan TOPSIS dan Pembobotan PIPRECIA,” J. Inf. Technol. Softw. Eng. Comput. Sci., vol. 2, no. 1, pp. 22–30, 2024.
H. Sulistiani, U. Adji, and S. Maryana, “Sistem Pendukung Keputusan Dalam Memilih Bibit Kedelai Menggunakan Kombinasi Metode TOPSIS dan ROC,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 3, pp. 1381–1389, 2023.
Setiawansyah, A. A. Aldino, P. Palupiningsih, G. F. Laxmi, E. D. Mega, and I. Septiana, “Determining Best Graduates Using TOPSIS with Surrogate Weighting Procedures Approach,” in 2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT), 2023, pp. 60–64. doi: 10.1109/IConNECT56593.2023.10327119.
T. T. Swastika, D. A. Prastiningtyas, and L. Isyriyah, “Sistem Penunjang Keputusan Pemilihan Perumahan Terbaik Menggunakan Metode TOPSIS Berbasis GIS,” J-INTECH (Journal Inf. Technol., vol. 10, no. 2, pp. 82–89, 2022.
S. Corrente and M. Tasiou, “A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria,” Expert Syst. Appl., vol. 214, p. 119045, 2023.
X. Lu, J. Wu, and J. Yuan, “Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, pp. 2236–2240.
U. Hairah and E. Budiman, “Kinerja Metode Rank Sum, Rank Reciprocal dan Rank Order Centroid Menggunakan Referensi Poin Moora (Studi Kasus: Bantuan Kuota Data Internet untuk Mahasiswa),” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 6, pp. 1129–1136, 2022.
L. Azzopardi, J. Mackenzie, and A. Moffat, “ERR is not C/W/L: Exploring the relationship between expected reciprocal rank and other metrics,” in Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, 2021, pp. 231–237.
S. Setiawansyah, “Kombinasi Pembobotan PIPRECIA-S dan Metode SAW dalam Pemilihan Ketua Organisasi Sekolah,” J. Ilm. Inform. dan Ilmu Komput., vol. 2, no. 1, pp. 32–40, 2023.
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