Implementasi Metode MABAC dengan Pembobotan Entropy Dalam Sistem Pendukung Keputusan Proses Rekruitmen CIO
DOI:
https://doi.org/10.30865/klik.v4i4.1736Keywords:
MABAC Method; Entropy Method; DSS; CIOAbstract
This research aims to investigate and implement the MABAC (Multi-Attributive Border Approximation) method with weighting based on entropy in a decision support system (DSS) to recommend the most suitable Chief Information Officer (CIO) candidate. As an integral part of upper-level management, the selection of a CIO is a strategic decision that has a major impact on organizational performance. Therefore, the use of SPK in the CIO recruitment process can help organizations make more effective and rational decisions. The MABAC method is used because it is able to overcome situations of uncertainty and ambiguity in CIO recruitment decision making, which involves several interrelated attributes and has the potential to influence CIO performance. Entropy-based weighting is used to measure the level of uncertainty or randomness in attribute data, thereby providing accurate weights for each attribute in the decision-making process. This research uses empirical data from a number of CIO candidates who have various backgrounds and experiences. The data includes a variety of attributes such as education, work experience, technical expertise, and leadership skills. The MABAC method with entropy weighting was used to process this data, and the results were used to produce a ranking of the most suitable CIO candidates. The results of this research indicate that the use of the MABAC method with entropy weighting in the SPK can produce more accurate and reliable CIO recommendations. These results can assist recruiting teams and senior management in selecting the CIO who best fits the organization's needs. Apart from that, this research also identifies the attributes that are most influential in CIO recruitment decisions, so that it can help organizations in designing appropriate skills development or training programs for selected CIO candidates. Thus, this research makes an important contribution to the field of HR management and strategic decision making, especially in the context of CIO recruitment. The results of calculations in this research show that the alternative in the name of Eric Yahya is in first place with a value of 0.405. The results can be a valuable guide for organizations looking to improve their executive selection and recruitment processes
Downloads
References
Y. W. Yoddy Wahyudi, “CHIEF INFORMATION OFFICER SEBAGAI PENGENDALI SISTEM MONITORING DI LABORATORIUM KOMPUTER SEKOLAH MENENGAH ATAS PGRI 2 PALEMBANG,” CHIEF Inf. Off. SEBAGAI PENGENDALI Sist. Monit. DI Lab. Komput. Sekol. MENENGAH ATAS PGRI 2 PALEMBANG, 2022.
A. Nurunnisa, “ANALISIS PERAN GOVERNMENT CHIEF INFORMATION OFFICER (GCIO) PADA DINAS KOMUNIKASI, INFORMATIKA, STATISTIK DAN PERSANDIAN KABUPATEN KARIMUN.” IPDN, 2023.
D. Pribadi, R. A. Saputra, and J. M. Hudin, “Sistem Pendukung Keputusan,” 2020.
D. O. Wibowo and A. T. Priandika, “Sistem Pendukung Keputusan Pemilihan Gedung Pernikahan Pada Wilayah Bandar Lampung Menggunakan Metode Topsis,” J. Inform. Dan Rekayasa Perangkat Lunak, vol. 2, no. 1, pp. 73–84, 2021.
R. D. Kurniawati and I. Ahmad, “Sistem Pendukung Keputusan Penentuan Kelayakan Usaha Mikro Kecil Menengah Dengan Menggunakan Metode Profile Matching Pada Uptd Plut Kumkm Provinsi Lampung,” J. Teknol. Dan Sist. Inf., vol. 2, no. 1, pp. 74–79, 2021.
R. K. Hondro, “MABAC: Pemilihan Penerima Bantuan Rastra Menggunakan Metode Multi-Attributive Border Approximation Area Comparison,” J. Mahajana Inf., vol. 3, no. 1, pp. 41–52, 2018.
R. K. Ndruru and D. P. Utomo, “Sistem Pendukung Keputusan Penilaian Kinerja Generik Anggota Polri Di Polda Sumatera Utara Menggunakan Metode MABAC & Entropy,” Konf. Nas. Teknol. Inf. dan Komput., vol. 4, pp. 303–310, 2020, doi: 10.30865/komik.v4i1.2710.
S. A. Panjaitan, “Sistem Pendukung Keputusan Perekrutan Internal Audit Officer (Audit) Menerapkan Kombinasi Metode AHP dan MABAC,” TIN Terap. Inform. Nusant., vol. 2, no. 12, pp. 710–720, 2022.
M. A. Rupang and A. Kusnadi, “Implementasi Metode Entropy dan Topsis Dalam Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik,” Ultim. Comput. J. Sist. Komput., vol. 10, no. 1, pp. 13–18, 2018.
C. A. D. Kirana and A. S. Harahap, “Pendukung Keputusan dalam Penilaian Pegawai Pemerintah Non Pegawai Negeri menggunakan Metode Entropy,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 1, pp. 159–166, 2022.
G. S. Mahendra, P. Nugraha, I. P. Y. Indrawan, and I. M. S. Ramayu, “Implementasi Pemilihan Maskapai Penerbangan Menggunakan FUCOM-MABAC pada Sistem Pendukung Keputusan,” SmartAI J., vol. 1, no. 1, pp. 11–22, 2022.
S. Setiawansyah, “Sistem Pendukung Keputusan Rekomendasi Tempat Wisata Menggunakan Metode TOPSIS,” J. Ilm. Inform. dan Ilmu Komput., vol. 1, no. 2, pp. 54–62, 2022.
N. Agustina and E. Sutinah, “Penerapan Metode MOORA Pada Sistem Pendukung Keputusan Pemilihan Aplikasi Dompet Digital,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 6, no. 2, pp. 300–304, 2022.
I. Susilawati and P. Pristiwanto, “Sistem Pendukung Keputusan Pemilihan Pekerja Buruh Harian Lepas Dengan Menggunakan Metode Waspas (Studi Kasus: PT. Socfin Indonesia),” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 5, no. 1, 2021.
G. S. Mahendra et al., Buku Ajar Sistem Pendukung Keputusan. PT. Sonpedia Publishing Indonesia, 2023.
R. T. Aldisa, “Penerapan Metode MABAC dalam Sistem Pendukung Keputusan Rekomendasi Aplikasi Pemesanan Hotel Terbaik,” J. Inf. Syst. Res., vol. 4, no. 1, pp. 191–201, 2022.
F. Nugroho, A. Triayudi, and M. Mesran, “Sistem Pendukung Keputusan Rekomendasi Objek Wisata Menerapkan Metode MABAC dan Pembobotan ROC,” J. Sist. Komput. dan Inform., vol. 5, no. 1, pp. 112–121, 2023.
D. W. Sipahutar and M. Mesran, “Sistem Pendukung Keputusan Pemilihan Teknisi Broadcasting Pada TVRI Medan Menerapkan Metode MABAC,” JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 2, pp. 55–63, 2021.
A. Ahyuna, B. Rahman, F. Nugroho, I. W. S. Nirawana, and A. Karim, “Analisa Penerapan Metode MABAC dengan Pembobotan Entropy dalam Penilaian Kinerja Dosen di Era Society 5.0,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 29–39, 2023.
S. R. Tanjung, M. Mesran, S. Sarwandi, and M. V Siagian, “Penerapan Metode COPRAS dan ENTROPY dalam Pemilihan Anggota Badan Pengawas Pemilihan Umum (BAWASLU),” J. Informatics Manag. Inf. Technol., vol. 1, no. 2, pp. 48–59, 2021.
M. P. Hasibuan and M. D. Irawan, “Penerapan Metode Entropy dan MOORA Dalam Pemilihan Investasi Saham LQ45 Berbasis Keputusan,” Resolusi Rekayasa Tek. Inform. dan Inf., vol. 3, no. 5, pp. 355–363, 2023.
A. Ernawati, A. O. Sari, S. N. Sofyan, A. Aulia, and Z. Sitorus, “Implementasi Sistem Pendukung Keputusan dalam menentukan Kecamatan Terbaik Menggunakan Algoritma Entropy dan Additive Ratio Assessment (ARAS),” Bull. Inf. Technol., vol. 4, no. 4, pp. 488–500, 2023.
A. Ernawati, “Penerapan Algoritma Entropy Dan Aras Menentukan Penerima Beasiswa Mahasiswa Berprestasi Di Pemerintah Kabupaten Labuhanbatu,” Bull. Inf. Technol., vol. 3, no. 2, pp. 74–84, 2022.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Metode MABAC dengan Pembobotan Entropy Dalam Sistem Pendukung Keputusan Proses Rekruitmen CIO
ARTICLE HISTORY
Issue
Section
Copyright (c) 2024 Siti Emalia Saqila, Amelia Hayatul, Agus Iskandar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
