Implementasi Metode AHP dan MOORA dalam Penentuan Pemberian Bonus Triwulan Berdasarkan Penilaian Kinerja Guru
DOI:
https://doi.org/10.30865/resolusi.v4i3.1491Keywords:
AHP; MOORA; DSS; Rewards; PerformanceAbstract
A Decision Support System is a technology built to assist an organization or institution in making decisions. Mardi Waluya Elementary School is an educational institution in which there is a decision-making process for determining the provision of quarterly bonuses to teachers based on the results of performance assessments. However, in the decision making process several problems were found, namely that it was still carried out conventionally in determining quarterly bonuses, which caused the determination process to not be transparent and accountable. Apart from that, the reporting of the results of recommendations for teachers receiving quarterly bonuses carried out by assessors was not effective in its delivery because it was slow. So school principals need technology in the form of a decision support system, which is transparent, accountable, effective and efficient in providing recommendations for teachers who will receive quarterly bonuses. In this research, a decision support system will be built that applies the Analytic Hierarchy Process (AHP) method and the Multi-Objective Optimization by Ratio Analysis (MOORA) method. The AHP method is used to obtain consistent weights, while the MOORA method is used to rank teacher performance assessments, so that determining bonuses in teacher performance assessments can be more effective, efficient and accountable. The implementation of the AHP and MOORA methods in this decision support system is built on a website basis. It is hoped that the results of this research will make it easier for school principals to determine the provision of quarterly bonuses to teachers who have good performance, so that the results of the decision are given to the teachers who are entitled to them.
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T. Kurnialensya and Y. Fitriyanto, “Sistem Pendukung Keputusan Penerimaan Karyawan Menggunakan Metode SAW (Simple Additive Weighting),” Acad. J. Comput. Sci. Res., vol. 2, no. 2, pp. 118–128, 2020, doi: 10.38101/ajcsr.v2i2.286.
S. Nurajizah, N. A. Ambarwati, and S. Muryani, “Sistem Pendukung Keputusan Pemilihan Internet Service Provider Terbaik Dengan Metode Analytical Hierarchy Process,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 6, no. 3, pp. 231–238, 2020, doi: 10.33330/jurteksi.v6i3.632.
B. R. Putra and A. Diana, “Rancang Bangun Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Dengan Metode Analytical Hierarchy Process (AHP) Pada Rumah Makan Ciganea Pusat,” J. Perad. Sains, Rekayasa Dan Teknol., vol. 9, no. 2, pp. 250–264, 2021, doi: 10.47065/tin.v2i8.1261.
N. P. D. Prabawati, “Peran Pemuda Dalam Kegiatan Pengembangan Pariwisata Di Desa Tibubeneng, Kabupaten Badung, Bali,” J. Kepariwisataan Indones. J. Penelit. dan Pengemb. Kepariwisataan Indones., vol. 13, no. 1, pp. 73–84, 2019, doi: 10.47608/jki.v13i12019.73-84.
Haryani and D. Fitriani, “Sistem Pendukung Keputusan Penentuan Karyawan Terbaik Pada Collection Pt . Panin Bank Menggunakan,” J. Mantik Penusa, vol. 3, no. 1, pp. 1–8, 2019.
Fachruddin, M. R. Pahlevi, M. Ismail, E. Rasywir, and Y. Pratama, “Analisis Usability Pada Implementasi Sistem Pengelolaan Keuangan Masjid Menggunakan USE Questionnaire,” J. Media Inform. Budidarma, vol. 4, pp. 1216–1224, 2020, doi: 10.30865/mib.v4i4.2518.
A. Hernandes, H. Hartini, and D. Sartika, “Steganografi Citra Menggunakan Metode Least Significant Bit (LSB) dan Linear Congruential Generator (LCG),” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 5, no. 2, pp. 137–146, 2019, doi: 10.35957/jatisi.v5i2.134.
R. Ekasari, M. S. Pradana, G. Adriansyah, M. A. Prasnowo, A. F. Rodli, and K. Hidayat, “Analisis Kualitas Pelayanan Puskesmas Dengan Metode Servqual,” J. Darussalam J. Pendidikan, Komun. dan Pemikir. Huk. Islam, vol. 9, no. 1, p. 82, 2017, doi: 10.30739/darussalam.v9i1.118.
P. P. Nicolas, H. Soetanto, W. Wahyudi, and A. Rossi, “Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik pada PT. XYZ dengan Metode Profile Matching dan Interpolasi,” J. Sist. dan Teknol. Inf., vol. 9, no. 2, p. 121, 2021, doi: 10.26418/justin.v9i2.44159.
D. W. Sitohang and A. Rikki, “Implementasi Algoritma K- Means Clustering untuk Mengelompokkan Data Gizi Balita pada Kecamatan Garoga Tapanuli Utara,” KAKIFIKOM (Kumpulan Artik. Karya Ilm. Fak. Ilmu Komputer), vol. 02, pp. 80–92, 2019, doi: 10.54367/kakifikom.v1i2.642.
W. Julianto, R. Yunitarini, and M. K. Sophan, “Algoritma C4.5 Untuk Penilaian Kinerja Karyawan,” Scan, vol. Vo. IX, no. No. 2, pp. 33–39, 2014.
G. A. Gulo, N. W. Nurhidayati, D. Aprillia, and M. Maruloh, “Sistem Penunjang Keputusan Pemilihan Karyawan Terbaik Di Restoran Soto Pak J Menggunakan Metode Ahp,” J. Larik Ldng. Artik. Ilmu Komput., vol. 1, no. 1, pp. 11–20, 2021, doi: 10.31294/larik.v1i1.504.
A. Muhammad Azizi, P. Korespondensi, and F. Ariany, “Sistem Informasi Pengajuan Cuti Pegawai Menggunakan Metode Pengujian Iso 25010 (Study Kasus?: Pt Mutiara Ferindo Internusa),” vol. 4, no. 3, pp. 326–334, 2023, [Online]. Available: https://doi.org/10.33365/jatika.v4i3.3721.
Sugiyono, “Prof. Dr. Sugiyono. 2018. Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: Alfabeta.,” Prof. Dr. Sugiyono. 2018. Metod. Penelit. Kuantitatif, Kualitatif, dan R&D. Bandung Alf., 2018.
I. G. A. Sudibya and I. W. M. Utama, “Pegawai Di Lingkungan Kantor Dinas Pekerjaan Umum,” in Manajemen, Strategi Bisnis, dan Kewirausahaan, 2013, vol. 6, no. 2, pp. 173–184.
M. Angeline and F. Astuti, “Rancang Bangun Sistem Pendukung Keputusan Pemilihan Pramuwisata Menggunakan Metode Profile Matching,” J. Ilm. SMART, vol. II, no. 2, pp. 45–51, 2018.
R. Ramadiani and A. Rahmah, “Sistem pendukung keputusan pemilihan tenaga kesehatan teladan menggunakan metode Multi-Attribute Utility Theory,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 5, no. 1, p. 1, 2019, doi: 10.26594/register.v5i1.1273.
D. Levia and Mhubaligh, “Analisis Proses Produksi CPO Untuk Mengidentifikasi Faktor-Faktor Yang Mempengaruhi Kualitas Mutu CPO,” J. Teknol. dan Manaj. Ind. Terap., vol. 2, no. 2, pp. 82–89, 2023, doi: 10.55826/tmit.v2i2.72.
E. Rasywir, R. Sinaga, and Y. Pratama, “Evaluasi Pembangunan Sistem Pakar Penyakit Tanaman Sawit dengan Metode Deep Neural Network ( DNN ),” vol. 4, pp. 1206–1215, 2020, doi: 10.30865/mib.v4i4.2518.
E. Rasywir, R. Sinaga, and Y. Pratama, “Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network ( CNN ),” J. Paradig. UBSI, vol. 22, no. 2, pp. 117–123, 2020.
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