EVALUASI KINERJA DENGAN METODE SIMPLE ADDITIVE WEIGHTING UNTUK MENGIDENTIFIKASI MEKANIK TERBAIK
Abstract
Evaluasi kinerja adalah penilaian kinerja baik secara kualitas atau kuantitas sesuai jobdesk yang dimiliki oleh karyawan. Pentingnya evaluasi kinerja dalam suatu perusahaan adalah menilai produktifitas atau hasil kinerja karyawan serta bagaimana karyawan ikut berkontribusi dalam mengembangkan dan mensukseskan perusahaan di masa depan. Galuh Motor Group adalah perusahaan yang terus mengembangkan potensi, kemampuan, keahlian dan kesejahteraan mekaniknya melalui evaluasi kinerja sehingga diperoleh mekanik yang terbaik. Namun dalam prosesnya, masih terdapat kecenderungan penilaian secara subjektif dari atasan. Beberapa kriteria yang digunakan antara lain absensi, penjualan sparepart, penjualan nilai jasa, jumlah unit motor yang dikerjakan. Kriteria untuk pemilihan mekanik terbaik dapat ditentukan secara otomatis dengan sistem pendukung keputusan. Sistem ini mengunakan metode SAW (Simple Additive Weighting) yang dapat merekomendasikan mekanik yang terbaik. Hasilnya, seluruh mekanik dilakukan perangkingan sesuai dengan nilai penjumlahan dari perkalian matriks normalisasi dengan vector bobot kriteria, dimana sebelummya masing-masing kriteria diberikan bobot kepentingan dan normalisasi matriks dari masing-masing alternatif. Dalam simulasi ini sistem dapat mengidentifikasikan mekanik terbaik dengan nilai preferensi paling tinggi yaitu 0,9701 oleh A1. Dengan adanya sistem evaluasi kinerja mekanik, diharapkan mampu memberikan rekomendasi bagi perusahaan dalam menentukan mekanik terbaik sesuai hasil kinerjanya secara objektif.References
Aulia Fitrul Hadi, Randy Permana, H. S. (2019). Decision Support System in Determining Structural Position Mutations Using Simple Additive Weighting ( SAW ) Method. International Coference Computer Science and Engineering. https://doi.org/10.1088/1742-6596/1339/1/012015
Badrul, M., & Gultom, R. (2023). Sistem Pendukung Keputusan Pemilihan Mekanik Terbaik Dengan Metode Analytical Hierarchy Process. 7, 158–171.
Irawan, Y. (2020). Decision Support System for Employee Bonus Determination With Web-Based Simple Additive Weighting (Saw) Method in Pt. Mayatama Solusindo. Journal of Applied Engineering and Technological Science, 2(1), 7–13. https://doi.org/10.37385/jaets.v2i1.162
Jayawardani, W. R. K., & Maryam, M. (2022). Sistem Pendukung Keputusan Seleksi Penerima Program Keluarga Harapan dengan Implementasi Metode SAW dan Pembobotan ROC. Emitor: Jurnal Teknik Elektro, 22(2), 99–109. https://doi.org/10.23917/emitor.v22i2.18411
Kusumantara, P. M., Kustyani, M., & Ayu, T. (2019). Pendukung Keputusan Pemilihan Wedding Organizer Di. Teknika Engineering and Sains Journal, 3(I), 19–24.
Librado, D., Prabawa, T., & Triyanto, H. A. (2023). Klasterisasi Penerima Bantuan Sosial Menggunakan Metode Simple Additive Weighting. JIKO (Jurnal Informatika Dan Komputer), 7(1), 30. https://doi.org/10.26798/jiko.v7i1.677
Mahendra, G. S., & Nugraha, P. G. S. C. (2020). Komparasi Metode AHP-SAW dan AHP-WP Pada SPK Penentuan E-Commerce Terbaik di Indonesia. Jurnal Sistem Dan Teknologi Informasi (Justin), 8(4), 346. https://doi.org/10.26418/justin.v8i4.42611
Muryanah, S., Maula, I., & Murniasih, I. (2020). Menyisipkan Pesan Rahasia Kedalam Gambar Dengan Metode Blowfish dan Least Significant Bit (LSB). JIKA (Jurnal Informatika), 4(3), 87–93.
Muslihudin, M., & Hartini, D. (2017). Perancangan Sistem Pendukung Pengambilan Keputusan Untuk Penerimaan Beasiswa Di Sma Pgri 1 Talang Padang Dengan Model Fuzzy Multiple Attribute Menggunakan Metode Simple Additive Weighting (Saw). Jurnal TAM (Technology Acceptance Model), 4(1), 34–40. http://www.ojs.stmikpringsewu.ac.id/index.php/JurnalTam/article/view/35
Oktariany, T. R., Maryaningsih, & Sudarsono, A. (2022). Analisa Metode SAW , WP Dan TOPSIS dalam Menentukan Pegawai Terbaik Dinas Tanaman Pangan Hortikultura. Jurnal Media Computer Science, 1(2), 267–272.
Piasecki, K., & Roszkowska, E. (2019). Simple Additive Weighting Method Equipped with Fuzzy Ranking of Evaluated Alternatives.
Prof. Dr. H. M. Ma’ruf Abdullah, S. M. (2014). Manajemen dan Evaluasi Kinerja Karyawan. In B. R. Hakim (Ed.), Aswaja Pressindo. Aswaja Pressindo. https://idr.uin-antasari.ac.id/5011/1/Manajemen dan Evaluasi Kinerja.pdf
Puspa, M. (2019). Decision Support System For Supplementary Food Recipients (PMT) By Using The Simple Additive Weighting (SAW) Method. Jurnal Teknik Informatika C.I.T, 11(2), 37–44. www.medikom.iocspublisher.org/index.php/JTI
Serelia, E. B., & Adin Saf, M. R. (2020). Sistem Pendukung Keputusan Penentuan Peminatan Siswa Dengan Menggunakan Metode SAW (Simple Additive Weighting) Pada SMA Negeri Dharma Pendidikan. Techno.Com, 19(3), 227–236. https://doi.org/10.33633/tc.v19i3.3498
Shinta Amelia, C. P. (2019). Uji Kinerja Metode Weighted Product Dan Simple Additive Weighting. Tehnik Informatika, 7(2), 1–10.
Silaen, N. R., Syamsuriansyah, & Chaerunnisah, R. (2021). Kinerja Karyawan. In Suparyanto dan Rosad (2015 (Vol. 5, Issue 3). Widina Bhakti Persada Bandung. https://repository.penerbitwidina.com/media/publications/344479-kinerja-karyawan-dab4c13a.pdf
Sry Yunarti, & Moeis, D. (2022). Analisis Metode WP dan SAW melalui Uji Sensitivitas untuk Penentuan Penerima Diakonia. Insect (Informatics and Security): Jurnal Teknik Informatika, 8(1), 48–57. https://doi.org/10.33506/insect.v8i1.1907
Suryadi, S., Ritonga, W. A., Siagian, T. N., Marpaung, M. F. R., Hariyanto, H., Ritonga, S., & Ramadhana, R. S. A. (2022). Uji Sensitivitas Metode Pembobotan ROC, SWARA Terhadap Kriteria Karyawan Terbaik Dengan Menggunakan Metode SAW. Journal of Information System Research (JOSH), 3(4), 532–540. https://doi.org/10.47065/josh.v3i4.1952
Zain, A. S., & Purniawati, R. (2020). Sistem Pendukung Keputusan Penerimaan Siswa Baru dengan Metode Simple Additive Weighting. Sains, Aplikasi, Komputasi Dan Teknologi Informasi, 2(1), 18. https://doi.org/10.30872/jsakti.v2i1.2668
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