MONITORING DASHBOARD USING LINEAR REGRESSION FOR EMPLOYEE PERFORMANCE
DOI:
10.54443/ijset.v4i11.1101Published:
2025-09-07Downloads
Abstract
Company management needs to continuously monitor and measure the performance of its employees to ensure the achievement of the goals that have been set. The performance monitoring process requires data and information obtained from 35 employees. The problem is in the process of the employee payroll system, attendance, leave and the main thing is data management for monitoring employees still use conventional method by manual. The results of performance monitoring will then be conveyed to interested parties, efficiently and effectively. After that, the existing data were analyzed using SPSS which stands for Statistical Product and Service Solution. Validity testing can also be done using SPSS and produces a validity test of the data, which is < 0.05 so it is valid. The reliability test of the data is > 0.70, 0.751 for employee salaries and 0.757 for employee performance so it is reliable. The normality test of the data are > 0.05, 0.077 for employee salaries and 0.059 for employee performance so that the data is normally distributed. The linearity test of the data is 0.604 > 0.05, it can be concluded that there is a linear relationship between salary and employee performance. Regression analysis test simple linear data from the data, namely the significance level of 0.001 < 0.05 then the regression model can be used to predict the participation variable or in other words there is an effect of the salary variable on the performance variable.
Keywords:
dashboard employee performance monitoring intelligent business linear regression SPSSReferences
D. Lestari, “Perancangan Sistem Informasi Penggajian Karyawan Pada PR. Tunas Mandiri Kabupaten Pacitan,” Peranc. Sist. Inf. Penggajian Karyawan Pada PR. Tunas Mandiri Kabupaten Pacitan, vol. 3, no. 4, 2014.
L. P. Handho dan S. D. Purnamasari, “Dasboard Monitoring Mahasiswa Dan Lulusan Untuk Meningkatkan Potensi Penerimaan Mahasiswa Baru Serta Strategi Pemasaran,” J. Comput. Inf. Syst. Ampera, vol. 1, no. 2, 2020, doi: 10.51519/journalcisa.v1i2.37.
I. G. P. Kawiana, Manajemen Sumber Daya Manusia, “MSDM” Perusahaan, vol. 4, no. 3. 2020.
E. Turban, R. Sharda, dan J. Aronson, “Business intelligence: a managerial approach,” Tamu-Commerce.Edu, 2008.
S. Few, “Information Dashboard Design,” Eff. Vis. Commun. data Sebastopol, 2006, Diakses: Des 21, 2010. [Daring]. Tersedia pada: http://www.mendeley.com/research/information-dashboard-design/.
Imelda, “Businnes Intelligence,” Bisnis Intell., vol. 11, no. Bisnis Intellijen, hal. 111–122, 2008, [Daring]. Tersedia pada: https://jurnal.unikom.ac.id/jurnal/business-intelligence.3c/09-miu-11-1-imelda.pdf.
dursun delen Sharda, Ramesh, “Ramesh Sharda, Dursun Delen, Efraim Turban - Business Intelligence, Analytics, and Data Science_ A Managerial Perspective-Pearson (2017).pdf.” hal. 515, 2018.
A. R. Barlan, M. Laekkeng, dan R. Sari, “Pengaruh Sanksi Perpajakan, Tingkat Pendapatan, Dan Pengetahuan Pajak Terhadap Kepatuhan Wajib Pajak Kendaraan Bermotor Di Kantor Samsat Kabupaten Polewali Mandar,” J. Ekon. dan Bisnis Islam, vol. 6, no. 2, 2021.
I. Nazaruddin dan E. Fatmaningrum, “Analisis Statistik Dengan SPSS,” Anal. Stat. Ekon. dan Bisnis Dengan SPSS, hal. 100–105, 2021.
N. M. Janna, “Konsep Uji Validitas dan Reliabilitas dengan Menggunakan SPSS,” Artik. Sekol. Tinggi Agama Islam Darul Dakwah Wal-Irsyad Kota Makassar, no. 18210047, hal. 1–13, 2020.
W. V. Sujarweni dan L. R. Utami, “The Master Book of SPSS,” Anak Hebat Indones., vol. 03, no. 2016, 2019.
Maryadi, “PENGARUH GAJI, BONUS, DAN FASILITAS TERHADAP MOTIVASI KERJA KARYAWAN PADA PT. BANK SULSELBAR KANTOR PUSAT MAKASSAR,” Gema Kampus IISIP YAPIS Biak, vol. 11, no. 1, hal. 11–21, Apr 2016, doi: 10.52049/gemakampus.v11i1.13.
M. S. P. Hasibuan, “Manajemen Sumber Daya Manusia,” Ed. Revisi Jakarta Bumi Aksara, 2011.
A. Rusydi dan M. Fadhli, Statistika Pendidikan: Teori dan Praktik Dalam Pendidikan. 2018.
R. Tanamal, “What is the most influential factor on decisions using youtube as a tool to support buy or sell means? (Case study surabaya city and surrounding area),” J. Theor. Appl. Inf. Technol., vol. 97, no. 20, 2019.
S. Siregar, Statistika Terapan Untuk Perguruan Tinggi: Edisi Pertama. 2017.
M. A. Oktaviani dan H. B. Notobroto, “Perbandingan Tingkat Konsistensi Normalitas Distribusi Metode Kolmogorov-Smirnov, Lilliefors, Shapiro-Wilk, dan Skewness-Kurtosis,” J. Biometrika dan Kependud., vol. 3, no. 2, 2014.
Sugiyono dan A. Susanto, “Cara Mudah Belajar SPSS dan LISREL: Teori dan Aplikasi untuk Analisis Data Penelitian,” in Cara Mudah Belajar SPSS dan LISREL: Teori dan Aplikasi untuk Analisis Data Penelitian, 2015.
I. M. Yuliara, “Modul Regresi Linier Sederhana,” Univ. Udayana, 2016.
Robbins, “Perilaku Organisasi: Konsep, Kontroversi dan Aplikasi. Jiilid 1,” in Jakarta: Prenhallindo. Stephen,L., 2007.
A. P. Utomo, N. Mariana, dan R. S. A. Rejeki, “RANCANGAN DASHBOARD KINERJA LAYANAN PASIEN RUMAH SAKIT,” Dinamik, vol. 22, no. 2, hal. 57–66, Jul 2017, doi: 10.35315/dinamik.v22i2.7107.
Aziz I., A. Z. Fanani, and A. Affandy, “Analisis Metode Ensemble Pada Klasifikasi Penyakit
Jantung Berbasis Decision Tree,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 1–12,
, doi: 10.30865/mib.v7i1.5169.
R. Nurcahyo, A. Z. Fanani, A. Affandy, and M. I. Aziz, “Peningkatan Algoritma C4. 5
Berbasis PSO Pada Penyakit Kanker Payudara,” J. Media Inform. Budidarma, vol. 7, no.
, pp. 1758–1765, 2023, doi: 10.30865/mib.v7i4.6841.
N. Yannuansa, M. Safa’udin, and M. I. Aziz, “Pemanfaatan Algoritma K-Means Clustering
dalam Mengolah Pengaruh Hasil Belajar Terhadap Pendapatan Orang Tua Pada Mata
Pelajaran Produktif,” J. Tecnoscienza, vol. 6, no. 1, pp. 43–55, 2021, doi:
51158/tecnoscienza.v6i1.530.
License
Copyright (c) 2025 Muhammad Oktoda Noorrohman, Mochammad Ilham Aziz, Saifulloh Azhar, Satria Pradana Rizki Yulianto, Widodo, Devi Ratnasari , Fatika La Viola Ifanka , Melvien Zainul Asyiqien
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
