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Arif Aulia
Magister Manajemen Institut Teknologi dan Bisnis Haji Agus Salim, Bukittinggi
Economics, Econometrics & Finance Engineering

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Analysis Of Factors Influencing The Performance Of Payakumbuh City Public Housing And Residential Area Employees Using Binary Logistic Regression Edvidel Arda; Arif Aulia; Jerry Heikal
Jurnal Ekonomi dan Bisnis Digital Vol. 1 No. 3 (2024): Januari - Maret
Publisher : CV. ITTC INDONESIA

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Abstract

Housing and Residential Area Service operates efficiently and is able to respond to growing urban challenges. However, in a complex organization like the Department, understanding employee performance and identifying areas that need improvement can be a very complex task. to analyze the factors that influence employee performance at the Public Housing and Settlement Area Service of Payakumbuh City. The method used is binary logistic regression with the dependent variable (Y) being employee performance, while the independent variables are Discipline, Cooperation, Competence, Integrity, Commitment, Performance. From the results of the above analysis there are 2 (two) variables that significantly influence the performance of Housing Department employees People and Residential Areas of Payakumbuh City, namely Competency and Commitment Variables. From the results of binary logistic regression, it is known that there are 36 employees with high performance, while there are 17 employees with low performance.
Employee Performance Segmentation In The Public Housing Service And Payakumbuh City Residential Area With Using The K-Means Clustering Model Edvidel Arda; Arif Aulia; Opel Saputra; Jerry Heikal
Jurnal Ekonomi dan Bisnis Digital Vol. 1 No. 3 (2024): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Effective employee performance management is one of the key factors in ensuring that the Department of Housing and Settlement Areas in Payakumbuh City operates efficiently and can respond to the evolving urban challenges. However, in a complex organization like this department, understanding employee performance and identifying areas that need improvement can be a very complex task. In this context, employee performance segmentation techniques, such as the use of K-means Clustering models, can be significantly helpful. This model allows us to group employees based on their performance characteristics, which, in turn, can be used to identify performance trends and issues that may exist. In this study, data processing is conducted using the K-means clustering method, which divides the data into 3 (three) clusters. The sample used in this research consists of 53 samples, comprising employees in the Department of Housing and Settlement Areas in Payakumbuh City, resulting in Cluster I classified as high cluster, Cluster II classified as low cluster, and Cluster III classified as a medium cluster.
Co-Authors Edvidel Arda Edvidel Arda Jerry Heikal Jerry Heikal Opel Saputra