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Implementasi Logika Dengan Pemrograman Java Netbeans 7.0 Ramadhan, Hari; Dedi Leman
ULINA: Jurnal Pengabdian kepada Masyarakat Vol 2 No 1 (2024): Januari
Publisher : Universitas Mandiri Bina Prestasi (UMBP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58918/ulina.v2i1.222

Abstract

Tritech Informatics Vocational School is one of the references for Information Technology and Software Engineering Schools that can train students to hone and develop their knowledge in the field of computers. Here, both state and private universities take part in training and community service activities to help train students at school to hone and develop their knowledge. Through this training and community service activity, students or audiences are expected to be able to increase their knowledge in programming, especially basic algorithm programming. Also, with this training and community service activity, students at Tritech Informatics Vocational School Medan can develop their knowledge in the fields of education and teaching. So that the knowledge gained from this activity can be useful for them in the future. We, from one of the private universities, will provide training and insight into arrays that can be implemented in the Java programming language, where the data structure used to store a certain amount of data is of the same type. We will provide students with Tritech Informatics Vocational School Medan training in the form of Avarage Logic, Summery in an Array. Where this training is intended to hone and develop students' knowledge while implementing the Tridharma Higher Education Program.
Implementation of the K-Means Clustering Algorithm for Segmenting Employee Mental Health Profiles Based on Work Productivity Indicators Rahman, Maulia; Leman, Dedi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.6974

Abstract

This study aims to identify mental health profile segmentation among employees based on work productivity indicators in the context of working from home (WFH) using the K-Means clustering algorithm. This study uniquely integrates mental health and productivity indicators into an unsupervised clustering framework. A cross-sectional method was conducted on 100 employee respondents with 10 main variables, analysed using K-Means with four optimal cluster evaluation methods. The results identified four distinct segments: Low WFH Adaptation (25%), High WFH Enthusiasts (30%), Mixed Preference (25%), and Office Preference (20%), with Silhouette Score validation of 0.623 and Davies-Bouldin Index of 0.967. The main findings reveal the paradox of High WFH Enthusiasts, who have the highest productivity (93%) but the highest mental health risk (1.90). This segmentation provides a practical framework for developing personalised mental health intervention strategies in employee management in the remote working era.