Classification of Alumni Employment Fields of the Nursing Study Program at Politeknik Nusa Utara Using K-Means Clustering Method
DOI:
https://doi.org/10.58982/krisnadana.v5i1.936Keywords:
K-means, Classification, Field of work, GraduatesAbstract
Tracer study is a trace trace of graduates performed every year after graduation competence in the world of work. In addition, tracer studies are also a prerequisite for obtaining accreditation from the National Council for Accreditation of Education. Alumni field classification can be done using the K-means Clustering method to group data based on data feature similarities. This research aims to facilitate the analysis of the classification of alumni's field of work. Job classification data was obtained from the alumni tracer on the Nursing Studies Program. The study analyzed a healthcare tracer that contains a curriculum cover from 2014 to 2022 using a K-means Clustering algorithm using Microsoft excel. The attributes used are domicile, admission time, graduation time, and workplace authority. The formed cluster is two clusters. The results of this research can be used as a basis for decision-making to determine the promotion strategy as well as the preparation of curricula based on the cluster formed by the Nursing Studies Program available at the Health Department of Politeknik Nusa Utara.
References
I. Ibrahim and M. Iqbal, “Career Pattern Analysis of SMKN 1 Stabat Graduates Using K-Means Clustering Algorithm on Tracer Study Dataset,” J. Data Sci., vol. 3, no. 01, pp. 43–57, 2025, doi: https://doi.org/10.58471/jds.v3i01.6543.
D. Prasetyawan, A. Mulyanto, and R. Gatra, “Pemetaan Lintasan Karir Alumni Berdasarkan Analisis Cluster: Kombinasi K-Means dan Reduksi Dimensi Autoencoder,” Edumatic J. Pendidik. Inform., vol. 9, no. 1, pp. 198–207, 2025, doi: https://doi.org/10.29408/edumatic.v9i1.29713.
T. Tutik and M. I. Rosadi, “Rancang Bangun Aplikasi Tracer Study Alumni SMK Negeri 1 Sukorejo Berbasis Android,” J. Krisnadana, vol. 2, no. 1, pp. 277–288, 2022.
I. G. I. Sudipa et al., PENERAPAN SISTEM INFORMASI DI BERBAGAI BIDANG. PT. Sonpedia Publishing Indonesia, 2023.
D. Budiman et al., Sistem Informasi Manajemen: Panduan Praktis dalam Pembangunan Sistem Informasi Manajemen. PT. Sonpedia Publishing Indonesia, 2024.
D. P. Rakhmadani and F. D. Adhinata, “A Web-Based Information System for Lecturer’s Performance Appraisal Using Gamification Concepts and Rating Scale Methods,” J. Ris. Inform., vol. 3, no. 2, pp. 167–174, 2021, doi: 10.34288/jri.v3i2.201.
N. M. D. G. Pratiwi, N. K. V. Juliuntari, P. W. Agustin, and S. Aprilia, “Prototyping Approach for User Interface Optimization of" Gowes" Bicycle Rental Mobile Application,” TECHNOVATE J. Inf. Technol. Strateg. Innov. Manag., vol. 1, no. 3, pp. 127–140, 2024, doi: 10.52432/technovate.1.3.2024.127-140.
R. Muktiadi and A. Y. Badharudin, “Metode K-Means untuk Mengelompokkan Alumni Berdasarkan Waktu Mencari Pekerjaan,” Sainteks, vol. 16, no. 1, 2019, doi: https://doi.org/10.30595/sainteks.v16i1.7019.
K. K. Gupta and S. Kumar, “K-means clustering based high order weighted probabilistic fuzzy time series forecasting method,” Cybern. Syst., pp. 1–23, 2022.
B. Kwintiana et al., DATA SCIENCE FOR BUSINESS: Pengantar & Penerapan Berbagai Sektor. PT. Sonpedia Publishing Indonesia, 2023.
H. Kim and G. Qin, “Summarizing Students’ Free Responses for an Introductory Algebra-Based Physics Course Survey Using Cluster and Sentiment Analysis,” IEEE Access, vol. 11, pp. 89052–89066, 2023, doi: 10.1109/ACCESS.2023.3305260.
A. H. Lubis, “Model segmentasi pelanggan dengan kernel k-means clustering berbasis customer relationship management,” Sink. J. dan Penelit. Tek. Inform., vol. 1, no. 1, 2016.
N. Sinsu, O. Lumasuge, and S. A. Sehang, “CLUSTERING SEBARAN ALUMNI PROGRAM STUDI SISTEM INFORMASI POLITEKNIK NEGERI NUSA UTARA,” J. Ilm. Behongang, vol. 3, no. 1, pp. 8–14, 2020.
D. Adriani et al., “Using Distance Measure to Perform Optimal Mapping with the K-Medoids Method on Medicinal Plants, Aromatics, and Spices Export,” J. Wirel. Mob. Networks, Ubiquitous Comput. Dependable Appl., vol. 14, no. 3, pp. 103–111, 2023, doi: 10.58346/JOWUA.2023.I3.008.
A. Kashtalian and T. Sochor, “K-means Clustering of Honeynet Data with Unsupervised Representation Learning.,” in IntelITSIS, 2021, pp. 439–449.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Noldy Sinsu, Arifin P Tindi, Oktavianus Lumasuge
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
