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All Journal JRST (Jurnal Riset Sains dan Teknologi) Riwayat: Educational Journal of History and Humanities
Ferdiansah, Gilang
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Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Social Sciences

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Klasifikasi Kepribadian Karyawan Menggunakan Machine Learning Ferdiansah, Gilang; Yuadi, Imam
Riwayat: Educational Journal of History and Humanities Vol 8, No 4 (2025): Oktober, Social Issues and Problems in Society
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i4.49440

Abstract

Pemahaman terhadap tipe kepribadian menjadi mutlak pada kondisi digitalisasi dan hybrid working. Tipe kepribadian yang umum dikenal saat ini adalah introver dan ekstrover. Organisasi yang tidak mampu memahami tipe kepribadian karyawan, akan berdampak pada penurunan motivasi dan kinerja karyawan. Salah satu cara mengklasifikasikan tipe kepribadian pegawai adalah dengan pendekatan machine learning. Evaluasi terhadap beberapa hasil pendekatan machine learning, akan memberikan model dengan kinerja terbaik yang mampu mengklasifikasikan tipe kepribadian. Model Nave Bayes menjadi model terbaik pada klasfikasi tipe kepribadian ini dengan nilai accuracy sebesar 93,41%, lebih tinggi dibandingkan model lainnya. Penelitian ini diharapkan menambah wawasan ilmu pengetahuan pada human resources analitik dan memberikan informasi klasifikasi tipe kepribadian karyawan bagi organisasi.
Cyberloafing Analytics: Predicting Causes Using Machine Learning Models Ferdiansah, Gilang; Yuadi, Imam
JRST (Jurnal Riset Sains dan Teknologi) Volume 10 No. 1, March 2026: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v10i1.25997

Abstract

Cyberloafing refers to the practice of employees utilizing internet access for non-job-related activities during work hours. Cyberloafing poses a dilemma for organizations, as it is deemed aberrant conduct that might impact overall performance. Consequently, organizations must ascertain the determinants of cyberloafing. This study seeks to identify a suitable predictive model for the determinants of cyberloafing behavior in the workplace using a machine learning methodology. The employed methodology utilizes the conventional data mining cycle, namely the Cross-Industry Standard Process for Data Mining (CRISP-DM), with Orange Data Mining as the application tool. The findings indicate that Logistic Regression is the most effective model for forecasting cyberloafing. Logistic Regression yields performance scores of 90.5% Precision and 88.9% Recall. Conversely, the Naïve Bayes model had the lowest metrics, with a Precision of 64.8% and a Recall of 51.9%. This study serves as a reference demonstrating that Logistic Regression effectively predicts cyberloafing. This study enables firms to examine the factors contributing to cyberloafing, facilitating the development of policies aimed at mitigating its adverse effects.
Co-Authors Imam Yuadi