DOI: https://doi.org/10.14710/jati.21.1.76-87

STUDI KOMPARATIF MODEL MACHINE LEARNING DALAM MEMPREDIKSI KETERLAMBATAN PEGAWAI: LOGISTIC REGRESSION, SVM, DAN RANDOM FOREST

*Inggrid Nindia Aprila Palupi  -  Universitas Airlangga, Indonesia
M Fariz Fadillah Mardianto  -  Universitas Airlangga, Indonesia
Imam Yuadi  -  Universitas Airlangga, Indonesia
Budiyan Mariyadi  -  Universitas Muhammadiyah Bandung, Indonesia

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Abstract

Keterlambatan karyawan adalah salah satu jenis pelanggaran terhadap disiplin kerja yang dapat berdampak pada produktivitas dan efektivitas organisasi. Penelitian ini bertujuan untuk mengembangkan serta membandingkan performa dari tiga algoritma machine learning Regresi Logistik, SVM, dan Random Forest dalam memprediksi keterlambatan pegawai dengan menggunakan data keterlambatan dan karakteristik individu. Dataset yang digunakan terdiri dari 1902 data, yang dibagi 80% data training dan 20% data testing dengan enam variabel, mencakup usia, lama bekerja, status pernikahan, jarak tempat tinggal ke kantor, jenis kendaraan yang digunakan, dan gaya hidup. Hasil analisis menunjukkan bahwa Random Forest memberikan kinerja prediktif yang paling baik dalam mengenali pegawai yang memiliki potensi untuk terlambat, dengan nilai akurasi tertinggi sebesar 0.82, presisi sebesar 0.93, recall sebesar 0.84, dan F1-score sebesar 0.88. Model ini terbukti dapat menunjukkan kemampuan klasifikasi yang andal dan seimbang. Analisis feature importance mengidentifikasi usia dan masa kerja sebagai faktor paling berpengaruh terhadap prediksi keterlambatan. Temuan ini tidak hanya memberikan wawasan baru dalam pengelolaan kedisiplinan pegawai, tetapi juga membuka peluang implementasi sistem peringatan dini yang dapat diintegrasikan ke dalam sistem kehadiran digital organisasi. Penelitian ini merekomendasikan perluasan variabel untuk studi lanjutan dan pemanfaatan hasil model sebagai dasar penyusunan kebijakan SDM yang lebih adaptif dan berbasis data.

 

Abstract

[Comparative Study of Machine Learning Models in Predicting Employee Delay: Logistic Regression, SVM, and Random Forest] Employee tardiness is one type of violation of work discipline that can impact organizational productivity and effectiveness. This study aims to develop and compare the performance of three machine learning algorithms Logistic Regression, SVM, and Random Forest in predicting employee tardiness using tardiness data and individual characteristics. The dataset used consists of 1902 data, which is divided into 80% training data and 20% with six variables, including age, length of service, last education level, marital status, distance from residence to office, type of vehicle used, and lifestyle. The results of the analysis show that Random Forest provides the best predictive performance in identifying employees who have the potential to be late, with the highest accuracy value of 0.82, precision of 0.93, recall of 0.84, and F1-score of 0.88. This model is proven to be able to demonstrate reliable and balanced classification capabilities. Feature importance analysis identifies age and length of service as the most influential factors in predicting tardiness. These findings not only provide new insights into employee discipline management but also open up opportunities for the implementation of an early warning system that can be integrated into the organization's digital attendance system. This study recommends expanding the variables for further studies and utilizing the model results as a basis for formulating more adaptive and data-based HR policies.

Keywords: sustainability industry; developing strategy; MCDM

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Keywords: keterlambatan pegawai; machine learning; random forest; logistic regression; SVM; Prediksi SDM

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