Klasifikasi Ketidakhadiran di Tempat Kerja Menggunakan Metode Support Vector Machine

Natasya Sumeisey; Titik Misriati; Imam Nawawi

doi:10.53697/jkomitek.v4i2.2618

Authors

Natasya Lexbert Sumeisey Universitas Bina Sarana Informatika
Titik Misriati Universitas Bina Sarana Informatika
Imam Nawawi Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.53697/jkomitek.v4i2.2618

Keywords:

Ketidakhadiran Karyawan, Klasifikasi, Prediksi Ketidakhadiran, Pembelajaran Mesin

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan ketidakhadiran karyawan di tempat kerja menggunakan metode Support Vector Machine (SVM), dengan menggunakan dataset yang mencakup informasi demografi, pekerjaan, dan faktor-faktor lain yang terkait dengan ketidakhadiran. Dataset yang digunakan berisi catatan ketidakhadiran karyawan sebuah perusahaan kurir di Brasil dari UCI Machine Learning. Penelitian ini mengimplementasikan model SVM dengan kernel Radial Basis Function (RBF), yang dipilih karena kemampuannya dalam menangani data non-linier. Hasil evaluasi model menunjukkan kinerja yang sangat baik, dengan AUC sebesar 0,995, akurasi mencapai 98,1%, dan skor F1 sebesar 0,981, yang menunjukkan keseimbangan yang sangat baik antara presisi dan ingatan. Model tersebut berhasil memprediksi sebagian besar ketidakhadiran karyawan secara akurat, dengan kesalahan prediksi yang minimal. Namun, masih ada beberapa kesalahan kecil dalam memprediksi ketidakhadiran, yang dapat diperbaiki dengan menyetel hiperparameter dan menambahkan fitur tambahan yang terkait dengan faktor-faktor yang memengaruhi ketidakhadiran. Secara keseluruhan, penelitian ini menunjukkan bahwa model SVM merupakan alat yang efektif dan efisien untuk memprediksi ketidakhadiran karyawan, dengan hasil yang dapat diterapkan dalam mengelola ketidakhadiran di organisasi.

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