SRI REJEKI SUKANI, NPM 2109100101 (2025) PREDIKSI KEDISIPLINAN KARYAWAN BERDASARKAN KEHADIRAN PADA PT. PP LONDON SUMATRA INDONESIA TBK. SEI RUMBIYA ESTATE MENGGUNAKAN ALGORITMA NAÏVE BAYES. Skripsi thesis, Universitas Labuhanbatu.
![[img]](http://repository.ulb.ac.id/style/images/fileicons/text.png) |
Text
COVER.pdf Download (2MB) |
|
Text
BAB I.pdf Download (271kB) |
|
Text
BAB II.pdf Download (664kB) |
|
Text
BAB III.pdf Download (594kB) |
|
Text
BAB IV.pdf Download (1MB) |
|
Text
BAB V.pdf Download (247kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (214kB) |
Abstract
Penelitian ini bertujuan untuk memprediksi Kedisiplinan karyawan berdasarkan kehadiran di PT. PP London Sumatra Indonesia Tbk., Sei Rumbiya Estate dengan menggunakan algoritma Naïve Bayes. Data yang digunakan merupakan data absensi karyawan selama satu tahun (2024) yang diperoleh dari sistem absensi fingerprint perusahaan. Variabel yang dianalisis mencakup sakit, rujukan, sakit di rumah sakit, sakit di rumah sakit empat bulan, sakit selama lima bulan, dan jumlah lembut. Sedangkan target klasifikasi adalah kategori Kedisiplinan karyawan (Disiplin dan Tidak Disiplin). Proses penelitian meliputi pengumpulan data, preprocessing data, pembagian data menjadi 80% data latih dan 20% data uji, pembangunan model Naïve Bayes, serta evaluasi kinerja model. Hasil evaluasi menunjukkan bahwa model Naïve Bayes mampu memprediksi Kedisiplinan karyawan berdasarkan Kedisiplinan dengan akurasi 100%, precision 100%, recall 100%, dan F1-score 100%. Hasil ini mengindikasikan bahwa Naïve Bayes memiliki kemampuan klasifikasi yang sangat baik untuk dataset yang digunakan. Temuan ini diharapkan dapat membantu perusahaan dalam memantau pola Kedisiplinan karyawan secara lebih efektif dan mendukung pengambilan keputusan dalam manajemen sumber daya manusia. Kata Kunci: Karyawan, Kedisiplinan, Klasifikasi, Prediksi, Naïve Bayes ================================================================================================= This study aims to predict employee attendance at PT. PP London Sumatra Indonesia Tbk., Sei Rumbiya Estate using the Naïve Bayes algorithm. The data used consists of employee attendance records for one year (2024), obtained from the company’s fingerprint attendance system. The analyzed variables include sick leave, referral, hospitalization, four-month hospitalization, five-month sick leave, and the number of permissions. The target classification is employee attendance categories (Diligent and Not Diligent). The research process involved data collection, data preprocessing, splitting the data into 80% training and 20% testing sets, building the Naïve Bayes model, and evaluating the model’s performance. The evaluation results show that the Naïve Bayes model is able to predict employee discipline based on attendance with 100% accuracy, 100% precision, 100% recall, and 100% F1-score. These findings indicate that Naïve Bayes has an excellent classification capability for the dataset used. This study is expected to assist the company in monitoring employee discipline patterns more effectively and support decision-making in human resource management. Keywords: Employee, Discipline, Classification, Prediction, Naïve Bayes
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Karyawan, Kedisiplinan, Klasifikasi, Prediksi, Naïve Bayes==============Employee, Discipline, Classification, Prediction, Naïve Bayes |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 15 Sep 2025 04:38 |
| Last Modified: | 15 Sep 2025 04:38 |
| URI: | http://repository.ulb.ac.id/id/eprint/1685 |
Actions (login required)
 |
View Item |
