PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK MEMPREDIKSI TINGKAT PROMOSI kARYAWAN PT PELABUHAN TANJUNG PRIOK

ANDI IRFAN DAENG MAPPA, . (2025) PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK MEMPREDIKSI TINGKAT PROMOSI kARYAWAN PT PELABUHAN TANJUNG PRIOK. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Penelitian ini membandingkan empat algoritma Machine Learning untuk memprediksi tingkat promosi karyawan di PT Pelabuhan Tanjung Priok menggunakan metode Knowledge Discovery in Database (KDD). Dataset yang digunakan mencakup 15 variabel dengan total 1,107 data. Penelitian dilakukan melalui tahapan pengumpulan data, pre-processing, transformasi data, pemodelan data, dan evaluasi. Algoritma yang dibandingkan meliputi Random Forest, K-Nearest Neighbors, Decision Tree, dan XGBoost. Evaluasi model menggunakan Confusion Matrix dengan metrik accuracy, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa algoritma XGBoost memiliki performa terbaik setelah dilakukan optimasi parameter menggunakan Hyperparameter Tuning. Penelitian ini memberikan kontribusi praktis bagi PT Pelabuhan Tanjung Priok untuk meningkatkan efektivitas proses promosi jabatan serta menawarkan wawasan bagi perusahaan lain dalam mengembangkan sistem prediksi promosi yang efisien dan akurat. ***** This study compares four Machine Learning algorithms to predict employee promotion rates at PT Pelabuhan Tanjung Priok using the Knowledge Discovery in Database (KDD) method. The dataset consists of 15 variables and 1,107 records. The research process involves data collection, preprocessing, data transformation, data mining, and evaluation. The algorithms compared include Random Forest, K-Nearest Neighbors, Decision Tree, and XGBoost. Model evaluation uses a Confusion Matrix with metrics such as accuracy, precision, recall, and F1-score. The results indicate that XGBoost algorithm outperforms other algorithms, achieving the highest accuracy after parameter optimization using Hyperparameter Tuning. This research provides practical insights for PT Pelabuhan Tanjung Priok to enhance the effectiveness of its promotion process and offers valuable guidance for other companies seeking to develop efficient and accurate promotion prediction systems.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Irma Permata Sari, M.Eng. ; 2). Fuad Mumtas, M.T.I.
Subjects:	Teknologi dan Ilmu Terapan > Teknik Komputer
Divisions:	FT > S1 Sistem dan Teknologi Informasi
Depositing User:	Users 26192 not found.
Date Deposited:	11 Feb 2025 08:19
Last Modified:	11 Feb 2025 08:19
URI:	http://repository.unj.ac.id/id/eprint/52675

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