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Sukma Wati, Ade
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science

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Pemanfaatan Algoritma Decision Tree Pada Machine Learning Dalam Penentuan Klasifikasi Kinerja Karyawan Pada CV Duta Media Ienda, Ienda Meiriska; Sukma Wati, Ade; Rahmi, Lailatur Rahmi
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 17 No 1 (2025): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

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Abstract

Employee performance assessment is a critical step in achieving corporate objectives. However, this process often faces challenges such as a lack of accuracy and objectivity. To address these issues, this study proposes utilizing a machine learning-based classification model using the Decision Tree algorithm. The model is designed to classify employee performance based on four key aspects: productivity, skills, discipline, and work achievements. The research employs a supervised learning method with the Decision Tree algorithm, using employee performance data to build and evaluate the classification model. The objective of this study is to create an accurate, objective, and reliable assessment system that management can use to evaluate and improve human resource performance. The results indicate that the classification model achieves an accuracy level of 80%, demonstrating the model's capability to predict employee performance comprehensively. While this accuracy is considered satisfactory, the findings also suggest room for further development to enhance prediction accuracy and consistency, particularly in complex cases. The implementation of this model offers significant benefits in supporting strategic decision-making by company management and contributes to improving the quality of human resources.   Keywords— Machine Learning, Employee performance,, Decision Tree
Optimasi Metode Naïve Bayes Menggunakan Smoothing dan Feature Selection Untuk Penyakit Demam Berdarah Dengue Lemi; Ilma Hasana Kunio, Nurul; Sukma Wati, Ade
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7208

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease caused by the Dengue virus and has emerged as a significant health issue in many tropical countries, including Indonesia. Early identification of the disease is crucial to prevent further spread and complications. This study aims to refine the Naïve Bayes methodology to improve the accuracy of early detection of medical data related to patients suffering from DHF. The application of Naïve Bayes is expected to enhance predictive accuracy and facilitate healthcare professionals in diagnostic procedures. The data used in this research consists of clinical patient information, including laboratory findings and experienced symptoms. The results show that the optimization of the Naïve Bayes method successfully increased prediction accuracy to 92%, which could serve as an effective diagnostic alternative for early DHF detection. The conclusion of this study is that Naïve Bayes can be relied upon to identify DHF more quickly and accurately, ultimately contributing to the medical decision-making process.
Co-Authors Ienda, Ienda Meiriska Ilma Hasana Kunio, Nurul Lemi Rahmi, Lailatur Rahmi