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Barutu, Sipra
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Automotive Engineering Computer Science & IT Economics, Econometrics & Finance

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Pengujian Algoritma C4.5 Untuk Mengevaluasi Kinerja Pegawai Pada Klinik Lulu Nasution, Darmeli; Marsya, Alviona; Ramatika, Desy; Siburian, Ramli S; Barutu, Sipra
Surplus: Jurnal Ekonomi dan Bisnis Vol. 2 No. 2 (2024): Januari-Juni 2024
Publisher : Yayasan Pendidikan Tanggui Baimbaian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71456/sur.v2i2.879

Abstract

Penelitian ini membahas pengujian algoritma C4.5 untuk mengevaluasi kinerja pegawai di Klinik Lulu. Evaluasi kinerja pegawai merupakan aspek penting dalam manajemen sumber daya manusia untuk meningkatkan produktivitas dan efisiensi operasional. Algoritma C4.5 digunakan untuk membangun pohon keputusan dari data kinerja pegawai, memungkinkan identifikasi pola dan hubungan antara faktor-faktor yang memengaruhi kinerja. Dengan pendekatan berbasis data mining, diharapkan penilaian kinerja pegawai menjadi lebih akurat dan objektif. Hasil penelitian menunjukkan bahwa algoritma C4.5 dapat membantu manajemen Klinik Lulu dalam meningkatkan efektivitas evaluasi kinerja pegawai.
Application Of C4.5 Algorithm In Disease Classification Barutu, Sipra
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5263

Abstract

In the modern era, information and communication technology (ICT) has a significant impact on the health sector, one of which is through the application of artificial intelligence (AI) for disease diagnosis. The C4.5 algorithm, one of the popular classification algorithms, shows great potential in helping doctors classify diseases more accurately and efficiently. Research shows that the C4.5 algorithm is able to achieve a high level of accuracy in classifying various types of diseases, such as diabetes mellitus, heart disease, and lung disease. Its advantages include ease of interpretation, resistance to data noise, and efficiency. However, its application also has several challenges, such as the availability of quality data, complex interpretation of results, and the potential for overfitting. Nevertheless, the C4.5 algorithm offers great potential to improve the quality of patient diagnosis and care. Further research is needed to overcome the challenges and improve the effectiveness of the C4.5 algorithm in disease classification, such as the development of anti-overfitting techniques, optimal attribute selection methods, and application to more types of diseases. With continued research and development, the C4.5 algorithm can become a valuable tool for doctors and other medical personnel in fighting disease.
Co-Authors Marsya, Alviona Nasution, Darmeli Ramatika, Desy Siburian, Ramli S