Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5

Ikhsan Romli, Ahmad Turmudi Zy

Abstract


Technological development and scientific advancement are very important and influential parts of all fields. With the development and advancement of technology, being experts in a field is a must because it is required to know more and learn about the technology that is currently developing. Information technology and informatics are needed to support performance. Large and small companies also need fast and accurate information to make easier decisions making. Therefore, data mining classification techniques are needed to solve these problems. The classification used in data mining is a Decision tree because it is a technique that is widely used and produces output with existing rules so that it can present employee data to determine the overtime schedule. This study uses the C4.5 algorithm to determine the overtime schedule. The test results of the overtime schedule with the C4.5 algorithm with the Confusion matrix have good accuracy, precision, and recall values, namely 91% accuracy, 86.05% precision, and 92.5% recall.

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References


F. Dwi Meliani Achmad, Budanis, Slamat, “Klasifikasi Data Karyawan Untuk Menentukan Jadwal Kerja Menggunakan Metode Decision Tree,” J. IPTEK, vol. 16, no. 1, pp. 18–23, 2012.

Y. S. Luvia, D. Hartama, A. P. Windarto, and Solikhun, “Penerapan Algoritma C4.5 Untuk Klasifikasi Predikat Keberhasilan Mahasiswa Di AMIK Tunas Bangsa,” JURASIK, vol. 1, no. 1, pp. 75–79, 2016, doi: 10.1134/S1068162020050106.

T. C. Kurniawan, “Penerapan Algoritma C4.5 Dalam Penerimaan Calon Karyawan PT. Telkom Akses Area Lampung Berbasis Website,” Semin. Nas. IIB Darmajaya, pp. 491–501, 2017.

F. F. Harryanto and S. Hansun, “Penerapan Algoritma C4.5 untuk Memprediksi Penerimaan Calon Pegawai Baru di PT WISE,” J. Tek. Inform. Dan Sist. Inf., vol. 3, no. 2, pp. 95–103, 2017.

S. L. Saefudin, “Sistem Pendukung Keputusan Untuk Penilaian Kinerja Karyawan,” J. Pengemb. Ris. dan Obs. Tek. Inform., vol. 2, no. September, pp. 40–43, 2015.

A. S. Febriarini and E. Z. Astuti, “Penerapan Algoritma C4.5 untuk Prediksi Kepuasan Penumpang Bus Rapid Transit (BRT) Trans Semarang,” Eksplora Inform., vol. 8, no. 2, pp. 95–103, 2019, doi: 10.30864/eksplora.v8i2.156.

Yulia and A. D. Putri, “Data Mining Menggunakan Algoritma C4.5 Untuk Memprediksi Kepuasan Mahasiswa Terhadap Kinerja Dosen Di Kota Batam,” in SNISTEK, 2019, no. 2, pp. 235–240, doi: https://doi.org/10.33884/cbis.v7i2.1373.

I. Romli, F. Kharida, and C. Naya, “Penentuan Kepuasan Pelanggan Terhadap Pelayanan Kantor Pelayanan Pajak Menggunakan C4.5 dan PSO,” RESTI, vol. 4, no. 2, pp. 296–302, 2020.

M. F. Arifin and D. Fitrianah, “Penerapan Algoritma Klasifikasi C4.5 Dalam Rekomendasi Penerimaan Mitra Penjualan Studi Kasus : PT Atria Artha Persada,” InComTech, vol. 8, no. 2, pp. 87–102, 2018, doi: 10.22441/incomtech.v8i1.2198.

F. Gorunescu, Data Mining: Concepts, Models, and Techniques, 12th ed. Warsaw, Poiland: Springer, 2011.

F. Santoso, A. Syukur, and A. Z. Famani, “Algoritma C4 . 5 Dengan Particle Swarm Optimization Untuk Klasifikasi Lama Menghafal Al-Quran Pada Santri,” J. Teknol. Inf., vol. 14, no. 2, pp. 92–103, 2018.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Third Edit. USA: Morgan Kaufmann, 2012.

Suyanto, Data mining : untuk klasifikasi dan klasterisasi data, 1st ed. Bandung: Infoirmatika, 2017.

Kusrini and E. T. Luthfi, Algoritma Data Mining. Yogyakarta: Andi Offset, 2009.




DOI: http://dx.doi.org/10.30645/j-sakti.v4i2.260

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