Penerapan Algoritma C4.5 untuk Memprediksi Penerimaan Calon Pegawai Baru di PT WISE

  • Fandy Ferdian Harryanto Universitas Multimedia Nusantara
  • Seng Hansun Universitas Multimedia Nusantara
Keywords: C4.5 Algorithm, Decision Tree, Employee Candidate, Prediction

Abstract

A company in general needs employee that have good ability, good manners and also can company. But there are difficulties in finding the qualities of people as a good employee candicacy. That’s why we need a way or method to identify peoples with the potential to become a new employee candidate. C4.5 algorithm can be used to predict and classify new employee candidate that have the potential to get into a corporation by using decision tree according to the data that we have and predict the new employee candidate qualities. According to the testing method called ten-fold cross validation, the accuracy of the prediction for the new employee candidate is 71% by using the built prediction application which implementing C4.5 algorithm.

References

[1] IGI Global Dictionary, 2015, What is Information Gain, http://www.igiglobal.com/dictionary/information-gain/14407/, diakses tgl 10 Desember 2015.
[2] Jiandi, R., 2016, Implementasi Algoritma C4.5 untuk Prediksi Potensi Mahasiswa Sebagai Pengurus Organisasi Menggunakan Data Hasil PAPI KOSTICK (Studi
Kasus: Universitas Multimedia Nusantara), Universitas Multimedia Nusantara, Tangerang.
[3] Jefri, 2013, Implementasi Algoritma C4.5 Dalam Aplikasi untuk Memprediksi Jumlah Mahasiswa yang Mengulang Mata Kuliah STMIK Yogyakarta, STMIK
AMIKOM, Yogyakarta.
[4] Kumara, R. dan Supriyanto, C., 2015, Klasifikasi Data Mining untuk Penerimaan Seleksi Calon Pegawai Negeri Sipil 2014 Menggunakan Algoritma Decision
Tree C4.5, Universitas Dian Nuswantoro, Jawa Tengah.
[5] Mengkepe, E., 2004, Sistem Pendukung Keputusan Pemberian Kredit Mobil PT. Astra International tbk., Isuzu Division Makassar, Universitas Widyatama,
Bandung.
[6] Putri, S. U., 2015, Implementasi Metode C4.5 untuk Menentukan Guru Terbaik pada SMK 1 Percut Sei Tuan Medan, STMIK Budi Darma, Medan.
[7] HSSINA, B., dkk., 2014, A Comparative Study of Decision Tree ID3 and C4.5, Sultan Moulay Slimane University, Morocco.
[8] Raditya, A.,2012, Implementasi Data Mining Classification untuk Mencari Pola Prediksi Hujan dengan Menggunakan Algoritma C4.5, Universitas Gunadarma,
Depok.
[9] Sulistiyani, T. dan Ambar, R., 2003, Manajemen Sumber Daya Manusia, Graha Ilmu, Yogyakarta.
[10] Slamet, A.,2007, Manajemen Sumber Daya Manusia, Universitas Negeri Semarang, Semarang.
[11] Tjahyono, A. dan Anggara, A. M., 2010, Sistem Pendukung Keputusan Penerimaan Pegawai Baru pada PT. Kanasritex Semarang, Techno.com, Vol. 9 No.3.
[12] Marwana, 2014, Algoritma C4.5 untuk Simulasi Prediksi Kemenangan Dalam Pertandingan Sepakbola, STIMED, Nusa Palapa, Makassar.
[13] Triisant, 2015, Pohon Keputusan dengan Algoritma C4.5, http://dokumen.tips/documents/algoritma-c45.html, Diakses tgl 21 Maret 2016.
[14] Ruggieri, S., 2002, Efficient C4.5, IEEE Transaction on Knowledge and Data Engineering 14(2), hal.438-444. [15] Refaeilzadeh, P., Tang, L.,dan Liu, H., 2009,
Cross-Validation, Encyclopedia of Database Systems, hal.532-538.
Published
2017-03-16