Comparative Analysis of Data Mining Applications in Predicting the Quality of Employee Performance Using the C4.5 Algorithm Method

  • Hijrah Hijrah STAIN Teungku Dirundeng Meulaboh
  • Maulidar Maulidar Sekolah Tinggi Ilmu Administrasi Pelita Nusantara
  • Adria Adria Sekolah Tinggi Ilmu Administrasi Pelita Nusantara
Keywords: Comparative Analysis, Confusion Matrix, Data Mining Applications, C45 Algorithm

Abstract

 This study was conducted to measure and compare the values ​​of accuracy, precision, and recall, from the rapid miner and weka data mining applications by comparing the value of the confusion matrix, the application of the confusion matrix method was carried out by testing the training data obtained from the performance data of company employees engaged in the Internet. Service Providers. Testing the data is carried out to predict the quality of employee performance with the C4.5 algorithm. The approach taken in this research is carried out with a qualitative approach and uses library research research techniques, the results of which are analyzed using the analytical method of comparing the results of the confusion matrix value. From the results of the comparative analysis that has been carried out, the results of the rapid miner application are superior in terms of Accuracy of 94.12% and Recall of 95.6%, while the Weka application is better only in terms of Recall by 95.6%. The test was carried out using the rapid miner data mining application version 7.6.0.0.1 and the Weka application version 3.8.1.

References

[1] S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.
[2] S. N. Evita, W. O. Z. Muizu, and Raden Tri Wayu Atmojo, “Penilaian Kinerja Karyawan Dengan Menggunakan Metode Behaviorally Anchor Rating Scale dan Management By Objectives (Studi kasus pada PT Qwords Company International),” Pekbis J., vol. 9, no. 1, pp. 18–32, 2017.
[3] Ainurrohmah, “Akurasi Algoritma Klasifikasi pada Software Rapidminer dan Weka,” Prisma, vol. 4, pp. 493–499, 2021, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/.
[4] D. Damayanti, “Perbandingan Akurasi Software Rapidminer dan Weka Menggunakan Algoritma K-Nearest Neighbor (K-NN),” J. Heal. Sains, vol. 2, no. 6, pp. 994–1006, 2021, doi: 10.46799/jsa.v2i6.247.
[5] M. Faid, M. Jasri, and T. Rahmawati, “Perbandingan Kinerja Tool Data Mining Weka dan Rapidminer Dalam Algoritma Klasifikasi,” Teknika, vol. 8, no. 1, pp. 11–16, 2019, doi: 10.34148/teknika.v8i1.95.
[6] A.- Arini, L. K. Wardhani, and D.- Octaviano, “Perbandingan Seleksi Fitur Term Frequency & Tri-Gram Character Menggunakan Algoritma Naïve Bayes Classifier (Nbc) Pada Tweet Hashtag #2019gantipresiden,” Kilat, vol. 9, no. 1, pp. 103–114, 2020, doi: 10.33322/kilat.v9i1.878.
[7] Paul V.M.;, G. M. Indra, B. E. Damanik;, I. Parlina;, and W. Saputra;, “DALAM MENENTUKAN KELAYAKAN PENERIMAAN BANTUAN BEDAH RUMAH PADA DESA TIGA DOLOK Paul V . M ., Indra Gunawan , Bahrudi Efendi Damanik , Iin Parlina dan Widodo Saputra STIKOM Tunas Bangsa Pematangsiantar Abstrak Penerapan Data Mining Menggunakan Algoritma C4,” vol. 1, pp. 396–409, 2021.
[8] M. I. P.-A. Sri Wahyuni, Kana Saputra S, “IMPLEMENTASI RAPIDMINER DALAM MENGANALISA DATA MAHASISWA DROP OUT 1Sri,” J. Abdi Ilmu, vol. 10, pp. 421–437, 2017.
[9] V. R. Prasetyo, H. Lazuardi, A. A. Mulyono, and C. Lauw, “Penerapan Aplikasi RapidMiner Untuk Prediksi Nilai Tukar Rupiah Terhadap US Dollar Dengan Metode Linear Regression,” J. Nas. Teknol. dan Sist. Inf., vol. 7, no. 1, pp. 8–17, 2021, doi: 10.25077/teknosi.v7i1.2021.8-17.
[10] S. Pujiono, A. Amborowati, and M. Suyanto, “Analisis Kepuasan Publik Menggunakan Weka Dalam Mewujudkan Good Governance Di Kota Yogyakarta,” Data Manaj. dan Teknol. Inf., vol. 14, no. 2, p. 45, 2013.
[11] F. F. Harryanto and S. Hansun, “Penerapan Algoritma C4.5 untuk Memprediksi Penerimaan Calon Pegawai Baru di PT WISE,” Tek. Inform. Dan Sist. Inf., vol. 3, no. 2, pp. 95–103, 2017, [Online]. Available: http://jurnal.mdp.ac.id/index.php/jatisi/article/view/71.
[12] A. Asroni, B. Masajeng Respati, and S. Riyadi, “Penerapan Algoritma C4.5 untuk Klasifikasi Jenis Pekerjaan Alumni di Universitas Muhammadiyah Yogyakarta,” Semesta Tek., vol. 21, no. 2, pp. 158–165, 2018, doi: 10.18196/st.212222.
[13] F. D. Astuti, “Seleksi Atribut Menggunakan Information Gain Untuk Clustering Penduduk Miskin Dengan Validity Index Xie Beni,” Teknika, vol. 6, no. 1, pp. 61–65, 2017, doi: 10.34148/teknika.v6i1.58.
[14] P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, “Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5,” Prism. Pros. Semin. Nas. Mat., vol. 3, pp. 64–71, 2020.
[15] A. Saifudin, “Metode Data Mining Untuk Seleksi Calon Mahasiswa,” vol. 10, no. 1, pp. 25–36, 2018.
[16] I. Budiman and R. Ramadina, “Penerapan Fungsi Data Mining Klasifikasi untuk Prediksi Masa Studi Mahasiswa Tepat Waktu pada Sistem Informasi Akademik Perguruan Tinggi,” Ijccs, vol. x, No.x, no. 1, pp. 1–5, 2015.
[17] H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
[18] K. S. Nugroho, “Menerapkan Model Klasifikasi Machine Learning pada RapidMiner,” 2020. https://ksnugroho.medium.com/menerapkan-model-machine-learning-pada-rapidminer-142259846e13 (accessed Jan. 18, 2022).
[19] Durrotul, “Implementasi Data Mining Dalam Prediksi Performance Software Engineer PT.Emerio Menggunakan Decision Tree,” J. Ilm. Inform. Komput. Univ. Gunadarma, vol. 22, no. 1, pp. 31–43, 2017.
[20] M. T. . DR. DERWIN SUHARTONO, S.KOM., “Weka: Software untuk Memahami Konsep Data Mining,” 2018. https://socs.binus.ac.id/2018/11/29/weka-software-untuk-memahami-konsep-data-mining/ (accessed Jan. 18, 2022).
[21] E. Rilvani, A. B. Trisnawan, and P. P. Santoso, “Pelita Teknologi : Jurnal Ilmiah Informatika , Arsitektur dan Lingkungan,” Pelita Teknol. J. Ilm. Inform. Arsit. dan Lingkung., vol. 14, no. 2, pp. 103–110, 2019.
Published
2022-06-17