Komparasi 3 Metode Algoritma Klasifikasi Data Mining Pada Prediksi Kenaikan Jabatan

Authors

  • Jaka Tirta Samudra Universitas Potensi Utama
  • B. Herawan Hayadi Universitas Potensi Utama
  • Puji Sari Ramadhan STMIK TRIGUNA DHARMA

DOI:

https://doi.org/10.53513/jsk.v5i2.5642

Keywords:

Komparasi Data Mining Klasifikasi Kenaikan Jabatan Prediksi

Abstract

With the increasing growth of the company and employee access to increase the desire to make exemplary employees or become employees who have high ideas to become role models for subordinates or other employees, so from the research case on the university campus, quality conducts a survey with data obtained directly from the university. Employees who work as permanent or contract employees sometimes get the right as an increase for promotion from the company for each field as well as an allocation of satisfactory employee performance from the aspect of the work carried out. In this research model using three models from Naïve Bayes, K-Nearest Neighbor, and Neural Network by taking the dataset directly from the analysis results, for that an analysis is carried out on each aspect to determine the results of the value classification used in the evaluation using 5-Fold validation, 10-Fold, and 20-Fold Cross Validatio thus obtain results to identify in the promotion classification with the highest value of accuracy of 76.6%, the highest value of F1 of 67.8%, the highest value of precision of 65.9%, and the highest value of recall of 76.6%.

References

Ahamed, K. I. (2016). A Study on Neural Network Architectures. 1–7.

Al-kaf, H. A. G., Chia, K. S., & Mohammed, N. A. (2018). A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum. 6466. https://doi.org/10.1080/10916466.2018.1425717

Dalianis, H. (2018). Evaluation Metrics and Evaluation. Clinical Text Mining, 1967, 45–53. https://doi.org/10.1007/978-3-319-78503-5_6

Damuri, A., Riyanto, U., Rusdianto, H., & Aminudin, M. (2021). Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako. 8(6), 219–225. https://doi.org/10.30865/jurikom.v8i6.3655

Hayadi, B. H., Kim, J.-M., Hulliyah, K., & Sukmana, H. T. (2021). Predicting Airline Passenger Satisfaction with Classification Algorithms. IJIIS: International Journal of Informatics and Information Systems, 4(1), 82–94. https://doi.org/10.47738/ijiis.v4i1.80

Hayadi, B. H., Sudipa, I. G. I., & Windarto, A. P. (2021). Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(1), 11–20. https://doi.org/10.30812/matrik.v21i1.1273

Ketut, N., Astuti, M., Utami, N. W., Putu, I. G., & Juliharta, K. (2022). CLASSIFICATION OF BLOOD DONOR DATA USING C4 . 5 AND K-NEAREST NEIGHBOR METHOD ( CASE STUDY : UTD PMI BALI PROVINCE ). 18(1). https://doi.org/10.33480/pilar.v18i1.2790

Madame, Y., & Wahyu, A. (2022). Klasifikasi Rumah Tangga Penerima Subsidi Listrik di Provinsi Gorontalo Tahun 2019 dengan Metode K-Nearest Neighbor dan Support Vector Machine Electricity Subsidy Recipient Households Classification in Gorontalo Province in 2019 using K-Nearest Neighbor and Support Vector Machine. 10(1), 63–68. https://doi.org/10.26418/justin.v10i1.51210

Manullang, R. A., & Sianturi, F. A. (2021). Penerapan Algoritma K-Nearest Neighbour Untuk Memprediksi Kelulusan Mahasiswa. 4(2), 42–50.

Rini, M. S. (2018). Kajian kemampuan metode neural network untuk klasifikasi penutup lahan dengan menggunakan Citra Landsat-8 OLI (kasus di Kota Yogyakarta dan sekitarnya). Geomedia: Majalah Ilmiah Dan Informasi Kegeografian, 16(1), 1–12. https://doi.org/10.21831/gm.v16i1.20974

Rizky, S. A., Yesputra, R., & Santoso, S. (2021). Prediksi Kelancaran Pembayaran Cicilan Calon Debitur Dengan Metode K-Nearest Neighbor. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 7(2), 195–202. https://doi.org/10.33330/jurteksi.v7i2.1078

Romli, I., & Putra, B. M. (2020). Evaluasi Penilaian Kinerja Dalam Klasifikasi Data Mining Dengan metode Naïve Bayes. 1(1), 36–45.

Saudi, R., Fathir, A., Agus, T. R., Suyono, A. A., & Ibrahim, F. (2021). Analisis Sentimen Haramnya Musik Secara Umum Menggunakan Metode KNN. https://doi.org/10.47002/metik.v5i2.284

Tasya, M. R., A, B. S. W., & Luthfi, E. T. (2020). Klasifikasi Kualitas Kematangan Wortel Menggunakan Metode GLCM ( Gray Level Co-Occurrence Matrix ) Dan Neural Network. 1–10.

Utami, E., Hartanto, A. D., Informatika, P. T., Komputer, F. I., & Yogyakarta, U. A. (n.d.). Klasifikasi Kepribadian dengan Metode DISC pada Twitter Menggunakan Algoritma Artificial Neural Network Indicator ), Big Five , dan DISC ( Dominance , Influence , Steadiness dan. 1–20.

Wanto, A., Herawan Hayadi, B., Subekti, P., Sudrajat, D., Wikansari, R., Bhawika, G. W., Sumartono, E., & Surya, S. (2019). Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN. Journal of Physics: Conference Series, 1255(1). https://doi.org/10.1088/1742-6596/1255/1/012016

Wibawa, M. S., & Maysanjaya, I. M. D. (2018). Multi Layer Perceptron Dan Principal Component Analysis Untuk Diagnosa Kanker Payudara. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 7(1), 90. https://doi.org/10.23887/janapati.v7i1.12909

Yanto, B., -, B., -, J., & Hayadi, B. H. (2020). Indentifikasi Pola Aksara Arab Melayu Dengan Jaringan Syaraf Tiruan Convolutional Neural Network (Cnn). JSAI (Journal Scientific and Applied Informatics), 3(3), 106–114. https://doi.org/10.36085/jsai.v3i3.1151

Yanto, B., Lubis, A., Hayadi, B. H., & Nst, E. A. (2021). Klarifikasi Kematangan Buah Nanas Dengan Ruang Warna Hue Saturation Intensity. 135–146

Downloads

Published

2022-07-16