Implementation of Artifical Neural Networks with Multilayer Perceptron for Analysis of Acceptance of Permanent Lecturers
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Abstract
Lecturer acceptance selection is the first step in building an education. The Multilayer Perceptron method can be applied in the case of permanent lecturer admissions. The problem faced in the admission of permanent lecturers is that reception is still subjective. This research will prove the ability of the Multilayer Perceptron algorithm to classify eligibility as a lecturer or not. Inputs from this study were prospective applicants' data, namely age, grade point average (GPA), written test score, interview value, and home base status. Sample data amounted to 100 data. 75% of the data is used as training data, and 25% as test data. The test results of the accuracy of the data are known that the multilayer perceptron neural network method has an accuracy rate of 98.7% and with a ROC Area value of 0.989. This proves that the application of the model used belongs to the classification category very well because it has a ROC value between 0.90-1.00.
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[2] M. Marsono, “Penerapan Data Mining Pengaturan Pola Tata Letak Barang Pada Berkah Swalayan Untuk Strategi Penjualan Menggunakan Algoritma Apriori,” InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 3, no. 2. pp. 170–175, 2019, doi: 10.30743/infotekjar.v3i2.908.
[3] R. Rosnelly and L. Wahyuni, “Tropical Diseases Identification Using Neural Network Adaptive Resonance Theory 2,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm, pp. 1–4, 2019, doi: 10.1109/CITSM.2018.8674364.
[4] M. A. B. W. Nordin, D. Vedenyapin, M. F. Alghifari, and T. S. Gunawan, “The disruptometer: An artificial intelligence algorithm for market insights,” Bull. Electr. Eng. Informatics, vol. 8, no. 2, pp. 727–734, 2019, doi: 10.11591/eei.v8i2.1494.
[5] Y. Ardilla, H. Tjandrasa, I. Arieshanti, and A. E. Permutasi, “Deteksi Penyakit Epilepsi dengan Menggunakan Multilayer Perceptron,” J. Tek. POMITS, vol. 3, no. 1, pp. 1–5, 2014.
[6] I. Satriadi, “Implementasi Jaringan Syaraf Tiruan Dengan Multilayer Perceptron Untuk Analisa Pemberian Kredit,” vol. 5, no. 6, pp. 605–610, 2018.
[7] J. M. Informasi, “Diagnosa penyakit kulit pada anjing dengan algoritma multilayer perceptron,” no. 2, pp. 1–7, 2019.
[8] G. Tika and Adiwijaya, “Klasifikasi Topik Berita Berbahasa Indonesia Menggunakan Multilayer Perceptron,” e-Proceeding Eng., vol. 6, no. 2, p. 2137, 2019.
[9] M. Irfan, B. A. Ardi Sumbodo, and I. Candradewi, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 7, no. 2, p. 139, 2017, doi: 10.22146/ijeis.18260.
[10] M. S. H. Shyara Taruna R, “Enhanced Naïve Bayes Algorithm for Intrusion Detection in Data Mining,” J. Comput. Sci. Inf. Technol. Vol. 4 , 2013, 960-962, vol. 27, no. 6, pp. 960–962, 2008.
[11] L. R. Angga Ginanjar Mabrur, “Penerapan Data Mining Untuk Memprediksi Kriteria Nasabah Kredit,” J. Komput. dan Inform., vol. 1, no. 1, pp. 53–57, 2012.
[12] S. Mujiasih, “Utilization Of Data Mining For Weather Forecasting`,” J. Meteorol. dan Geofis., vol. 12, no. 2, pp. 189–195, 2011.
[13] C. P. Lubis and Z. Situmorang, “Penerapan Metode Naïve Bayes dan C4.5 Pada Penerimaan Pegawai di Universitas Potensi Utama,” Csrid, vol. 12, no. 2460–870X, pp. 51–62, 2020.
[14] S. N. Sains, S. Wardani, S. R. Andani, and M. Informatika, “Jaringan Saraf Tiruan Memprediksi Rata-rata Pengeluaran Perkapita Untuk Makan Dan Bukan Makanan Menurut Provinsi,” pp. 630–635, 2019.
[15] M. Z. Naf’an and J. Arifin, “Identifikasi Tanda Tangan Berdasarkan Grid Entropy Menggunakan Multi Layer Perceptron,” J. Infotel, vol. 9, no. 2, p. 172, 2017, doi: 10.20895/infotel.v9i2.219.
[16] M. Badrul, “Optimasi Neural Network Dengan Algoritma Genetika Untuk Prediksi Hasil Pemilukada,” Bina Insa. ICT J., vol. 3421, pp. 229–242, 2016.