Identifikasi Mahasiswa Berprestasi Menggunakan Algoritma Backpropagation
DOI:
https://doi.org/10.30865/resolusi.v1i2.113Keywords:
Artificial Neural Network; Backpropagation; StudentAbstract
Determination of performance students requires a gradual process. To speed up the process of determining performance students, the Artificial Neural Network method was used. The method used is Backpropagation on student data on AMIK Tunas Bangsa Pematangsiantar. This research produced five architectural models, they are 5-5-1, 5-6-1, 5-7-1, 5-8-1, 5-9-1 and 5-10-1 with models 5-5-1 the best in accelerating the process of determining performance students. So that this architectural model is best to be used to determine the better performance students
Downloads
References
R. Muhammad, G. P. Din, and A. M. Masykur, “Dinamika Psikologis Mahasiswa Berprestasi: Studi Kualitatif Deskriptif,” J. Empati, vol. 5, no. 1, pp. 50–54, 2016.
G. Saputri, A. Ibrahim, and M. Afrina, “Perancangan Pemilihan Mahasiswa Prestasi Pada Fakultas Ilmu Komputer Universitas Sriwijaya,” vol. 3, no. 1, pp. 1–4, 2017.
A. Sudarsono, “Jaringan Syaraf Tiruan Untuk Memprediksi Laju Pertumbuhan Penduduk Menggunakan Metode Bacpropagation (Studi Kasus Di Kota Bengkulu),” Media Infotama, vol. 12, no. 1, pp. 61–69, 2016.
S. Solikhun, A. P. Windarto, H. Handrizal, and M. Fauzan, “Jaringan Saraf Tiruan Dalam Memprediksi Sukuk Negara Ritel Berdasarkan Kelompok Profesi Dengan Backpropogation Dalam Mendorong Laju Pertumbuhan Ekonomi,” Klik - Kumpul. J. Ilmu Komput., vol. 4, no. 2, p. 184, 2017, doi: 10.20527/klik.v4i2.90.
A. Heryati, Erduandi, and Terttiaavini, “Penerapan Jaringan Saraf Tiruan Untuk Memprediksi Pencapaian Prestasi Mahasiswa,” Konf. Nas. Sist. Inf. 2018 STMIK Atma Luhur Pangkalpinang, 8 – 9 Maret 2018, no. January 2018, pp. 8–9, 2018.
N. Lestari and L. L. Van FC, “Implementasi jaringan syaraf tiruan untuk menilai kelayakan tugas akhir mahasiswa (studi kasus di amik bukittinggi),” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 10–24, 2017, doi: 10.31849/digitalzone.v8i1.614.
R. S. Suhartanto, C. Dewi, and L. Muflikhah, “Implementasi Jaringan Syaraf Tiruan Backpropagation untuk Mendiagnosis Penyakit Kulit pada Anak,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 7, pp. 555–562, 2017.
A. Wanto, “Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan Pada Kabupaten/Kota Di Provinsi Riau,” Klik - Kumpul. J. Ilmu Komput., vol. 5, no. 1, p. 61, 2018, doi: 10.20527/klik.v5i1.129.
Y. H. Siregar, “Prediksi Perilaku Pola Jumlah Mahasiswa Mengguakan Jaringan Syaraf Tiruan Dengan Metode Backpropagation,” vol. 1, no. 2, pp. 145–152, 2017.
S. Solikhun, M. Safii, and A. Trisno, “Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, p. 24, 2017, doi: 10.30645/j-sakti.v1i1.26.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Identifikasi Mahasiswa Berprestasi Menggunakan Algoritma Backpropagation
ARTICLE HISTORY
Issue
Section
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).