Perbandingan Metode Backpropagation dan Learning Vector Quantization (LVQ) Dalam Menggali Potensi Mahasiswa Baru di STMIK PalComTech
DOI:
https://doi.org/10.30812/matrik.v18i2.387Keywords:
Backpropagation, Artificial Neural Network, Learning Vector QuanitzationAbstract
The Research aimst to compare backpropagation and Learning Vector Quantization (LVQ) methods in exploring the potential of new students at STMIK PalComTech. Comparisons in this study involve four input variables used which consist of four basic subjects of informatics engineering and information systems (math, basic programming, computer networks and management bases) which then make informatics techniques and information systems as outputs, to get the accuracy level high in this study, the researchers used several variations of parameters which eventually produced the best accuracy of the two methods. From 120 data tested using variations in test data and training data which are then processed using variations in the learning rate parameters and epochs. From the test results obtained the level of accuracy of pattern recognition in the backpropagation method is 99.17% with a learning rate variation of 0.1 and epoch 100, the learning vector quantization method has an accuracy rate of 96.67% with a variation of learning rate 1 and epoch 20 From the results of the comparison the Backpropagation method is superior in terms of accuracy so that it becomes the right method to use in exploring the potential of new students at STMIK PalComTech.
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References
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[2] a. Nurkhozin, M. I. Irawan, and I. Mukhlash, “Klasifikasi Penyakit Diabetes Mellitus Menggunakan Jaringan Syaraf Tiruan Backpropagation Dan Learning,†Pros. Semin. Nas. Penelitian, Pendidik. dan Penerapan MIPA, no. 7, pp. 1–8, 2011.
[3] M. F. Q. Azizi, “Perbandingan antara Metode Backpropagation dengan Metode Learning Vector Quantization (LVQ) pada Pengenalan Citra Barcode.†Universitas Negeri Semarang, 2013.
[4] A. Prabowo, E. A. Sarwoko, and D. E. Riyanto, “Learning Vector Quantization Pada Pengenalan Pola Tandatangan,†J. SAINS DAN Mat., vol. 14, no. 4, pp. 147–153, 2006.
[5] R. Meliawati, O. Soesanto, and D. Kartini, “Penerapan Metode Learning Vector Quantization (LVQ) Pada Prediksi Jurusan Di SMA PGRI 1 Banjarbaru,†KLIK-KUMPULAN J. ILMU Komput., vol. 3, no. 1, pp. 11–20, 2016.
[6] D. Kartini, R. A. Nugroho, and M. R. Faisal, “Klasifikasi Kelulusan Mahasiswa Menggunakan Algoritma Learning Vector Quantization,†POSITIF J. Sist. dan Teknol. Inf., vol. 3, no. 2, pp. 93–98, 2017.
[7] D. A. Nugraha and W. Retnowati, “Sistem Pendukung Keputusan Penjurusan di SMA Menggunakan Metode Neural Network Backpropagation (Studi Kasus SMA Islam Kepanjen Malang),†Bimasakti.
[8] A. Jumarwanto, R. Hartanto, and D. Prastiyanto, “Aplikasi jaringan saraf tiruan backpropagation untuk memprediksi penyakit THT di Rumah Sakit Mardi Rahayu Kudus,†J. Tek. Elektro, vol. 1, no. 1, p. 11, 2009.
[9] S. Kusumadewi, “Artificial intelligence (teknik dan aplikasinya),†Yogyakarta Graha Ilmu, vol. 5, 2003.
[10] J. J. Siang, “Jaringan syaraf tiruan dan pemrogramannya menggunakan Matlab,†Penerbit Andi, Yogyakarta, 2005. [11] D. Puspitaningrum, “Pengantar Jaringan Syaraf Tiruan,†2006.
[12] L. V Fausett, Fundamentals of neural networks: architectures, algorithms, and applications, vol. 3. prentice-Hall Englewood Cliffs, 1994.
[13] Y. A. Lesnussa, S. Latuconsina, and E. R. Persulessy, “Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Memprediksi Prestasi Siswa SMA (Studi kasus: Prediksi Prestasi Siswa SMAN 4 Ambon),†J. Mat. Integr. ISSN, vol. 1412, p. 6184, 2015.
[14] Y. A. Lesnussa, L. J. Sinay, and M. R. Idah, “Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Penyebaran Penyakit Demam Berdarah Dengue (DBD) di Kota Ambon,†J. Mat. Integr., vol. 13, no. 2, pp. 63–72, 2017.
[15] A. Hasim, “Prakiraan Beban Listrik Kota Pontianak Dengan Jaringan Syaraf Tiruan (Artificial Neural Network).†IPB, Bogor (Tesis S2), 2008.
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Published
2019-05-30
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Articles
How to Cite
[1]
Y. Aprizal, R. I. Zainal, and A. Afriyudi, “Perbandingan Metode Backpropagation dan Learning Vector Quantization (LVQ) Dalam Menggali Potensi Mahasiswa Baru di STMIK PalComTech”, MATRIK, vol. 18, no. 2, pp. 294–301, May 2019, doi: 10.30812/matrik.v18i2.387.
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