Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network
DOI:
https://doi.org/10.30812/varian.v3i2.651Keywords:
Level of accuracy, Classification, Boosting Neural Network, Feedforward Neural NetworkAbstract
Supervised learning in Machine learning is used to overcome classification problems with the Artificial Neural Network (ANN) approach. ANN has a few weaknesses in the operation and training process if the amount of data is large, resulting in poor classification accuracy. The results of the classification accuracy of Artificial Neural Networks will be better by using boosting. This study aims to develop a Boosting Feedforward Neural Network (FANN) classification model that can be implemented and used as a form of classification model that results in better accuracy, especially in the classification of the inaccuracy of Terbuka University students. The results showed the level of accuracy produced by the Feedforward Neural Network (FFNN) method had an accuracy rate of 72.93%. The application of boosting on FFN produces the best level of accuracy which is 74.44% at 500 iterations
References
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Mijwel, M. M. (2018). Artificial Neural Networks Advantages and Disadvantages. Retrieved from https://www.researchgate.net/publication/323665827_Artificial_Neural_Networks_Advantages_and_Disadvantages
Muzakkir, I., Syukur, A., & Dewi, I. N. (2014). PENINGKATAN AKURASI ALGORITMA BACKPROPAGATION DENGAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION DALAM PREDIKSI PELANGGAN TELEKOMUNIKASI YANG HILANG. Jurnal Pseudocode, 1(1), 1–12.
Powers, D. M. W. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. International Journal of Machine Learning, 37–63.
Rifai, B. (2013). Algoritma Neural Network Untuk Prediksi Penyakit Jantung. Techno Nusa Mandiri, IX(1), 1–9.
Singh, Y., & Chauhan, A. S. (2009). Neural Networks in Data Mining. Journal of Theoretical and Applied Information Technology, 5(6), 37–42.
Somantri, O., & Wiyono, S. (2017). Peningkatan Akurasi Klasifikasi Tingkat Penguasaan Materi Bahan Ajar Menggunakan Jaringan Syaraf Tiruan Dan Algoritma Genetika. Jurnal Teknologi Dan Sistem Komputer, 5(4), 147–152. https://doi.org/10.14710/jtsiskom.5.4.2017.147-152
Suwardika, G., Suniantara, I. K. P., & Hendayanti, N. P. N. (2019). Ketidaktepatan waktu kelulusan mahasiswa universitas terbuka dengan metode boosting cart. Jurnal varian, 2(2), 37–46. https://doi.org/10.30812/varian.v2i2.361
Warsito, B. (2006). Perbandingan Model Feed Forward Neural Network Dan Generalized Regression Neural Network Pada Data Nilai Tukar Yen Terhadap Dolar AS. In Prosiding SPMIPA. (pp. 127–131).
Wezel, M., & Potharst, R. (2007). Improved Customer Choice Predictions using Ensemble Methods. European Journal of Operational Research, 181(1), 436–452. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0377221706003900
Wu, G., Ren, Y., Li, Y., Kwak, H., & Jang, S. (2009). Research on Parameter Optimization of Neural Networ. International Journal of HybridInformation Technology, 2(1), 81–90.
Yanti, N. (2011). Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Prediksi Stok Obat Di Apotek(Studi Kasus : Apotek Abc). In Seminar Nasional APlikasi Teknologi Informasi 2011 (SNATI 2011) (Vol. 2011, pp. 17–18).
Zhang, Y. (2010). New Advance in Machine Learning. Croatia: In-Tech.
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Published
2020-04-30
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[1]
“Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network”, JV, vol. 3, no. 2, pp. 95–102, Apr. 2020, doi: 10.30812/varian.v3i2.651.
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