PREDIKSI PRESTASI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA BACKPROPAGATION
Abstract
This study aims to overcome the problems in predicting student achievement at the Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done by applying Backpropagation algorithm and implement it into Matlab software. Backpropagation algorithm is one of the methods on artificial neural networks that is quite reliable in solving problems including prediction. In this study conducted on the object of students semester One with a lot of data samples 26 samples. The data sample is divided into two parts, 70% of the data is used as training data and 30% of the data is used as testing data. This study uses ten architectural models, namely 9-2-1, 9-3-1, 9-4-1, 9-5-1, 9-6-1, 9-7-1, 9-8-1, 9-9-1, 9-10-1, 9-11-1. Of the ten Backpropagation network architecture models implemented in predicting student achievement in Matlab software obtained the best output is 9-2-1 pattern with epoch 8149, time duration for 17 seconds, and MSE (error rate) value of 2.80 e-05 for training and MSE (error rate) of 0.1248 with accuracy of 87.5% for testing. The best architecture obtained is expected to be used as a picture by the academic Polytechnic Business Indonesia (PBI) in predicting student achievement.
References
[1] F. A. Bachtiar, I. K. Syahputra, and S. A. Wicaksono, “PERBANDINGAN ALGORITME MACHINE LEARNING UNTUK MEMPREDIKSI PENGAMBIL MATA KULIAH,” vol. 6, no. 5, pp. 543–548, 2019, doi: 10.25126/jtiik.2019611755.
[2] M. Tyrtaiou, A. Papaleonidas, A. Elenas, and L. Iliadis, “Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks,” 2020, pp. 85–98. doi: 10.1007/978-3-030-48791-1_6.
[3] S. Sonang, S. Defit, and M. Ramadhan, “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan”, [Online]. Available: https://www.bps.go.id/.
[4] I. B. K. Sudiatmika, F. Rahman, Trisno, and Suyoto, “Image forgery detection using error level analysis and deep learning,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 653–659, Apr. 2019, doi: 10.12928/TELKOMNIKA.V17I2.8976.
[5] “6 Application of Artificial Neural Networks for Airline Number of Passenger Estimation in time Series state”.
[6] M. D. Yalidhan and M. F. Amin, “IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 05, no. 02, 2018.
[7] M. Attamimi, R. Mardiyanto, and A. N. Irfansyah, “Inclined image recognition for aerial mapping using deep learning and tree based models,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 6, pp. 3034–3044, Dec. 2018, doi: 10.12928/TELKOMNIKA.v16i6.10157.
[8] F. Zola, G. W. Nurcahyo, and J. Santony, “JARINGAN SYARAF TIRUAN MENGGUNAKAN ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI PRESTASI SISWA,” vol. 1, no. 1, 2018.
[9] C. K. Arthur, V. A. Temeng, and Y. Y. Ziggah, “Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction,” Ghana Mining Journal, vol. 20, no. 1, pp. 20–33, Jul. 2020, doi: 10.4314/gm.v20i1.3.
[10] N. Sianturi, “Jaringan Syaraf Tiruan Pengaruh Infuse Pada Berat Badan Pria dengan Menggunakan Metode Backpropagation,” Journal of Computer System and Informatics (JoSYC), vol. 1, no. 3, pp. 134–140, 2020.
[11] I. Waspada, A. Wibowo, and N. S. Meraz, “SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER,” Jurnal Ilmu Komputer dan Informasi, vol. 10, no. 2, p. 108, Jun. 2017, doi: 10.21609/jiki.v10i2.481.
[12] M. Adi et al., “PENERAPAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI SAWAH MENURUT KABUPATEN/KOTA DI SUMATERA UTARA,” vol. 4, no. 1, pp. 77–86.
[13] C. T. Chen and G. X. Gu, “Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning,” Advanced Science, vol. 7, no. 5, Mar. 2020, doi: 10.1002/advs.201902607.
[14] N. Nafi’iyah, R. A. Ahmad, and S. Mujilahwati, “Prediksi Nilai Calon Mahasiswa dengan Algoritma Backpropagation (Studi Kasus: Data Kaggle),” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 3, no. 1, pp. 9–17, Apr. 2020, doi: 10.32672/jnkti.v3i1.1945.
[15] R. Novickis, D. J. Justs, K. Ozols, and M. Greitāns, “An approach of feed-forward neural network throughput-optimized implementation in FPGA,” Electronics (Switzerland), vol. 9, no. 12, pp. 1–16, Dec. 2020, doi: 10.3390/electronics9122193.
[16] I. C. Afolabi, S. I. Popoola, and O. S. Bello, “Modeling pseudo-second-order kinetics of orange peel-paracetamol adsorption process using artificial neural network,” Chemometrics and Intelligent Laboratory Systems, vol. 203, Aug. 2020, doi: 10.1016/j.chemolab.2020.104053.
[17] J. Bilski, B. Kowalczyk, A. Marchlewska, and J. M. Zurada, “Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks,” Journal of Artificial Intelligence and Soft Computing Research, vol. 10, no. 4, pp. 299–316, Oct. 2020, doi: 10.2478/jaiscr-2020-0020.
[18] M. Žic, V. Subotić, S. Pereverzyev, and I. Fajfar, “Solving CNLS problems using Levenberg-Marquardt algorithm: A new fitting strategy combining limits and a symbolic Jacobian matrix,” Journal of Electroanalytical Chemistry, vol. 866, Jun. 2020, doi: 10.1016/j.jelechem.2020.114171.
[19] R. García-Ródenas, L. J. Linares, and J. A. López-Gómez, “Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm,” Neural Computing and Applications, vol. 33, no. 7, pp. 2561–2588, Apr. 2021, doi: 10.1007/s00521-020-05131-y.
[20] S. Mutrofin, M. Mughniy Machfud, D. H. Satyareni, R. Venantius, H. Ginardi, and C. Fatichah, “KOMPARASI KINERJA ALGORITMA C4.5, GRADIENT BOOSTING TREES, RANDOM FORESTS, DAN DEEP LEARNING PADA KASUS EDUCATIONAL DATA MINING”, doi: 10.25126/jtiik.2020732665.
[21] L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: 10.1016/j.neucom.2020.07.061.
[22] N. L. W. S. R Ginantra, M. A. Hanafiah, A. Wanto, R. Winanjaya, and H. Okprana, “Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts,” IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012022, Feb. 2021, doi: 10.1088/1757-899x/1071/1/012022.
[23] F. Cichos, K. Gustavsson, B. Mehlig, and G. Volpe, “Machine learning for active matter,” Nature Machine Intelligence, vol. 2, no. 2, pp. 94–103, Feb. 2020, doi: 10.1038/s42256-020-0146-9.
[24] Sri Sivasubramaniya Nadar College of Engineering. Electronics and Communication Engineering Department and Institute of Electrical and Electronics Engineers, WiSPNET 2019 : 2019 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) : 21-23, March 2019.
[25] Q. H. Nguyen et al., “A novel hybrid model based on a feedforward neural network and one step secant algorithm for prediction of load-bearing capacity of rectangular concrete-filled steel tube columns,” Molecules, vol. 25, no. 15, Aug. 2020, doi: 10.3390/molecules25153486.
[26] A. Munther, R. Razif, M. AbuAlhaj, M. Anbar, and S. Nizam, “A preliminary performance evaluation of K-means, KNN and em unsupervised machine learning methods for network flow classification,” International Journal of Electrical and Computer Engineering, vol. 6, no. 2, pp. 778–784, Apr. 2016, doi: 10.11591/ijece.v6i1.8909.
[27] I. Fitriyaningsih and Y. Basani, “Flood Prediction with Ensemble Machine Learning using BP-NN and SVM,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 3, pp. 93–97, Jul. 2019, doi: 10.14710/jtsiskom.7.3.2019.93-97.
[28] S. R. Salkuti, “A survey of big data and machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 575–580, 2020, doi: 10.11591/ijece.v10i1.pp575-580.
[29] E. W. Prasetyo, N. Hidetaka, D. A. Prasetya, W. Dirgantara, and H. F. Windi, “Spatial Based Deep Learning Autonomous Wheel Robot Using CNN,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 11, no. 3, p. 167, Dec. 2020, doi: 10.24843/lkjiti.2020.v11.i03.p05.
[30] E. Yan, J. Song, C. Liu, J. Luan, and W. Hong, “Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2453–2481, Apr. 2020, doi: 10.1007/s10462-019-09738-z.
[31] R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 302–311, May 2020, doi: 10.30812/matrik.v19i2.688.
[32] N. Choiriyati, Y. Arkeman, and W. A. Kusuma, “Deep learning model for metagenome fragment classification using spaced k-mers feature extraction,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 234–238, Jul. 2020, doi: 10.14710/jtsiskom.2020.13407.
[33] P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” in Journal of Physics: Conference Series, Sep. 2019, vol. 1255, no. 1. doi: 10.1088/1742-6596/1255/1/012033.
[34] A. Panyafong, N. Neamsorn, and C. Chaichana, “Heat load estimation using Artificial Neural Network,” in Energy Reports, Feb. 2020, vol. 6, pp. 742–747. doi: 10.1016/j.egyr.2019.11.149.
[35] T. Afriliansyah and Z. Zulfahmi, “Prediction of Life Expectancy in Aceh Province by District City Using the Cyclical Order Algorithm,” International Journal of Information System & Technology Akreditasi, vol. 3, no. 36, pp. 268–275, 2020.
[2] M. Tyrtaiou, A. Papaleonidas, A. Elenas, and L. Iliadis, “Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks,” 2020, pp. 85–98. doi: 10.1007/978-3-030-48791-1_6.
[3] S. Sonang, S. Defit, and M. Ramadhan, “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan”, [Online]. Available: https://www.bps.go.id/.
[4] I. B. K. Sudiatmika, F. Rahman, Trisno, and Suyoto, “Image forgery detection using error level analysis and deep learning,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 653–659, Apr. 2019, doi: 10.12928/TELKOMNIKA.V17I2.8976.
[5] “6 Application of Artificial Neural Networks for Airline Number of Passenger Estimation in time Series state”.
[6] M. D. Yalidhan and M. F. Amin, “IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 05, no. 02, 2018.
[7] M. Attamimi, R. Mardiyanto, and A. N. Irfansyah, “Inclined image recognition for aerial mapping using deep learning and tree based models,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 6, pp. 3034–3044, Dec. 2018, doi: 10.12928/TELKOMNIKA.v16i6.10157.
[8] F. Zola, G. W. Nurcahyo, and J. Santony, “JARINGAN SYARAF TIRUAN MENGGUNAKAN ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI PRESTASI SISWA,” vol. 1, no. 1, 2018.
[9] C. K. Arthur, V. A. Temeng, and Y. Y. Ziggah, “Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction,” Ghana Mining Journal, vol. 20, no. 1, pp. 20–33, Jul. 2020, doi: 10.4314/gm.v20i1.3.
[10] N. Sianturi, “Jaringan Syaraf Tiruan Pengaruh Infuse Pada Berat Badan Pria dengan Menggunakan Metode Backpropagation,” Journal of Computer System and Informatics (JoSYC), vol. 1, no. 3, pp. 134–140, 2020.
[11] I. Waspada, A. Wibowo, and N. S. Meraz, “SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER,” Jurnal Ilmu Komputer dan Informasi, vol. 10, no. 2, p. 108, Jun. 2017, doi: 10.21609/jiki.v10i2.481.
[12] M. Adi et al., “PENERAPAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI SAWAH MENURUT KABUPATEN/KOTA DI SUMATERA UTARA,” vol. 4, no. 1, pp. 77–86.
[13] C. T. Chen and G. X. Gu, “Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning,” Advanced Science, vol. 7, no. 5, Mar. 2020, doi: 10.1002/advs.201902607.
[14] N. Nafi’iyah, R. A. Ahmad, and S. Mujilahwati, “Prediksi Nilai Calon Mahasiswa dengan Algoritma Backpropagation (Studi Kasus: Data Kaggle),” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 3, no. 1, pp. 9–17, Apr. 2020, doi: 10.32672/jnkti.v3i1.1945.
[15] R. Novickis, D. J. Justs, K. Ozols, and M. Greitāns, “An approach of feed-forward neural network throughput-optimized implementation in FPGA,” Electronics (Switzerland), vol. 9, no. 12, pp. 1–16, Dec. 2020, doi: 10.3390/electronics9122193.
[16] I. C. Afolabi, S. I. Popoola, and O. S. Bello, “Modeling pseudo-second-order kinetics of orange peel-paracetamol adsorption process using artificial neural network,” Chemometrics and Intelligent Laboratory Systems, vol. 203, Aug. 2020, doi: 10.1016/j.chemolab.2020.104053.
[17] J. Bilski, B. Kowalczyk, A. Marchlewska, and J. M. Zurada, “Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks,” Journal of Artificial Intelligence and Soft Computing Research, vol. 10, no. 4, pp. 299–316, Oct. 2020, doi: 10.2478/jaiscr-2020-0020.
[18] M. Žic, V. Subotić, S. Pereverzyev, and I. Fajfar, “Solving CNLS problems using Levenberg-Marquardt algorithm: A new fitting strategy combining limits and a symbolic Jacobian matrix,” Journal of Electroanalytical Chemistry, vol. 866, Jun. 2020, doi: 10.1016/j.jelechem.2020.114171.
[19] R. García-Ródenas, L. J. Linares, and J. A. López-Gómez, “Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm,” Neural Computing and Applications, vol. 33, no. 7, pp. 2561–2588, Apr. 2021, doi: 10.1007/s00521-020-05131-y.
[20] S. Mutrofin, M. Mughniy Machfud, D. H. Satyareni, R. Venantius, H. Ginardi, and C. Fatichah, “KOMPARASI KINERJA ALGORITMA C4.5, GRADIENT BOOSTING TREES, RANDOM FORESTS, DAN DEEP LEARNING PADA KASUS EDUCATIONAL DATA MINING”, doi: 10.25126/jtiik.2020732665.
[21] L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: 10.1016/j.neucom.2020.07.061.
[22] N. L. W. S. R Ginantra, M. A. Hanafiah, A. Wanto, R. Winanjaya, and H. Okprana, “Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts,” IOP Conference Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012022, Feb. 2021, doi: 10.1088/1757-899x/1071/1/012022.
[23] F. Cichos, K. Gustavsson, B. Mehlig, and G. Volpe, “Machine learning for active matter,” Nature Machine Intelligence, vol. 2, no. 2, pp. 94–103, Feb. 2020, doi: 10.1038/s42256-020-0146-9.
[24] Sri Sivasubramaniya Nadar College of Engineering. Electronics and Communication Engineering Department and Institute of Electrical and Electronics Engineers, WiSPNET 2019 : 2019 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) : 21-23, March 2019.
[25] Q. H. Nguyen et al., “A novel hybrid model based on a feedforward neural network and one step secant algorithm for prediction of load-bearing capacity of rectangular concrete-filled steel tube columns,” Molecules, vol. 25, no. 15, Aug. 2020, doi: 10.3390/molecules25153486.
[26] A. Munther, R. Razif, M. AbuAlhaj, M. Anbar, and S. Nizam, “A preliminary performance evaluation of K-means, KNN and em unsupervised machine learning methods for network flow classification,” International Journal of Electrical and Computer Engineering, vol. 6, no. 2, pp. 778–784, Apr. 2016, doi: 10.11591/ijece.v6i1.8909.
[27] I. Fitriyaningsih and Y. Basani, “Flood Prediction with Ensemble Machine Learning using BP-NN and SVM,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 3, pp. 93–97, Jul. 2019, doi: 10.14710/jtsiskom.7.3.2019.93-97.
[28] S. R. Salkuti, “A survey of big data and machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 575–580, 2020, doi: 10.11591/ijece.v10i1.pp575-580.
[29] E. W. Prasetyo, N. Hidetaka, D. A. Prasetya, W. Dirgantara, and H. F. Windi, “Spatial Based Deep Learning Autonomous Wheel Robot Using CNN,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 11, no. 3, p. 167, Dec. 2020, doi: 10.24843/lkjiti.2020.v11.i03.p05.
[30] E. Yan, J. Song, C. Liu, J. Luan, and W. Hong, “Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2453–2481, Apr. 2020, doi: 10.1007/s10462-019-09738-z.
[31] R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 302–311, May 2020, doi: 10.30812/matrik.v19i2.688.
[32] N. Choiriyati, Y. Arkeman, and W. A. Kusuma, “Deep learning model for metagenome fragment classification using spaced k-mers feature extraction,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 234–238, Jul. 2020, doi: 10.14710/jtsiskom.2020.13407.
[33] P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” in Journal of Physics: Conference Series, Sep. 2019, vol. 1255, no. 1. doi: 10.1088/1742-6596/1255/1/012033.
[34] A. Panyafong, N. Neamsorn, and C. Chaichana, “Heat load estimation using Artificial Neural Network,” in Energy Reports, Feb. 2020, vol. 6, pp. 742–747. doi: 10.1016/j.egyr.2019.11.149.
[35] T. Afriliansyah and Z. Zulfahmi, “Prediction of Life Expectancy in Aceh Province by District City Using the Cyclical Order Algorithm,” International Journal of Information System & Technology Akreditasi, vol. 3, no. 36, pp. 268–275, 2020.
Published
2022-06-30
How to Cite
SONANG, Sahat; PURBA, Arifin Tua; SIRAIT, Sarida.
PREDIKSI PRESTASI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA BACKPROPAGATION.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 5, n. 1, p. 67-77, june 2022.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/512>. Date accessed: 08 july 2025.
doi: https://doi.org/10.37600/tekinkom.v5i1.512.
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