Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan

Mustaqim Mustaqim
Budi Warsito
Bayu Surarso

Abstract


Combination of Synthetic Minority Oversampling Technique (SMOTE) and Backpropagation Neural Network to handle imbalanced class in predicting the use of contraceptive implants

 

 

Kegagalan akibat pemakaian alat kontrasepsi implan merupakan terjadinya kehamilan pada wanita saat menggunakan alat kontrasepsi secara benar. Kegagalan pemakaian kontrasepsi implan tahun 2018 secara nasional sejumlah 1.852 pengguna atau 4% dari 41.947 pengguna. Rasio angka kegagalan dan keberhasilan pemakaian kontrasepsi implan yang cenderung tidak seimbang (imbalance class) membuatnya sulit diprediksi. Ketidakseimbangan data terjadi jika jumlah data suatu kelas lebih banyak dari data lain. Kelas mayor merupakan jumlah data yang lebih banyak, sedangkan kelas minor jumlahnya lebih sedikit. Algoritma klasifikasi akan mengalami penurunan performa jika menghadapi kelas yang tidak seimbang. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan data kegagalan pemakaian kontrasepsi implan. SMOTE menghasilkan akurasi yang baik dan efektif daripada metode oversampling lainnya dalam menangani imbalance class karena mengurangi overfitting. Data yang sudah seimbang kemudian diprediksi dengan Neural Network Backpropagation. Sistem prediksi ini digunakan untuk mendeteksi apakah seorang wanita mengalami kehamilan atau tidak jika menggunakan kontrasepsi implan. Penelitian ini menggunakan 300 data, terdiri dari 285 data mayor (tidak hamil) dan 15 data minor (hamil). Dari 300 data dibagi menjadi dua bagian, 270 data latih dan 30 data uji. Dari 270 data latih, terdapat 13 data latih minor dan 257 data latih mayor. Data latih minor pada data latih diduplikasi sebanyak data pada kelas mayor sehingga jumlah data latih menjadi 514, terdiri dari 257 data mayor, 13 data minor asli, dan 244 data minor buatan. Sistem prediksi menghasilkan nilai akurasi sebesar 96,1% pada epoch ke-500 dan 1.000. Implementasi kombinasi SMOTE dan Neural Network Backpropagation terbukti mampu memprediksi pada imbalance class dengan hasil prediksi yang baik.

 

 

The failed contraceptive implant is one of the sources of unintended pregnancy in women. The number of users experiencing contraceptive-implant failure in 2018 was 1,852 nationally or 4% out of 41,947 users. The ratio between failure and success rates of contraceptive implant, which tended to be unbalanced (imbalance class), made it difficult to predict. Imbalance class will occur if the amount of data in one class is bigger than that in other classes. Major classes represent a bigger amount of data, while minor classes are smaller ones. The imbalance class will decrease the performance of the classification algorithm. The Synthetic Minority Oversampling Technique (SMOTE) was used to balance the data of the contraceptive implant failures. SMOTE resulted in better and more effective accuracy than other oversampling methods in handling the imbalance class because it reduced overfitting. The balanced data were then predicted using backpropagation neural networks. The prediction system was used to detect if a woman using a contraceptive implant was pregnant or not. This study used 300 data, consisting of 285 major data (not pregnant) and 15 minor data (pregnant). Of 300 data, two groups of data were formed: 270 training data and 30 testing data. Of 270 training data, 13 were minor training data and 257 were major training data. The minor training data in the training data were duplicated as much as the number of data in major classes so that the total training data became 514, consisting of 257 major data, 13 original minor data, and 244 artificial minor data. The prediction system resulted in an accuracy of 96.1% on the 500th and 1,000th epochs. The combination of SMOTE and Backpropagation Neural Network was proven to be able to make a good prediction result in imbalance class.


Keywords


backpropagation; imbalance class; implan; implants; predict; prediksi; SMOTE

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References


BKKBN, B. (2013). Pedoman penggerakan KB dan ayoman komplikasi serta kegagalan kontrasepsi. Jakarta: Direktorat Bina Kesertaan KB Jalur Pemerintah.

Budayawan, K., Yuhandri, & Nurcahyo, G. W. (2019). Implementasi Jaringan Syaraf Tiruan dalam Memprediksi Frekuensi Resonansi Atena Mikrostrip. JTIP: Jurnal Teknologi Informasi dan Pendidikan, 12(1), 33-40.

Chen, G., Fu, K., Liang, Z., Sema, T., Li, C., & Tontiwachwuthikul, P. (2014). The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel, 126(June), 202-212.

Chen, L., Fang, B., Shang, Z., & Tang, Y. (2018). Tackling class overlap and imbalance problems in software defect prediction. Software Quality Journal, 26(1), 97-125.

García, V., Sánchez, J. S., & Mollineda, R. A. (2012). On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowledge-Based Systems, 25(1), 13-21.

Gholami, M., Cai, N., & Brennan, R. (2013). An artificial neural network approach to the problem of wireless sensors network localization. Robotics and Computer-Integrated Manufacturing, 29(2013), 96–109.

He, H., & Ma, Y. (2013). Imbalanced Learning: Foundations, Algorithms, and Applications. Canada: Wiley.

Jian, C., Gao, J., & Ao, Y. (2016). A new sampling method for classifying imbalanced data based on support vector machine ensemble. Neurocomputing, 193(June), 115-122.

Li, H., & Sun, J. (2012). Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples–Evidence from the Chinese hotel industry. Tourism Management, 33(3), 622-634.

Liu, X.-Y., Li, Q.-Q., & Zhou, Z.-H. (2013). Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights. IEEE 13th International Conference on Data Mining. Dallas, TX, USA: IEEE.

Mutrofin, S., Mu'alif, A., Ginardi, R. V., & Fatichah, C. (2019). Solution of class imbalance of k-nearest neighbor for data of new student admission selection. International Journal Of Artificial Intelligence Research, 3(2), 47-55.

Purnamasari, R. W., Dwijanto, D., & Sugiharti, E. (2013). Implementasi Jaringan Syaraf Tiruan Backpropagation Sebagai Sistem Deteksi Penyakit Tuberculosis (TBC). Unnes Journal of Mathematics, 2(2).

Sanguanmak, Y., & Hanskunatai, A. (2016). Auto-tuning of parameters in hybrid sampling method for class imbalance problem. 2016 International Computer Science and Engineering Conference (ICSEC). Chiang Mai, Thailand: IEEE.

Sermpinis, G., Dunis, C., Laws, J., & Stasinakis, C. (2012). Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage. Decision Support Systems, 54(1).

Shen, L., Lin, Z., & Huang, Q. (2016). Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks. European Conference on Computer Vision (ECCV 2016) (pp. 467-482). Cham: Springer.

Susanto, A. T. (2012). Aplikasi Diagnosa Kanker Serviks dengan Menggunakan Algoritma Backpropagation. Kupang: STIKOM Uyelindo.

Thammasiri, D., Delen, D., Meesad, P., & Kasap, N. (2014). A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Systems with Applications, 41(2), 321-330.

Widodo, W., Rachman, A., & Amelia, R. (2014). Jaringan Syaraf Tiruan Prediksi Penyakit Demam Berdarah dengan Menggunakan Metode Backpropagation. Jurnal IPTEK, 18(1), 64-70.

Yap, B. W., Rani, K. A., Rahman, H. A., Fong, S., Khairudin, Z., & Abdullah, N. N. (2014). An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). 285, pp. 13-22. Singapore: Springer.

Zhang, D., Liu, W., Gong, X., & Jin, H. (2011). A Novel Improved SMOTE Resampling Algorithm Based on Fractal. Journal of Computer information Systems, 7(6), 2204-2211.

Zhu, T., Lin, Y., & Liu, Y. (2017). Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recognition, 72(December), 327-340.




DOI: https://doi.org/10.26594/register.v5i2.1705

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