Penggunaan Feature Space SMOTE Untuk Mengurangi Overfitting Akibat Imbalance Dataset
Abstract
Keywords
Full Text:
PDFReferences
C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021, doi: 10.1007/s12525-021-00475-2.
J. Naranjo-Torres, M. Mora, R. Hernández-García, R. J. Barrientos, C. Fredes, and A. Valenzuela, “A review of convolutional neural network applied to fruit image processing,” Appl. Sci., vol. 10, no. 10, 2020, doi: 10.3390/app10103443.
E. MUGABO and D. W. M. (PhD), “Develop an Extended Model of CNN Algorithm in Deep Learning for Bone Tumor Detection and its Application,” Int. J. Innov. Sci. Res. Technol., vol. 8, no. 10, 2023, doi: https://doi.org/10.5281/zenodo.10040584.
O. Joseph and W. O. Apena, “Development of Segmentation and Classification Algorithms for Computed Tomography Images of Human Kidney Stone,” J. Electron. Res. Appl., vol. 5, no. 5, pp. 1–10, 2021, doi: 10.26689/jera.v5i5.1196.
A. Indrawati, “Penerapan Teknik Kombinasi Oversampling Dan Undersampling Hybrid Oversampling and Undersampling Techniques To Handling Imbalanced Dataset,” JIKO(Jurnal Inform. dan Komputer), vol. 4, no. 1, pp. 38–43, 2021, doi: 10.33387/jiko.
X. Ying, “An Overview of Overfitting and its Solutions,” J. Phys. Conf. Ser., vol. 1168, no. 2, 2019, doi: 10.1088/1742-6596/1168/2/022022.
Y. Yan et al., “Oversampling for imbalanced data via optimal transport,” 33rd AAAI Conf. Artif. Intell. AAAI 2019, 31st Innov. Appl. Artif. Intell. Conf. IAAI 2019 9th AAAI Symp. Educ. Adv. Artif. Intell. EAAI 2019, vol. 33, no. 1, pp. 5605–5612, 2019, doi: 10.1609/aaai.v33i01.33015605.
T. Wongvorachan, S. He, and O. Bulut, “A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining,” Inf., vol. 14, no. 1, 2023, doi: 10.3390/info14010054.
S. Bej, N. Davtyan, M. Wolfien, M. Nassar, and O. Wolkenhauer, “LoRAS: an oversampling approach for imbalanced datasets,” Mach. Learn., vol. 110, no. 2, pp. 279–301, 2021, doi: 10.1007/s10994-020-05913-4.
C. Supriyanto, A. Salam, J. Zeniarja, and A. Wijaya, “Two-Stages Input Space Image Augmentation and Interpretable Technique for Accurate and Explainable Skin Cancer Diagnosis,” Computation, vol. 11, no. 12, p. 246, Dec. 2023, doi: 10.3390/computation11120246.
A. N. Kasanah, M. Muladi, and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 196–201, 2019, doi: 10.29207/resti.v3i2.945.
G. Gumelar, Q. Ain, R. Marsuciati, S. Agustanti Bambang, A. Sunyoto, and M. Syukri Mustafa, “Kombinasi Algoritma Sampling dengan Algoritma Klasifikasi untuk Meningkatkan Performa Klasifikasi Dataset Imbalance,” SISFOTEK Sist. Inf. dan Teknol., vol. 5, no. 1, pp. 250–255, 2021.
A. Nugroho and E. Rilvani, “Penerapan Metode Oversampling SMOTE Pada Algoritma Random Forest Untuk Prediksi Kebangkrutan Perusahaan,” Techno.Com, vol. 22, no. 1, pp. 207–214, 2023, doi: 10.33633/tc.v22i1.7527.
J. Sanjaya and M. Ayub, “Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 311–323, 2020, doi: 10.28932/jutisi.v6i2.2688.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.
D. Alzu’Bi et al., “Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/3861161.
A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, no. November, p. 100258, 2022, doi: 10.1016/j.array.2022.100258.
R. D. Ramadhani, A. N. A. Thohari, C. Cartiko, A. Junaidi, and T. G. Laksana, “Optimasi Akurasi Metode Convolutional Neural Network untuk Klasifikasi Kualitas Buah Apel Hijau,” J. Mnemon., vol. 6, no. 2, pp. 149–156, 2023, doi: https://doi.org/10.36040/mnemonic.v6i2.6730.
D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Electron., vol. 10, no. 20, pp. 1–28, 2021, doi: 10.3390/electronics10202470.
M. Resa Arif Yudianto, P. Sukmasetya, R. Abul Hasani, and D. Sasongko, “Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1367–1375, 2022, doi: 10.47065/bits.v4i3.2575.
DOI: https://doi.org/10.62411/tc.v23i2.10215
Article Metrics
Abstract view : 0 timesPDF - 0 times
Refbacks
- There are currently no refbacks.
Diterbitkan Oleh :
Jurnal Techno.Com terindex di :
Jurnal Teknologi Informasi Techno.Com (p-ISSN : 1412-2693, e-ISSN : 2356-2579) diterbitkan oleh LPPM Universitas Dian Nuswantoro Semarang. Jurnal ini di bawah lisensi Creative Commons Attribution 4.0 International License.