Penerapan Metode GA-NM Pada Algoritma SVM Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah
- Irfan Fiqry Abror Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia
-
Taghfirul Yoga Azhima Siswa
*
Universitas Muhammadiyah Kalimantan Timur, Samarinda,
Indonesia
- Rudiman Rudiman Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia (*) Corresponding Author
Abstract
Class imbalance is a common challenge in data analysis, especially when the number of instances in the majority class significantly exceeds that in the minority class. This imbalance can cause classification models to favor the majority class, resulting in low accuracy in identifying the minority class. In this study, the Support Vector Machine (SVM) method combined with Near Miss and Genetic Algorithm (GA) is used to address the class imbalance problem in the scholarship recipient data of the Kartu Indonesia Pintar (KIP) program at Universitas Muhammadiyah Kalimantan Timur. The dataset consists of 1,075 records with 27 features representing the socio-economic factors of the scholarship recipients. Near Miss was applied to undersample the majority class, producing a more balanced data distribution. Subsequently, the SVM algorithm was utilized as the primary classification model, with feature selection and parameter optimization conducted using GA. The results indicate that the combination of SVM, Near Miss, and GA improved classification performance in identifying the minority class. The initial accuracy obtained without the method was 60.55% and after implementation it increased to 76.88%. This approach not only enhances the overall accuracy of the model but also ensures more stable performance, particularly for the minority class. Therefore, this study is expected to provide a significant contribution to the development of a more accurate and efficient scholarship selection system, as well as serve as a reference for future research in data mining and machine learning.
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References
R. Susetyoko, W. Yuwono, and E. Purwantini, “Model Klasifikasi Pada Seleksi Mahasiswa Baru Penerima KIP Kuliah Menggunakan Regresi Logistik Biner,” JIP (Jurnal Informatika Polinema), vol. 8, no. 4, 2023, doi: 10.33795/jip.v8i4.914.
M. I. Bachtiar, H. Suyono, M. Fauzan, and E. Purnomo, “75 Method Comparison In The Decision Support System Of A Scholarship Selection,” Jurnal Ilmiah KURSOR, vol. 11, no. 2, 2021, doi: 10.21107/kursor.v11i2.263.
A. Nata and S. Royal, “Analisis Sistem Pendukung Keputusan Dengan Model Klasifikasi Berbasis Machine Learning Dalam Penentuan Penerima Program Indonesia Pintar,” Journal of Science and Social Research, vol. 3, 2022, doi:10.54314/jssr.v5i3.1041.
M. Wang, J. Yu, M. Zhou, W. Quan, and R. Cheng, “Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM,” Sustainability (Switzerland), vol. 15, no. 24, Dec. 2023, doi: 10.3390/su152416833.
F. Hambidi Wiyanto, “Penerapan Senam Kaki Diabetes Terhadap Sensitivitas Kaki Pada Penderita Diabetes Melitus Di Wilayah Puskesmas Pucangsawit,” Public Health and Safety International Journal, vol. 3, no. 2, pp. 2715–5854, 2023, doi: 10.55642.
A. Indrawati, “Penerapan Teknik Kombinasi Oversampling Dan Undersampling Untuk Mengatasi Permasalahan Imbalanced Dataset,” JIKO (Jurnal Informatika dan Komputer), vol. 4, no. 1, pp. 38–43, 2021, doi: 10.33387/jiko.v4i1.2561.
V. Werner de Vargas, J. A. Schneider Aranda, R. dos Santos Costa, P. R. da Silva Pereira, and J. L. Victória Barbosa, “Imbalanced data preprocessing techniques for machine learning: a systematic mapping study,” Knowl Inf Syst, vol. 65, no. 1, pp. 31–57, 2023, doi: 10.1007/s10115-022-01772-8.
O. Liashenko, T. Kravets, and Y. Kostovetskyi, “Machine Learning and Data Balancing Methods for Bankruptcy Prediction,” Ekonomika , vol. 102, no. 2, pp. 28–46, 2023, doi: 10.15388/Ekon.2023.102.2.2.
E. Kocyigit, M. Korkmaz, O. K. Sahingoz, and B. Diri, “Enhanced Feature Selection Using Genetic Algorithm for Machine-Learning-Based Phishing URL Detection,” Applied Sciences (Switzerland), vol. 14, no. 14, Jul. 2024, doi: 10.3390/app14146081.
M. S. Hosen and S. S. Gutlapalli, “A Study of Innovative Class Imbalance Dataset Software Defect Prediction Methods,” Asian Journal of Applied Science and Engineering, vol. 10, no. 1, pp. 52–55, Dec. 2021, doi: 10.18034/ajase.v10i1.52.
I. R. Pratama, M. Maimunah, and E. R. Arumi, “Sistem Klasifikasi Penjualan Produk Alat Listrik Terlaris Untuk Optimasi Pengadaan Stok Menggunakan Naïve Bayes,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 4, p. 2135, Oct. 2022, doi: 10.30865/mib.v6i4.4418.
F. Sulianta, Basic Data Mining from A to Z. Feri Sulianta, 2023.
I. M. Hamdani, Nurhidayat, A. Karman, N. Fuady, and A. Hermina., “Edukasi dan Pelatihan Data Science dan Data Preprocessing,” INTISARI Jurnal Inovasi Pengabdian Masyarakat, vol. 2, no. 1, pp. 19–26, 2024, doi: 10.58227/intisari.v2i1.125.
John Baer, “Domain Specificity and the Limits of Creativity Theory,” The Journal of Creative Behavior vol. 1–46, 2023, doi: 10.1002/jocb.002.
T.-T. Wong and P.-Y. Yeh, “Reliable Accuracy Estimates from k -Fold Cross Validation,” IEEE Trans Knowl Data Eng, vol. PP, p. 1, Apr. 2020, doi: 10.1109/TKDE.2019.2912815.
Budhi Gustiandi, Langkah Awal Menguasai Bahasa Pemrograman Phyton. Penerbit BRIN, 2023. doi: 10.55981/brin.633.
S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 153–160, Oct. 2023, doi: 10.57152/malcom.v3i2.897.
J. V. Alegre-Requena, S. Sowndarya S. V., R. Pérez-Soto, T. M. Alturaifi, and R. S. Paton, “AQME: Automated quantum mechanical environments for researchers and educators,” Wiley Interdiscip Rev Comput Mol Sci, vol. 13, no. 5, pp. 1–18, 2023, doi: 10.1002/wcms.1663.
S. Katoch, S. S. Chauhan, and V. Kumar, “A review On Genetic Algorithm Past, Present, and Future,” Multimedia Tools and Applications, vol. 80, 2021, doi: 0.1007/s11042-020-10139-6.
W. R. Pratiwi and R. E. Putra, “Perbandingan Performa Algoritma GA-SVM dan BOA-SVM dalam Mengklasifikasi Artikel Berita Berbahasa Indonesia,” Journal of Informatics and Computer Science, vol. 02, 2021, doi: 10.26740/jinacs.v2n04.p252-258.
Pratiwi B, Handayani A, and Sarjana, “Pengukuran Kinerja Sistem Kualitas Udara Dengan Teknologi WSN Menggunakan Confusion Matrix,” JURNAL INFORMATIKA UPGRIS, Vol. 6, No. 2, 2020, doi: 10.26877/jiu.v6i2.6552.
R. M. Mathew and R. Gunasundari, “A Cluster-based Undersampling Technique for Multiclass Skewed Datasets,” Engineering, Technology and Applied Science Research, vol. 13, no. 3, pp. 10785–10790, Jun. 2023, doi: 10.48084/etasr.5844.
W. H. Lam, W. S. Lam, and P. F. Lee, “A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility,” Mathematics, vol. 12, no. 8, Apr. 2024, doi: 10.3390/math12081199.
Y. Dilla, E. Wawan, J. Pranoto, and N. Adzmi Verdikha, “Evaluasi Support Vector Machine Dengan Optimasi Metode Genetic Algorithm Pada Klasifikasi Banjir Kota Samarinda,” Jurnal Sains Komputer dan Teknologi Informasi, vol. 6, no. 1, 2023, doi: 10.33084/jsakti.v6i1.5462.
R. Syaputra, T. A. Y. Siswa, and W. J. Pranoto, “Model Optimasi SVM Dengan PSO-GA dan SMOTE Dalam Menangani High Dimensional dan Imbalance Data Banjir,” Teknika, vol. 13, no. 2, pp. 273–282, Jul. 2024, doi: 10.34148/teknika.v13i2.876.
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