Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes
DOI:
https://doi.org/10.29408/edumatic.v8i1.25838Keywords:
adasyn, hard voting classifier, diabetes diseaseAbstract
Diabetes is a chronic disease that arises from excess sugar levels in the body and lack of exercise intensity resulting in a buildup in the blood. Indonesia ranks fifth as the country with the largest number of people with diabetes based on a report from the International Diabetes Federation (IDF). The reason is that people with diabetes do not realize that they have diabetes, so there is a need for early detection in knowing this. The purpose of this research is to improve the performance of the Hard Voting Classifier model combining the Decision Tree, Random Forest, and XGBoost algorithms with the ADASYN oversampling technique that handles data imbalance in diabetes prediction. This study uses patient information data with a total of 1000 data and 14 features from the Medical City Hospital laboratory, Iraq. The results of this study show an increase in the performance of the prediction model with an accuracy value of 99.0%, precision 99.1%, recall 99.0%, and f1-score 98.98% without using ADASYN. Then get an accuracy value of 99.8%, precision 99.8%, recall 99.8%, and f1-score 99.8% by using ADASYN as an oversampling technique. This shows that there is an increase in the performance of the Hard Voting Classifier model so that it produces accurate predictions of diabetes, where the correctness of diabetes prediction is very good.
References
Altaf, I., Butt, M. A., & Zaman, M. (2022). Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models. Review of Computer Engineering Research, 9(2), 71–82. https://doi.org/10.18488/76.v9i2.3037
Amri, Z., Kusrini, K., & Kusnawi, K. (2023). Prediksi Tingkat Kelulusan Mahasiswa menggunakan Algoritma Naïve Bayes, Decision Tree, ANN, KNN, dan SVM. Edumatic: Jurnal Pendidikan Informatika, 7(2), 187-196. https://doi.org/10.29408/edumatic.v7i2.18620
Andryan, M. R., & Fajri, M. (2022). Komparasi Kinerja Algoritma Xgboost Dan Algoritma Support Vector Machine (Svm) Untuk Diagnosa Penyakit Kanker Payudara. JIKO (Jurnal Informatika dan Komputer), 6(1), 1–5. https://doi.org/10.26798/jiko.v6i1.500
Armansyah, A., & Ramli, R. K. (2022). Model Prediksi Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 6(1), 1-10. https://doi.org/10.29408/edumatic.v6i1.4789
Atif, M., Anwer, F., & Talib, F. (2022). An Ensemble Learning Approach for Effective Prediction of Diabetes Mellitus Using Hard Voting Classifier. Indian Journal Of Science And Technology, 15(39), 1978–1986. https://doi.org/10.17485/IJST/v15i39.1520
Depari, D. H., Widiastiwi, Y., & Santoni, M. M. (2022). Perbandingan Model Decision Tree, Naive Bayes dan Random Forest untuk Prediksi Klasifikasi Penyakit Jantung. Informatik : Jurnal Ilmu Komputer, 18(3), 239–248. https://doi.org/10.52958/iftk.v18i3.4694
Efriadi, D., Rahmaddeni, R., Agustin, A., & Junadhi, J. (2022). Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor. Edumatic: Jurnal Pendidikan Informatika, 6(1), 49-57. https://doi.org/10.29408/edumatic.v6i1.5255
Fajri, F., Tholib, A., & Yuliana, W. (2022). Application of Machine Learning Algorithm for Determining Elective Courses in Informatics Study Program. Jurnal Teknik Informatika dan Sistem Informasi, 8(3), 485–496. https://doi.org/10.28932/jutisi.v8i3.3990
Gunawan, M. I., Sugiarto, D., & Mardianto, I. (2020). Peningkatan Kinerja Akurasi Prediksi Penyakit Diabetes Mellitus Menggunakan Metode Grid Seacrh pada Algoritma Logistic Regression. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 6(3), 280–284. https://doi.org/10.26418/jp.v6i3.40718
Hidayat, W., Utami, E., Iskandar, A. F., Hartanto, A. D., & Prasetio, A. B. (2021). Perbandingan Performansi Model pada Algoritma K-NN terhadap Klasifikasi Berita Fakta Hoaks Tentang Covid-19. Edumatic: Jurnal Pendidikan Informatika, 5(2), 167-176. https://doi.org/10.29408/edumatic.v5i2.3664
Irwansyah, I., & Kasim, I. S. (2021). Indentifikasi Keterkaitan Lifestyle Dengan Risiko Diabetes Melitus. Jurnal Ilmiah Kesehatan Sandi Husada, 10(1), 62–69. https://doi.org/10.35816/jiskh.v10i1.511
Kaope, C., & Pristyanto, Y. (2023). The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 227–238. https://doi.org/10.29408/edumatic.v5i2.3664
Kibria, H. B., Nahiduzzaman, M., Goni, Md. O. F., Ahsan, M., & Haider, J. (2022). An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI. Sensors, 22(19), 7268. https://doi.org/10.3390/s22197268
Kurniawan, R., Wintoro, P. B., Mulyani, Y., & Komarudin, M. (2023). Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik. Jurnal Informatika dan Teknik Elektro Terapan, 11(2), 233–236. https://doi.org/10.23960/jitet.v11i2.3034
Maniruzzaman, Md., Rahman, Md. J., Ahammed, B., & Abedin, Md. M. (2020). Classification and prediction of diabetes disease using machine learning paradigm. Health Information Science and Systems, 8(1), 7–24. https://doi.org/10.1007/s13755-019-0095-z
Manongga, D., Rahardja, U., Sembiring, I., Lutfiani, N., & Yadila, A. B. (2022). Dampak Kecerdasan Buatan Bagi Pendidikan. ADI Bisnis Digital Interdisiplin Jurnal, 3(2), 41–55. https://doi.org/10.34306/abdi.v3i2.792
Masacgi, G. N., & Rohman, M. S. (2023). Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita. Edumatic: Jurnal Pendidikan Informatika, 7(2), 455–464. https://doi.org/10.29408/edumatic.v7i2.23812
Naufal, M. F., & Kusuma, S. F. (2023). Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI). Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(4), 873–882. https://doi.org/10.25126/jtiik.20241046823
Nurani, R. D., & Waluyo, A. (2022). Edukasi Senam Kaki Diabetes Dalam Pencegahan Komplikasi Penderita Diabetes Mellitus. Jurnal Batikmu, 2(1), 86–89. https://doi.org/10.48144/batikmu.v2i1.1180
Permana, B. C., & Patwari, I. D. (2021). Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes. Infotek : Jurnal Informatika dan Teknologi, 4(1), 63–69. https://doi.org/10.29408/jit.v4i1.2994
Prayoga, P. R., Purnawansyah, P., Hasanuddin, T., & Darwis, H. (2023). Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor. Edumatic: Jurnal Pendidikan Informatika, 7(1), 160-168. https://doi.org/10.29408/edumatic.v7i1.17521
Setiawan, D., Nugraha, A., & Luthfiarta, A. (2024). Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree. Jurnal Media Informatika Budidarma, 8(1), 83–93.
Sharma, S., & Singhal, A. (2023, November). A Novel Heart Disease Prediction System Using XGBoost Classifier Coupled With ADASYN SMOTE. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 76-81). IEEE. https://doi.org/10.1109/ICCCIS60361.2023.10425095
Sinaga, H. H., & Agustian, S. (2022). Pebandingan Metode Decision Tree dan XGBoost untuk Klasifikasi Sentimen Vaksin Covid-19 di Twitter. Jurnal Nasional Teknologi dan Sistem Informasi, 8(3), 107–114. https://doi.org/10.25077/TEKNOSI.v8i3.2022.107-114
Wedashwara, W., Hidayat, A., Irmawati, B., & Zubaidi, A. (2022). Klasifikasi Teks menggunakan Genetic Programming dengan Implementasi Web Scraping dan Map Reduce. Edumatic: Jurnal Pendidikan Informatika, 6(1), 58-67. https://doi.org/10.29408/edumatic.v6i1.5274
Wicaksono, D. F., Basuki, R. S., & Setiawan, D. (2024). Peningkatan Performa Model Machine Learning XGBoost Classifier melalui Teknik Oversampling dalam Prediksi Penyakit AIDS. Jurnal Media Informatika Budidarma, 8(2), 736–747.
Yahyaoui, A., Jamil, A., Rasheed, J., & Yesiltepe, M. (2019). A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques. International Informatics and Software Engineering Conference (UBMYK), 1–4. IEEE. https://doi.org/10.1109/UBMYK48245.2019.8965556
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