PENERAPAN METODE ENSEMBLE UNTUK MENINGKATKAN KINERJA ALGORITME KLASIFIKASI PADA IMBALANCED DATASET
Abstract
Pada bidang data mining sering kali para peneliti tidak memperhatikan keseimbangan distribusi kelas pada dataset. Hal ini dapat menimbulkan kesulitan yang cukup serius pada algoritme klasifikasi. karena secara teori mayoritas classifier mengasumsikan distribusi yang relatif seimbang, sehingga menyebabkan kinerja suatu algoritme klasifikasi menjadi kurang maksimal. Oleh karena itu, pada penelitian ini diterapkan metode ensemble dengan penambahan adaptive boosting untuk menyelesaikan permasalahan tersebut. Dari hasil pengujian yang dilakukan pada penelitian ini, metode ensemble dengan penambahan adaptive boosting dapat meningkatkan nilai kinerja algoritme klasifikasi. Nilai kinerja algoritme Naive Bayes dengan Adaptive Boosting akurasi yang dihasilkan sebesar 91.98%, sensitifitas sebesar 91.98%, spesifisitas sebesar 96.49%, dan g-mean sebesar 94.21%. Nilai kinerja algoritme Support Vector Machine dengan Adaptive Boosting akurasi yang dihasilkan sebesar 91.52%, sensitifitas sebesar 91.52%, spesifisitas sebesar 96.29%, dan g-mean sebesar 93.88%. Sedangkan Nilai kinerja algoritme Decision Tree dengan Adaptive Boosting akurasi yang dihasilkan sebesar 94.37%, sensitifitas sebesar 94.37%, spesifisitas sebesar 97.73%, dan g-mean sebesar 96.03%. Hal ini menunjukkan bahwa metode ensemble dengan Adaptive Boosting dapat menjadi solusi untuk meningkatkan kinerja algoritme pada imbalanced dataset.
Kata Kunci: adaptive boosting, data mining, ensemble, ketidakseimbangan kelas, klasifikasi.
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DOI: https://doi.org/10.33365/jti.v13i1.184
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