Penanganan imbalance class data laboratorium kesehatan dengan Majority Weighted Minority Oversampling Technique

Meida Cahyo Untoro(1*), Joko Lianto Buliali(2),

(1) Institut Teknologi Sepuluh Nopember
(2) Institut Teknologi Sepuluh Nopember
(*) Corresponding Author
Meida Cahyo Untoro
Joko Lianto Buliali


Diagnosis suatu penyakit akan menjadi tepat jika didukung dengan berbagai proses mulai pengecekan awal (amannesa) sampai pengecekan laboratorium. Hasil dari proses laboratorium mempunyai informasi berbagai penyakit, akan tetapi beberapa jenis penyakit memiliki prevalensi rendah. Penyakit bervalensi rendah memiliki pengaruh dalam penanganan pasien lebih lanjut. Dengan rasio yang tidak seimbang data laboratorium akan menyebabkan nilai akurasi menjadi rendah dalam pengklasifikasian dan penanganan penyakit. Majority Weighted Minority Oversampling Technique (MWMOTE) adalah saalah satu cara untuk menyelesaikan imbalanced. Penelitian ini bertujuan menangani permasalahan ketidakseimbangan data laboratorium kesehatan sehingga diperoleh hasil pengklasifikasian penyakit dengan tingkat akurasi lebih tinggi. Hasil pada penelitian ini menunjukkan bahwa MWMOTE dapat meningkatkan akurasi untuk permasalahan ketidakseimbangan data sebesar 3,13%.




Diagnosis of a disease will be appropriate if supported by various processes ranging from initial checks (amannesa) to laboratory checks. Results from the laboratory process have information on various diseases, but some types of diseases have a low prevalence. Low-valvature disease has an effect in the treatment of the patient further. With an unbalanced ratio the laboratory data will cause the accuracy value to be low in the classification and handling of the disease. Majority Weighted Minority Oversampling Technique (MWMOTE) is one way to complete imbalanced. This study aims to address the problem of imbalance of health laboratory data to obtain the results of the classification of disease with a higher degree of accuracy. The results of this study indicate that MWMOTE can improve accuracy for data imbalance problems by 3.13%.


classification; data laboratory health; imbalanced; MWMOTE; data laboratorium kesehatan; klasifikasi

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