Kehamilan pada ibu hamil memiliki beragam risiko selama prosesnya seperti preeklampsia, diabetes dan hipertensi gestational. Seiring dengan perkembangan teknologi dan pemanfaatan data, implementasi machine learning dalam pengembangan early diagnosis system untuk tingkat risiko kehamilan telah banyak dilakukan. Namun kendala dalam penerapan machine learning adalah sulitnya menemukan konfigurasi parameter yang tepat agar model machine learning mampu memberikan akurasi prediksi yang mumpuni. Pada penelitian ini diusulkan metode optimasi berbasis Bayesian untuk mengoptimalisasikan hyper-parameter dari model Decision Tree (DT) dan Extreme Gradient Boosting (XGB). Kedua model teroptimasi tersebut dilatih dan diuji dengan menggunakan data risiko kehamilan yang diperoleh dari hasil pengukuran medis pada ibu hamil. Dari hasil evaluasi diketahui terdapat pengaruh jumlah iterasi pada Bayesian Optimization (BO). Implementasi BO pada model Decision Tree (BODT) menunjukkan adanya sedikit peningkatan nilai performa dibandingan dengan penelitian sebelumnya. Sementara itu, capaian performa tertinggi diperoleh oleh kombinasi model XGB dan Bayesian (BOXGB) dimana capaian nilai akurasi pada model BOXGB yaitu 87% diikuti dengan nilai rata-rata presisi, recall, dan F1-score masing-masing sebesar 88%, 87%, dan 88%. Secara keseluruhan implementasi Bayesian Optimization mampu memberikan setelan hyper-parameter yang dapat meningkatkan kemampuan model machine learning khususnya dalam memprediksi tingkat risiko kehamilan pada ibu hamil berdasarkan data pengukuran klinis. Abstract During pregnancy process there are various risks such as preeclampsia, gestational diabetes and gestational hypertension. Along with the developments in technology as well as data science, the implementation of machine learning in early diagnosis system for pregnancy risk levels prediction has been widely carried out. However, there is a challenge in implementing machine learning, which is find the suitable yet effective parameter configuration in training machine learning model to provides better prediction accuracy. This research proposes a Bayesian-based Optimization (BO) method to tune up the hyper-parameters of Decision Tree (DT) and Extreme Gradient Boosting (XGB) models. These two optimized models were trained and tested using maternal risk dataset obtained from the clinical-based measurement on pregnant woman. From the evaluation result, it can be found that the number of iterations has high influence on the BO performance. The implementation of BO toward DT model has slight increase in performance result compared to the previous research. Meanwhile, the highest performance result achieved by the combination of BO and XGB (BOXGB) model where the proposed model reaches 87% of accuracy, followed by average value of precision, recall, and F1-score of 88%, 87%, and 88%, respectively. Overall, the implementation of BO is able to direct the hyper-parameter configuration which improves the machine learning performance especially in predicting maternal risk level based on clinical-based measurement data.
English
Kehamilan pada ibu hamil memiliki beragam risiko selama prosesnya seperti preeklampsia, diabetes dan hipertensi gestational. Seiring dengan perkembangan teknologi dan pemanfaatan data, implementasi machine learning dalam pengembangan early diagnosis system untuk tingkat risiko kehamilan telah banyak dilakukan. Namun kendala dalam penerapan machine learning adalah sulitnya menemukan konfigurasi parameter yang tepat agar model machine learning mampu memberikan akurasi prediksi yang mumpuni. Pada penelitian ini diusulkan metode optimasi berbasis Bayesian untuk mengoptimalisasikan hyper-parameter dari model Decision Tree (DT) dan Extreme Gradient Boosting (XGB). Kedua model teroptimasi tersebut dilatih dan diuji dengan menggunakan data risiko kehamilan yang diperoleh dari hasil pengukuran medis pada ibu hamil. Dari hasil evaluasi diketahui terdapat pengaruh jumlah iterasi pada Bayesian Optimization (BO). Implementasi BO pada model Decision Tree (BODT) menunjukkan adanya sedikit peningkatan nilai performa dibandingan dengan penelitian sebelumnya. Sementara itu, capaian performa tertinggi diperoleh oleh kombinasi model XGB dan Bayesian (BOXGB) dimana capaian nilai akurasi pada model BOXGB yaitu 87% diikuti dengan nilai rata-rata presisi, recall, dan F1-score masing-masing sebesar 88%, 87%, dan 88%. Secara keseluruhan implementasi Bayesian Optimization mampu memberikan setelan hyper-parameter yang dapat meningkatkan kemampuan model machine learning khususnya dalam memprediksi tingkat risiko kehamilan pada ibu hamil berdasarkan data pengukuran klinis. Abstract During pregnancy process there are various risks such as preeclampsia, gestational diabetes and gestational hypertension. Along with the developments in technology as well as data science, the implementation of machine learning in early diagnosis system for pregnancy risk levels prediction has been widely carried out. However, there is a challenge in implementing machine learning, which is find the suitable yet effective parameter configuration in training machine learning model to provides better prediction accuracy. This research proposes a Bayesian-based Optimization (BO) method to tune up the hyper-parameters of Decision Tree (DT) and Extreme Gradient Boosting (XGB) models. These two optimized models were trained and tested using maternal risk dataset obtained from the clinical-based measurement on pregnant woman. From the evaluation result, it can be found that the number of iterations has high influence on the BO performance. The implementation of BO toward DT model has slight increase in performance result compared to the previous research. Meanwhile, the highest performance result achieved by the combination of BO and XGB (BOXGB) model where the proposed model reaches 87% of accuracy, followed by average value of precision, recall, and F1-score of 88%, 87%, and 88%, respectively. Overall, the implementation of BO is able to direct the hyper-parameter configuration which improves the machine learning performance especially in predicting maternal risk level based on clinical-based measurement data.
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