KOMPARASI ALGORITMA DECISION TREE DAN LOGISTIC REGRESSION YANG DIOPTIMISASIKAN DENGAN BINARY DRAGONFLY ALGORITHM UNTUK MEMPREDIKSI PROSES PERSALINAN

RAHMAWATI, YULIA (2025) KOMPARASI ALGORITMA DECISION TREE DAN LOGISTIC REGRESSION YANG DIOPTIMISASIKAN DENGAN BINARY DRAGONFLY ALGORITHM UNTUK MEMPREDIKSI PROSES PERSALINAN. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Childbirth is one of the important challenges in the world of health because it is directly related to the safety of mothers and babies. This study aims to develop a more accurate and reliable prediction model in determining the type of delivery, either normal or caesarean, by optimizing the Decision Tree (DT) and Logistic Regression (LR) algorithms using Binary Dragonfly Algorithm (BDA). BDA, as a metaheuristic optimization algorithm, is used to improve the performance of both models through parameter optimization, reducing the risk of overfitting, and improving generalization ability. The dataset used comes from the open source DRYAD, which includes a wide range of medically relevant variables. The results show that the combination of DT and LR optimized with BDA is able to produce high prediction accuracy, supporting more precise and efficient medical decision-making. This research is expected to make a significant contribution in the application of machine learning for medical data analysis as well as open up opportunities for the development of prediction systems in the healthcare field. Keywords: Childbirth, Machine Learning, Decision Tree, Logistic Regression, Binary Dragonfly Algorithm Persalinan merupakan salah satu tantangan penting dalam dunia kesehatan karena berkaitan langsung dengan keselamatan ibu dan bayi. Penelitian ini bertujuan untuk mengembangkan model prediksi yang lebih akurat dan andal dalam memprediksi proses persalinan, baik normal maupun caesar, dengan mengoptimalkan algoritma Decision Tree (DT) dan Logistic Regression (LR) menggunakan Binary Dragonfly Algorithm (BDA). BDA, sebagai algoritma optimasi metaheuristik, digunakan untuk meningkatkan kinerja kedua model melalui optimasi parameter, mengurangi risiko overfitting, dan meningkatkan kemampuan generalisasi. Dataset yang digunakan berasal dari sumber terbuka DRYAD, yang mencakup berbagai variabel medis relevan. Hasil penelitian menunjukkan bahwa kombinasi DT dan LR yang dioptimalkan dengan BDA mampu menghasilkan akurasi prediksi yang tinggi, mendukung pengambilan keputusan medis yang lebih tepat dan efisien. Penelitian ini diharapkan memberikan kontribusi signifikan dalam aplikasi machine learning untuk analisis data medis serta membuka peluang pengembangan sistem prediksi di bidang kesehatan. Kata Kunci: Persalinan, Machine Learning, Decision Tree, Logistic Regression, Binary Dragonfly Algorithm.

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 039
NIM/NIDN Creators:	41821010115
Uncontrolled Keywords:	Persalinan, Machine Learning, Decision Tree, Logistic Regression, Binary Dragonfly Algorithm.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan > 613 Personal Health, Hygiene/Ilmu Kesehatan Umum, Kesehatan Individu, Promosi Kesehatan, Higienis Umum > 613.9 Birth Control and Sex Hygiene/Pembatasan Kelahiran dan Kesehatan Reproduksi
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	06 Aug 2025 02:14
Last Modified:	06 Aug 2025 02:14
URI:	http://repository.mercubuana.ac.id/id/eprint/96586

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