Pengaruh Jumlah Fitur pada Algoritma Machine Learning dalam Memprediksi Aktivitas Jatuh
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Published:
May 15, 2023
Keywords:
accelerometer, deteksi jatuh, ANOVA, machine learning
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Abstract
Mendeteksi terjadinya jatuh sangat penting dilakukan karena jatuh dapat memberikan dampak yang serius bagi kesehatan. Salah satu perangkat sensor yang dapat digunakan untuk menyediakan data aktivitas jatuh adalah sensor accelerometer. Data sensor tersebut perlu diektraksi menjadi fitur dan diklasifikasi menggunakan algoritma machine learning. Selain itu, untuk memilah dan memilih fitur, dan mengetahui kombinasi fitur yang relevan diperlukan algoritma seleksi fitur. Pada penelitian ini, jatuh dideteksi berdasarkan data sensor accelerometer tiga sumbu (x, y, dan z), data yang digunakan merupakan data publik. Data diekstraksi menggunakan fungsi statistik yang terdiri dari: minimum, maksimum, rata-rata, nilai tengah, dan standar deviasi. Terdapat 15 fitur yang akan dievaluasi oleh algoritma machine learning. Algoritma machine learning yang digunakan adalah: k-Nearest Neighbors (KNN), Decision Tree (D-Tree), Random Forest (RF), Support Vector Machine (SVM), AdaBoost, dan Gradient Boosting. Untuk mengevaluasi jumlah fitur yang paling optimal pada algoritma machine learning, seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA). Penggunaan fitur sebanyak 7, 8, 9, 10, dan 11 fitur menghasilkan performa machine learning yang paling optimal yang dicapai oleh machine learning: Decision Tree (D-Tree), Random Forest (RF), dan Gradient Boosting. Secara berturut-turut, ketiga classifier ini memiliki nilai accuracy, F-1, precision, recall, dan specificity adalah 1.000, 1.000, 1.000, 1.000, dan 1.000.
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How to Cite
Made Liandana, & I Made Darma Susila. (2023). Pengaruh Jumlah Fitur pada Algoritma Machine Learning dalam Memprediksi Aktivitas Jatuh. Jurnal Sistem Dan Informatika (JSI), 17(2), 109 - 120. https://doi.org/10.30864/jsi.v17i2.589
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