Klasifikasi Aktifitas Pada Human Activity Recognition Dataset Menggunakan Logistic Regression
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Abstract
Smartphone dan smartwatch telah menjadi perangkat yang diperlukan dalam kehidupan sehari-hari selama beberapa tahun terakhir. Smartphone yang tersebar pada masyarakat dilengkapi dengan berbagai sensor seperti Accelerometer dan Gyroscope yang dapat mengumpulkan data mentah. Pada penelitian sebelumnya, sensor tersebut diletakkan di berbagai posisi pada bagian tubuh manusia. Data ini dapat digunakan untuk melakukan Human Activty Recognition (HAR). HAR telah banyak diterapkan pada kehidupan kita sehari-hari seperti mendeteksi kesehatan, perilaku manusia dan pelacakan lokasi tindak kesehatan. Dataset yang digunakan pada penelitian ini menggunakan data dari UCI Machine Learning dengan 30 orang subject. Penelitian ini mengusulkan metode Logistic Regression dengan penambahan Hyperparameter untuk mendapatkan hasil akurasi yang lebih baik. Hasil ini memiliki peningkatan performa Logistic Regression dalam klasifikasi Human Activity Recognition dengan meraih nilai akurasi sebesar 95,92% pada semua aktivitas.
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