Pengenalan Aktivitas Manusia dengan Seleksi Fitur Analysis of Variance (ANOVA) dan Mutual Information (MI) pada Data Sensor Accelerometer Berbasis Machine Learning
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Published:
May 31, 2024
Keywords:
Accelerometer, MI, ANOVA, Machine learning
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Abstract
Pengenalan aktivitas manusia telah banyak dikembangkan untuk berbagai keperluan, seperti kesehatan, olahraga, hingga pengawasan lanjut usia. Penggunaan perangkat sensor menjadi salah satu pilihan dalam melakukan pengenalan aktivitas manusia. Sensor accelerometer adalah salah satu perangkat yang umum digunakan dalam pengenalan aktivitas. Data sensor ini memerlukan teknik dan algoritma yang tepat sehingga menghasilkan hasil pengenalan aktivitas yang sesuai. Penggunaan tradisional machine learning menjadi salah satu teknik yang dapat digunakan, teknik ini memerlukan proses ekstraksi fitur, dan seleksi fitur. Teknik seleksi fitur mana dan berapa jumlah fitur yang tepat untuk mendapatkan performa machine learning yang optimal perlu dilakukan investigasi lebih lanjut. Pada penelitian ini, dilakukan evaluasi terhadap kombinasi sejumlah fitur menggunakan algoritma machine learning: Extreme Gradient Boosting (XGB), Gradient Boosting (GBoost), Random Forest (RF), Decision Tree (DT), dan Support Vector Machine (SVM. Dataset publik yang digunakan yaitu FORTH-TRACE. Sensor yang digunakan adalah sensor accelerometer. Fitur yang digunakan meliputi nilai minimum, nilai maksimum, nilai rata-rata, nilai tengah, standar deviasi, dan nilai interkuartil. Sedangkan seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA) dan Mutual Information (MI). Performa machine learning yang paling optimal ketika jumlah fitur 17 sampai dengan 18 fitur dengan akurasi 0,875, sedangkan performa machine learning paling optimal dicapai dengan menggunakan Extreme Gradient Boosting (XGB).
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Made Liandana, I Made Darma Susila, & Yohanes Priyo Atmojo. (2024). Pengenalan Aktivitas Manusia dengan Seleksi Fitur Analysis of Variance (ANOVA) dan Mutual Information (MI) pada Data Sensor Accelerometer Berbasis Machine Learning. Jurnal Sistem Dan Informatika (JSI), 18(2), 118-130. https://doi.org/10.30864/jsi.v18i2.606
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H. Hegazy, M. Abdelsalam, M. Hussien, S. Elmosalamy, Y. Hassan, A. Nabil, and A. Atia, “Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 2, p. 2021.
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S. S. Bangaru, C. Wang, S. A. Busam, and F. Aghazadeh, “ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors,” Autom Constr, vol. 126, p. 103653, Jun. 2021.
G. Tripathy and A. Sharaff, “AEGA: enhanced feature selection based on ANOVA and extended genetic algorithm for online customer review analysis,” Journal of Supercomputing, vol. 79, no. 12, pp. 13180–13209, Aug. 2023.
C. Vong, T. Theptit, V. Watcharakonpipat, P. Chanchotisatien, and S. Laitrakun, “Comparison of Feature Selection and Classification for Human Activity and Fall Recognition using Smartphone Sensors,” 2021 Joint 6th International Conference on Digital Arts, Media and Technology with 4th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, ECTI DAMT and NCON 2021, pp. 170–173, Mar. 2021.
K. Karagiannaki, A. Panousopoulou, and P. Tsakalides, “A benchmark study on feature selection for human activity recognition,” UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 105–108, Sep. 2016.
