Penerapan Convolutional Neural Network (CNN) dan Euclidean Distance Matrices (EDM) untuk Mengurangi False Positive pada Pengenalan Aktifitas Finger Point Call
DOI:
https://doi.org/10.26418/jp.v9i1.61716Keywords:
activity recognition, finger point call, YOLOv7, false positive, euclidean distance matricesAbstract
Aktifitas finger point call (FPC) yang mengharuskan operator menunjuk (finger point) dan mengucapkan (call) sebelum menjalankan suatu proses, merupakan aktifitas yang umum diterapkan di industri manufaktur khususnya pada perusahaan Jepang. FPC terbukti efektif mengurangi human error, tetapi operator sering tidak konsisten dalam menerapkan FPC sehingga perlu sistem untuk mendeteksi aktifitas FPC sudah dilakukan dengan baik dan benar. Salah satu metode pengenalan aktifitas (activity recognition) yaitu menggunakan convolutional neural networks (CNN) untuk mengklasifikasikan aktifitas manusia. Namun, aktifitas FPC dinyatakan valid atau invalid setelah memastikan operator menunjuk dengan benar ke arah objek dan menunjuk ke arah referensi, sehingga harus dilakukan analisis pada beberapa frame video. Apabila hanya menggunakan CNN saja, akan menyebabkan tingkat false positive menjadi tinggi, karena CNN akan langsung melakukan analisis pada 1 frame video. Tujuan penelitian ini yaitu mengurangi false positive ketika mendeteksi aktifitas FPC dengan cara melakukan anlaisis lebih lanjut pada hasil deteksi menggunakan euclidean distance matrices (EDM). Hasil penelitian menunjukkan pada percobaan yang diperagakan oleh 1 orang: false positive berkurang hingga 100%, nilai Precision sebesar 1, dan nilai recall sebesar 0,96. Hasil ketika diperagakan oleh 10 orang: nilai Precision sebesar 0,9, dan nilai recall sebesar 0,9. lebih baik dibandingkan YOLOv7 versi original yang nilai Precisionnya hanya sebesar 0,5.
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