<![CDATA[ABSTRAKPupilometri merupakan metode pengukuran respons pupil terhadap stimulus. Kemampuan pupil mata dalam merespons cahaya diamati melalui pupillary light response (PLR). Penelitian mendapati PLR pasien demensia berbeda dengan pasien normal. Penelitian ini bertujuan merancang algoritma computer vision yang dapat mendeteksi pupil secara akurat, menampilkan respons pupil terhadap cahaya dalam bentuk grafik dan PLR pada sebuah aplikasi desktop, yang mengendalikan goggles berisi rangkaian kamera, pencahayaan, dan sensor jarak VL53L0X. Rekaman diproses dengan Local Binary Pattern (LBP) dan deteksi kontur untuk mendeteksi pupil. Data pengukuran diproses dan disimpan pada basis data lokal dan aplikasi web, sehingga tenaga medis dapat menentukan ada atau tidaknya gejala demensia pada pasien lansia. Tingkat ketelitian algoritma pengukuran pupil sebesar 73,33% yang didapatkan dari 30 kali pengujian.Kata kunci: computer vision, demensia, deteksi dini, pupillary light response, pupilometri ABSTRACTPupillometry is a method of measuring the pupil’s response towards stimulus. Pupil response to light is observed through pupillary light response (PLR). Research found that PLR values of patients suffering from dementia differ from that of normal patients. This study implements a computer vision algorithm that accurately detects the pupil, calculates, and shows its response towards light in graphs and PLR values on a desktop application which controls goggles that contain a camera, lighting setup, and the VL53L0X distance sensor. Video is processed using Local Binary Pattern (LBP) and contour detection to detect the pupil. Results are processed and saved in the local and web database, so experts can determine the presence of dementia symptoms in the elderly patient. The accuracy of the pupil detection algorithm is 73,33%, as obtained from 30 tests.Keywords: computer vision, dementia, early detection, pupillary light response, pupillometry]]>
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