IMPLEMENTATION OF EIGENFACE ALGORITHM FOR IDENTIFICATION OF ANOPHELES IN SMARTPHONES

Alvianus Dengen

doi:10.20884/1.jutif.2022.3.1.158

Alvianus Dengen Teknik Elektro, Fakultas Teknik, Universitas Teknologi Sulawesi, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.1.158

Keywords: Android, Application, Anopheles, Eigenface Algorithm, Identification, Training Image

Abstract

The development of smartphones as tools used in research provides many benefits in several fields, including education, health, and agriculture. Digital image research developed in mobile apps can help us choose the best decision so that the results are by what has been planned. One of the developments in entomology is entomology research that uses digital imagery. In the dataset activity, researchers used the anopheles’s wings type as much as 22 image data. Fifteen types of Anopheles were used by the researcher with 100 experiments as test data. Image training and identification have the same process, taking pictures from a gallery or directly using the camera from an Android device. Preprocessing step in applications includes 1) converting image pixels to 100x100 pixels, then this pixel size becomes the standard of use in applications. 2) Convert an image to a binary using a threshold algorithm; this event is commonly called the Image Binaryization Process. Images derived from the threshold process are converted into one-dimensional vectors. Training Image and image identification using the Eigenface algorithm. The basic principle of the Eigenface algorithm is to quote the unique information of each image and then compare it to the image in the dataset. Identification with anopheles applications results in a good accuracy value with a success rate of 94.29%, with FMR value = 4.62% and FNMR = 2.78%.

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