Prediction of Drug Users Based on Facial Scratching Pattern

Abstract

The current practice of drug inspection is usually carried out at school or university. This procedure, however, is not effective and efficient, as the urine samples are taken randomly. In many cases, the drug-taking student is not present or evades the urine or hair inspection. A predictive drug user tool is needed, where only suspected student drug users are selected for a urine test. In general, drug abuse constantly causes terrible damage to the skin lesions Since they damage the skin during hallucinations due to the effects of drugs. The Grey Level of Occurrence Matrix (GLCM) is used in this study to discover the scratch pattern. Our proposed GLCM is evaluated with 104 images collected from the Internet. Training data is generated from 88 images of people before and after the drug was collected from the Internet, and we set 16 image faces to test the prediction. The experiment shows that the prediction based on GLCM has better accuracy (81%) compared with the local binary pattern (LBP) which only reach up to 75%.

Analisis Kesuksesan Aplikasi Alpukat Betawi Menggunakan Metode Delone And McLean

“…Data collection merupakan proses pengumpulan, dan analisis berbagai informasi menggunakan teknik tertentu [9]. Tujuan utama dari data collection adalah untuk mengumpulkan data untuk dianalisis agar dapat membantu dalam pembuatan keputusan.…”

Penggunaan Aplikasi Deteksi Pecandu Narkoba Di Meruya Utara

“…Perubahan Wajah Pecandu Narkoba (sumber www.rehabs.com) Pemanfaatan teknologi informasi dapat dimanfaatkan untuk mengenali pecandu narkoba (Raghavendra et al, 2016). Penelitian sebelumnya telah dikembangkan aplikasi pengenalan pecandu narkoba menggunakan local binary pattern dan grey level co-occurrence matrix dengan tingkat akurasi diatas 80 % (Priambodo et al, 2021).…”

Sistem Pakar Deteksi Covid-19 Dengan Lie Detection Menggunakan Metode Circle Hough Transform

“…Deteksi wajah sendiri dapat dilakukan dengan berbagai cara, salah satunya menggunakan algoritma Viola-Jones. Algoritma Viola-Jones [22][23] [24][25] [26] merupakan algoritma yang paling banyak digunakan untuk mendeteksi wajah. Permasalahan yang terdapat dalam penghitungan fitur ini adalah Viola -Jones memiliki 60.000 jenis fitur yang berbeda.…”