Rani Kurnia Putri
Universitas PGRI Adi Buana

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Aplikasi Petrinet Pada Sistem Identifikasi Sidik Jari Rani Kurnia Putri
Jurnal Sains dan Informatika Vol. 5 No. 2 (2019): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v5i2.183

Abstract

Sebagai sebuah model, petri net merupakan grafik dua arah yang terdiri dari place, transisi serta tanda panah yang menghubungkan keduanya. Disamping itu, untuk merepresentasikan keadaan sistem, token diletakkan pada place tertentu. Ketika sebuah transisi terpantik, token akan bertransisi sesuai tanda panah. (Place) menggambarkan aktivitas pada suatu sistem, (Transisi) menggambarkan transisi atau perpindahan dari setiap aktifitas, dan Token (benda bulat di dalam ) menggambarkan status dari aktifitas state tersebut. Pada penelitian ini, dibahas mengenai aplikasi petri net pada sistem pengenalan sidik jari menggunakan pendekatan aljabar max plus. Pendekatan aljabar max-plus mampu menentukan dan menganalisa sifat sistem pengenalan sidik jari dengan sinkronisasi. Dalam proses identifikasi sidik jari, satuan waktu yang digunakan adalah detik, dimana merupakan awal waktu proses, dan merupakan waktu selesai proses, sehingga ketika seluruh proses identifikasi telah dilalui, diperoleh nilai optimum untuk setiap aktifitas state.
Modified Multi-Kernel Support Vector Machine for Mask Detection Muhammad Athoillah; Evita Purnaningrum; Rani Kurnia Putri
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7873

Abstract

Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.