The writer has seen that so far signatures are just validated manually, so there is possibility to create a system for hand signature recognition. The objective of this research is to improve the method for hand signature recognition using combination method with different characteristic. Contour and slope used for special feature in this research. Contour and slope from image will be applied using Dynamic Time Warping (DTW). Another extraction feature that been used was Polar Fourier Transform (PFT). The method employed for classification are Support Vector Machine (SVM).From the research results, the writer obtains the fact that the combination between the DTW and PFT using SVM classification, provide the better results in verification of an authentic hand signature with the accuracy of 93.23%. it is expected that from this research, the results can be utilized in the process of verification of an authentic hand signature in near future dailylife.
This paper describes a login system utilizing Two Factor Authentication and Zero Knowledge Proof using Schnorr NIZK. The proposed system is designed to prevent password leak when being sent over insecure network or when used in an untrusted devices. Zero Knowledge Proof is used for maintaining the confidentiality of the password and Two Factor Authentication is used to secure login process on untrusted devices. The proposed system has been tested and initial results indicates that such system is able to secure the login process without leaking the user's password.
Temperature control of air conditioner devices is still oriented to the target environment. This control mode ignores one's physiological condition. A person's thermal comfort varies when indoors. Thermal comfort is closely related to environmental thermal satisfaction conditions. EEG signal is a signal that can reflect brain activity. This research objective is to provide classifier model for classifying person's thermal comfort based on EEG signal. This research used three conditions of a room's temperature. The features used by classifier are an average frequency band, HFD, PFD, and MSE features. Classifier performance was assessed using ROC curve evaluation. The results of the classification of thermal comfort levels with EEG signals with the KNN classifier are obtained only by using the band frequency average feature, which is equal to 0.878 with a standard deviation of 0.022. While the SVM classifier gets the highest performance by using a combination of the average band + HFD frequency feature, which is 0.877 with a standard deviation of 0.013 in the linear kernel and RBF.
AbstrakKontrol suhu ruangan yang ada telah dikembangkan saat ini hanya memanfaatkan keadaan lingkungan/ruangan sebagai parameter untuk mengontrol suhu ruangan. Model kontrol seperti ini mengabaikan kondisi fisologis dan psikologi seseorang yang sedang berada di dalam ruangan. Setiap orang memiliki tingkat kenyamanan yang berbeda-beda saat berada di dalam ruangan berpendingin. Kondisi nyaman tersebut dipengeruhi oleh termoregulasi seseorang untuk memenuhi kenyamanan termal. Sinyal EEG merupakan sinyal yang mampu merefleksikan aktivitas otak. Karena kenyamanan termal erat kaitannya dengan kondisi kepuasan pikirian atas kondisi termal lingkungan, maka sinyal EEG ini dirasa mampu merefleksikan tingkat kenyamanan termal seseorang. Penelitian ini akan menganalisa tingkat kenyamanan termal seseorang berbasis sinyal EEG. Sinyal EEG direkam menggunakan alat mindwave neurosky headset. Sinyal EEG direkam dalam kondisi rileks. Hasil data sinyal yang didapat akan dianalisis dan dilatihkan pada classifier SVC. Pengujian dilakukan dengan menggunakan metode k-fold validation dengan menggunakan nilai k = 5. Analisis lebih lanjut terkait performa keseluruhan dari classifier tersebut menggunakan kurva ROC. Hasil yang didapat dengan analisis kurva ROC ialah performa classifier SVC sebesar 87%.Kata Kunci: support vector classifier, sinyal eeg, kenyamanan termal.
Contour and shape are the major point for signature recognition. There are a few approaches that have been used for shape detection, particularly to signature recognition. Combination methods of geometric features such as ratio, contour, shape or moment like Zernike moment, Moment Invariants (Hu) usually have been used to identifY the signature. One method never been used for signature recognition, Polar Fourier Transform. In this paper, a comparative study is conducted to compare three methods, Moment Invariants (Hu), Zernike Moment, and Polar Fourier Transform (PFT). Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used to classifY 20 person data set in each of which consists of 15 genuine signatures and 15 forgery signatures. The result shows that the methods using PFT and SVM achieves accuracy of 86.67%. Whilst the computational time of SVM is faster than MLP. The SVM method had an average value of 0.012 seconds for computational time.
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