Many methods are used to assess a person’s character and personality. Some are through the face, body movements, body language, handwriting and signatures. Assessing a person’s character and personality by looking at the type of handwriting and signature can be learned with the science of graphology. Detecting a person’s personality system based on a signature pattern automatically is still difficult. This study aims to build a system of predicting personality based on signature patterns using machine learning. A person’s personality based on a signature has many features. In this study the analysed features consist of four features, namely curve start, end streak, middle stroke, and underline. The steps taken in developing a prediction system are model training and model testing. The method used to extract features is Principle Component Analysis (PCA) and the method for classifying is Support Vector Machine (SVM). Based on the test results using confusion matrix produces an accuracy value of 71%. It can be concluded that machine learning can be implemented to predict personality based on signatures with good accuracy.
Saat ini penggunaan kecerdasan buatan berkembang dengan pesat, diantaranya dimanfaatkan untuk mengenali ekspresi wajah manusia. Ekspresi wajah manusia memiliki tingkat pengenalan yang kompleks. Pada penelitian ini akan diterapkan deep learning untuk mengetahui seberapa besar tingkat akurasi dalam pengenalan ekspresi wajah. Metode yang digunakan dalam penelitian ini yaitu gabungan Viola Jones dan Convolutional Neural Network. Viola Jones digunakan pada tahap segmentasi dan Convolutional Neural Network untuk mengklasifikasi data. Dataset ekspresi wajah yang dianalisis terdiri dari bahagia, merah, muak, sedih, takut, terkejut dan normal sejumlah 2205 data. Pengujian yang dilakukan menggunakan confussion matrix dengan tingkat akurasi sebesar 96,14%. Dari hasil pengujian ini menunjukan bahwa metode yang diusulkan memiliki akurasi yang baik untuk mengenali ekspresi wajah.
Facial expression is a form of nonverbal communication that can convey the emotional state of someone to the person who observes it. Research on the recognition of facial expressions is one of the interesting fields in computer science. This research aims to improve the accuracy of recognition performance. The process carried out in this research is to perform feature extraction and image classification. Markov Stationary Feature - Vector Quantization (MSF-VQ) method is used for feature extraction and Support Vector Machine (SVM) for image classification. Data set in used is 1440 data with six classifications of facial expressions. The results of the testing showed 97.41% which stated that this method could be recommended to be applied in the facial expressions recognition.
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