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"This book describes a methodology to represent socio-technical system concerns in the system architecting process. The resulting set of Human Views augments traditional system viewpoints with human-focused data. The Human Viewpoint methodology classifies the socio-technical system context, identifies and collects pertinent data, renders models that can be used for discussion and analysis, and presents the results in Fit for Purpose views that are useful for decision making. The inclusion of the Human Viewpoint during the system architecting stage allows the evaluation of human-system design trade-offs, recognises the impact of the human operator on system performance, and provides the foundation for Human System Integration evaluations during the ensuing system development."

""This textbook provides a comprehensive overview of the human-computer interface in clear, non-technical language, making it an ideal introduction for students of both psychology and computer science. Covering the past, present, and future developments in technology and psychology, it combines cutting-edge academic research with engaging illustrations and examples that show students how the material relates to their lives. Topics addressed include: human factors of input devices, and the basics of sensation and perception; memory and cognitive issues of users navigating their way through interfaces; communication via programming languages and natural speech interaction; cyberpathologies such as techno-stress and Internet addiction disorders; and challenges surrounding automation and artificial intelligence. This thoroughly updated second edition features new chapters on virtual reality and cybersecurity; expanded coverage of social media, mobile computing, e-learning, and video games; and end-of-chapter review questions that ensure students have mastered key objectives"--"Psychology as a science and a discipline must do more than merely acknowledge that we live in a digital environment with computers and automation. It must do more than add a footnote, chapter, or illustration to current texts while perpetuating theories developed in the pre-digital world. Instead, it must rethink its basic theories in every area - from sensory and perception to social and clinical. Fortunately, this is already occurring in many areas. Cognitive science and neuroscience were founded in the digital age, and human factors psychology has embraced the interaction with computers, but some areas have fallen behind. Rather than make too much of this now, instead we will develop and push these areas forward as we go through the successive chapters of this book. In doing so, we will try to cover the full range of psychology"--"

"ABSTRAK

Untuk menunjang pemantauan konsentrasi manusia, perlu adanya pemahaman mengenai respon sinyal dari EEG terhadap dua kondisi manusia ya itu saat sedang konsentrasi penuh dan konsentrasi tidak penuh (adanya distraksi). Dalam mengolah data sinyal EEG tersebut, dibutuhkan metode algoritma dan klasifikasi sinyal untuk mendapatkan hasil data sinyal dari dua kondisi tersebut. Pada penelitian ini akan dijelaskan tentang sistem perancangan pendeteksian konsentrasi manusia berdasarkan sinyal EEG. Metode yang digunakan adalah Fast Fourier Transform (FFT) dan Discrete Wavelet Transform (DWT) sedangkan dalam algoritma klasifikasinya menggunakan Support Vector Machine (SVM). Hasil yang telah didapatkan dalam pengujian ini adalah SVM lebih mampu untuk mengklasifikasikan sistem dengan kernel RBF menggunakan 30% holdout validation. Keakurasian dari sistem ini adalah 91% pada metode DWT dan 72% pada metode FFT. Sehingga, dari kedua ekstraksi metode FFT dan DWT, yang memiliki nilai ekstraksi terbaik adalah DWT.

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ABSTRACTTo support the monitoring focused human concentration, there is a need to understand the response of signals from EEG in two conditions which are when human is in full concentration and less concentration (presence of distraction).  To process those EEG signal data, an algorithm method and classification is needed to get the results of signal data from these two conditions In this research, the system of detecting design of human concentration levels based on EEG signals will be explained. The used methods are Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) while the classification algorithm uses Support Vector Machine (SVM).  The result of this research shows that by using SVM, a much more reliable result is achieved when a kernel RBF is used with 30% holdout validation. The result of the aforementioned method yields a 91% accuracy with DWT method and a 72% accuracy with FFT. 

 

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