The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.
The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink ** More information about this series at
Perkembangan Internet yang pesat saat ini menyebabkan tingginya tingkat resiko dalam pembajakan data. Salah satu cara mengamankan data dengan menggunakan kriptografi camellia. Camellia dikenal sebagai metode yang memiliki waktu enkripsi dan dekripsi yang cepat. Metode Camellia memiliki 3 macam besaran key yaitu 128-bit, 192-bit, dan 256-bit. Aplikasi ini dibuat dengan menggunakan bahasa pemrograman c++ dan menggunakan GUI visual studio 2010. Penelitian ini membandingkan besaran kunci terkecil dan terbesar yang digunakan pada file berekstensi .txt, .doc, .docx, .jpg, .mp4, .mkv, dan .flv. Aplikasi ini dibuat untuk mengetahui perbandingan waktu dan tingkat keamanan pada penggunaan kunci 128 bit dan 256 bit. Perbandingan keamanan dilakukan dengan membandingkan hasil nilai avalanche effect untuk kunci 128 bit dan 256 bit.
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