Interview with Peter Smith

Photographs by Todd Hido Interview with Peter Smith of Tokion Magazin

Pengaruh perbedaan suhu ekstraksi terhadap kekuatan gel, viskositas, dan rendemen gelatin ceker ayam kampung

Penelitian ini bertujuan untuk mengetahui pengaruh perbedaan suhu ekstraksi terhadap nilai kekuatan gel, viskositas dan rendemen gelatin dengan bahan baku ceker ayam kampung. Materi penelitian menggunakan ceker ayam kampung. Penelitian ini menggunakan rancangan acak lengkap (RAL) dengan 4 perlakuan dan 4 ulangan. Perlakuannya adalah perbedaan suhu ekstraksi T1=50oC, T2=60oC, T3=70oC, T4=80oC. Peubah yang dianalisis dalam penelitian adalah kekuatan gel, viskosita dan rendemen gelatin. Hasil analisis ragam menunjukkan bahwa perbedaan suhu ekstraksi memberikan pengaruh perbedaan yang sangat nyata (P<0,01) terhadap nilai kekuatan gel, viskositas dan rendemen gelatin ceker ayam kampung. Berdasarkan hasil analisis data dan pembahasan dapat disimpulkan bahwa gelatin ceker ayam kampung yang diekstraksi pada suhu 700C menghasilkan kualitas fisik gelatin yang baik dengan nilai kekuatan gel 72,3, viskositas 7,57 cP dan rendemen 13,60 %.Keywords: Ceker ayam kampung, Gelati

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them