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Abstrak :
The cloud is an outstanding platform to deal with functionally equivalent services which are exponentially increasing day-by-day. The selection of services to meet the client requirements is a subtle task. The services can be selected by ranking all the candidate services using their network and non-network Quality-of-Service (QoS) parameters, which is formulated as a NP hard optimization problem. In this paper, we proposed a linear discriminant analysis (LDA) based a four level matching model for service selection based on QoS parameters, which includes description matching of a service, matchmaking phase, LDA-based QoS matching and ranking. The LDA-service selection agent is deployed on each cloud to classify services into classes and rank the services based on the aggregate QoS value of each service. Finally, the test results show the efficiency in service selection with minimal discovery overhead, significant reduction in the computation time and the number of candidate services to be considered.

Abstrak :
The cloud is an outstanding platform to deal with functionally equivalent services which are exponentially increasing day-by-day. The selection of services to meet the client requirements is a subtle task. The services can be selected by ranking all the candidate services using their network and non-network Quality-of-Service (QoS) parameters, which is formulated as a NP hard optimization problem. In this paper, we proposed a linear discriminant analysis (LDA) based a four level matching model for service selection based on QoS parameters, which includes description matching of a service, matchmaking phase, LDA-based QoS matching and ranking. The LDA-service selection agent is deployed on each cloud to classify services into classes and rank the services based on the aggregate QoS value of each service. Finally, the test results show the efficiency in service selection with minimal discovery overhead, significant reduction in the computation time and the number of candidate services to be considered.

Abstrak :
ABSTRAK Idiosyncratic volatility adalah salah satu asset pricing puzzle yang paling aktif diteliti. Sejumlah riset dilakukan untuk dapat menjawab apakah idiosyncratic volatility sepenuhnya dapat dijelaskan oleh fenomena lain, tak terkecuali dengan perubahan dalam nilai fundamental perusahaan. Penelitian ini bertujuan untuk menganalisis pengaruh ROE volatility terhadap idiosyncratic volatility sepanjang life cycle stage perusahaan non-keuangan yang tercatat di Bursa Efek Indonesia dan The Stock Exchange of Thailand selama periode 2011-2017.
Penelitian ini menggunakan ROE sebagai proksi dari volatilitas fundamental perusahaan. Pola arus kas berdasarkan Dickinson (2011) digunakan untuk mengklasifikasikan tahap life cycle perusahaan untuk kemudian diklasifikasikan ulang dengan menggunakan Multiclass Linear Discriminant Analysis guna mendapatkan proksi life cycle yang baik. Hasil penelitian menunjukkan bahwa tahap introduction dan growth mempengaruhi ROE volatility dalam meningkatkan dan menurunkan idiosyncratic volatility.

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ABSTRACT Idiosyncratic volatility is one of the most actively studied asset pricing puzzles. A number of studies have been conducted to be able to answer whether idiosyncratic volatility can be fully explained by other phenomena, including changes in the company's fundamental values. This study aims to analyze the effect of return on equity volatility on idiosyncratic volatility throughout life cycle stage of non-financial companies listed on the Indonesia Stock Exchange and The Stock Exchange of Thailand during the period of 2011- 2017.
The cash flow pattern based on Dickinson (2011) is used to classify the life cycle stage of the company to then be reclassified by using Multiclass Linear Discriminant Analysis to obtain a good proxy of life cycle. The results showed that the introduction and growth stages affected ROE volatility in increasing and decreasing idiosyncratic volatility.

Abstrak :
Brain-Computer Interface (BCI) merupakan sebuah sistem yang mampu menerjemahkan sinyal-sinyal otak menjadi perintah kepada berbagai devais keluaran. Teknologi ini kini sedang berkembang pesat terutama untuk keperluan rehabilitasi gerak bagi orang-orang yang telah kehilangan kemampuan geraknya. Dalam penelitian ini, dirancang sebuah sistem BCI yang mampu menerjemahkan sinyal otak seseorang ketika sedang melakukan pembayangan gerak (motor imagery) untuk gerakan tangan menggenggam dan membuka. Hasil terjemahan tersebut dapat digunakan untuk menggerakkan sebuah antarmuka yang membantu orang tersebut untuk bergerak menggenggam dan membuka tangan secara real-time. Sistem BCI ini menggunakan perangkat akuisisi data yang terdiri dari Raspberry Pi 4 dan ADS1299 Analog-to-Digital Converter. Sistem ini juga dikembangkan dengan menggunakan berbagai algoritma pemrosesan dan klasifikasi data, mulai dari Independent Component Analysis, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbours, dan Random Forest. Akurasi hasil testing klasifikasi yang dilakukan oleh sistem ini bernilai 64,6% untuk mengklasifikasi 3 jenis pembayangan gerak (menggenggam, membuka, dan diam) menggunakan algoritma SVM serta 94,7% untuk klasifikasi 2 jenis pembayangan gerak (menggenggam dan membuka) menggunakan algoritma Random Forest. ......Brain-Computer Interface (BCI) is a system which can translate brain signals to command various output devices. This technology had been developing rapidly, especially for movement rehabilitation purposes for people with motoric disabilities. In this research, a BCI system has been developed which can translate one’s brain signals when one is imagining doing hand movement (motor imagery). The translation result can be used to drive an interface in real-time. This BCI system utilize an acquisition device, consisting of Raspberry Pi 4 and ADS1299 Analog-to-Digital Converter. Besides, this system has also been developed using several algorithms for processing and classifying data, namely Independent Component Analysis, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbours, and Random Forest. Testing accuracy for this system yielded a 64.6% for classifying three types of motor imagery (hand grasping, hand opening, and resting) with SVM, and 94.7% for classifying two types of motor imagery (hand grasping and hand opening only) using Random Forest.