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"Sistem tata udara presisi merupakan mesin refrigerasi yang digunakan di ruang pusat data untuk menjaga temperatur di dalam kabinet berkisar antara 20º - 22ºC, dan kelembaban antara 45-55%. Untuk mencapai keadaan tersebut, delapan variabel tak terukur belum dapat diestimasi sehingga dibutuhkan observer. Proses estimasi state dilakukan menggunakan model ruang keadaan. Persamaan untuk Filter Kalman dibagi menjadi persamaan time update dan measurement update. Penggunaan metode ini diharapkan diperoleh nilai matriks prediction error covarians yang konvergen pada nilai sekecil mungkin. Selain itu juga dibandingkan state hasil estimasi dengan state aktual model untuk mengetahui nilai kuadrat kesalahan estimasi yang terjadi.

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Precision air conditioning is a refrigeration machine that used in the data center to keep the temperature inside the cabinet ranged from 20 º - 22 º C, and humidity between 45-55%. To reach that state, the eight variables not measured can not be estimated so that the observer is required. State estimation process is done using a state space model. The equation for the Kalman Filter equations are divided into time update and measurement update. Use of this method is expected to obtain the prediction error matrix covarians which converges on the value as small as possible. It also compared to the estimated state with the actual state of the model to determine the value of the square of estimation error that occurred."

"Pergerakan harga saham berfluktuasi. Diperlukan suatu metode untuk mengestimasi pergerakannya. Model runtun waktu merupakan suatu model yang dapat digunakan dalam menganalisis pergerakan harga saham. Salah satu model runtun waktu sederhana adalah model local linear trend dengan level stokastik dan slope deterministik. Algoritma filter Kalman dapat digunakan untuk mengestimasi solusi model. Implementasi menggunakan data harga saham BMRI dan LSIP memberikan hasil yang cukup baik dilihat dari nilai rata – rata eror relatif dan nilai MSE (mean square error).

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The movements of stock price is fluctuate. A method is required to determine such movement. Time series model is one of the models that can be used in analyzing stock price movements. One of the basic model of time series is the local linear trend model with stochastic level and deterministic slope. Kalman filter algorithm can be used to estimate the solution of that model. Implementation on BMRI and LSIP stock price data gives satisfactory results based on the average relative error and MSE (mean square error)."

"This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic : as usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme, namely projective models, spurious rejection, model selection and self-calibration."

"Investor membutuhkan suatu model matematis yang dapat digunakan untuk memprediksi perubahan harga saham pada masa mendatang. Salah satu model yang sering digunakan oleh analis dalam memprediksi pergerakan harga saham adalah model runtun waktu. Model local level merupakan salah model runtun waktu dengan tujuan untuk mendapatkan karakteristik nilai komponen unobserved dari data observasi. Filter Kalman merupakan algoritma rekursif yang bertujuan menghitung komponen unobserved dengan variansi minimum dari suatu himpunan data. Skripsi ini membahas bagaimana mengestimasi solusi dan meramal data pada model local level menggunakan Filter Kalman. Implementasi menggunakan data harga saham historis bank BCA pada http://finance.yahoo.com/. menunjukkan bahwa hasil peramalan kurang baik. Peramalan yang kurang baik disebabkan karakteristik pergerakan harga saham bulanan yang digunakan.

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Investor needs a mathematical model to forecast future stock price changes. One of the mostly used models by stock analists to predict stock price movement is time series model. Local level model is one of the time series model which its goal is to obtain unobserved component characteristic from observation. Filter Kalman is a recursive algorithm to compute the unobserved component with the minimum variance from a set of past observations.

This scription shows how to estimate solution and forecast stock price in local level model by applying Kalman Filter. Implementation using BCA?s stock price at http://finance.yahoo.com/. show that estimation is very good and forecasting is less good. It is because the characteristic of stock price movement which was used."

"[ABSTRAKPenelitian ini bertujuan untuk meneliti perubahan persepsi investor terhadap risiko berinvestasi di Indonesia. Pada penelitian ini, deret waktu dari parameter risk aversion diestimasi menggunakan indeks saham Indonesia menggunakan data imbal hasil dengan frekuensi harian dari tahun 1990 hingga tahun 2015. Penelitian ini menggunakan model AR(1)-GARCH(1,1)-M untuk mengestimasi parameter risk aversion pada pasar saham Indonesia. Pemodelan yang dilakukan pada penelitian ini memodelkan parameter risk aversion dalam proses random walk. Penemuan dari penelitian ini menunjukkan bahwa premi risiko memiliki variansi sepanjang waktu dan mengindikasikan bahwa pasar saham Indonesia berpengaruh terhadap situasi perekonomian global.

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ABSTRACTThe purpose of this thesis is to examine the change in perception of risk by investors in Indonesia. In this research, the time series of risk aversion parameter is estimated for the Indonesian stock market using daily return data from year 1990 to 2015. This research makes use of AR(1)-GARCH(1,1)-M model to estimate the risk aversion parameter for Indonesian stock market. The model used in this research modelled the risk aversion parameter to follow a random walk process. The findings of this thesis show that the risk premium varies over time and indicate that the Indonesian stock market is vulnerable to global economy, The purpose of this thesis is to examine the change in perception of risk by investors in Indonesia. In this research, the time series of risk aversion parameter is estimated for the Indonesian stock market using daily return data from year 1990 to 2015. This research makes use of AR(1)-GARCH(1,1)-M model to estimate the risk aversion parameter for Indonesian stock market. The model used in this research modelled the risk aversion parameter to follow a random walk process. The findings of this thesis show that the risk premium varies over time and indicate that the Indonesian stock market is vulnerable to global economy]"

"Pesawat tanpa awak (Unmanned Aerial Vehicle atau disingkat UAV) adalah sebuah mesin terbang yang berfungsi dengan kendali jarak jauh secara autopilot. Penggunaan terbesar dari pesawat tanpa awak ini adalah dibidang militer untuk pengintaian, pengawasan, dan penyerangan. Dalam mendeteksi sebuah objek yang bergerak secara real-time oleh sebuah UAV, terdapat proses pengolahan sinyal yang kompleks dibandingkan apabila objeknya dalam keadaan diam. Ada beberapa masalah yang terdapat dalam proses deteksi objek bergerak pada UAV yang disebut uncertainty constraint factor (UCF) yaitu lingkungan, jenis objek, pencahayaan, kamera UAV, dan pergerakan (motion) objek. Salah satu masalah praktis yang menjadi perhatian beberapa tahun ini adalah analisis pergerakan (motion analysis). Pergerakan (Motion) dari sebuah objek pada setiap frame membawa banyak informasi tentang piksel dari objek bergerak yang memainkan peranan penting sebagai image descriptor. Pada tesis ini digunakan algoritma SUED (Segmentation using edge based dilation) untuk mendeteksi objek bergerak. Inti dari algoritma SUED adalah mengkombinasikan frame difference dan proses segmentasi secara bersama untuk mendapatkan hasil yang optimal dibanding dengan menggunakannya secara terpisah. Hasil simulasi menunjukkan peningkatkan performansi algoritma SUED dengan menggunakan kombinasi wavelet dan sobel operator pada deteksi tepinya yaitu jumlah frame untuk true positive meningkat sebesar 41 frame, kemudian false alarm rate yang didapatkan menurun menjadi 7 % dari 24 % apabila hanya menggunakan sobel operator. Kombinasi kedua metode tersebut juga dapat meminimalisir noise region yang mengakibatkan kesalahan dalam proses deteksi dan pelacakan. Hasil simulasi pelacakan objek bergerak dengan metode kalman filter bisa dilihat pada beberapa sampel yang diuji menunjukkan adanya penurunan kesalahan (error) centroid antara hasil deteksi dan hasil pelacakan objek bergerak.

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An unmanned aerial vehicle (UAV), commonly known as a drone and also referred by several other names is an aircraft without a human pilot aboard. The flight of UAVs may be controlled either autonomously by onboard computers or by the remote control of a pilot on the ground or in another vehicle. Unmanned aerial vehicle (UAV) usually is used in military field for reconnaissance, surveillance, and assault. To detect a moving object in real-time, there are complex processes than to detect the object that does not moving. There are some issues that faced in detection process of moving object in UAV, called constraint uncertainty factor (UCF) such as environment, type of object, illumination, camera of UAV, and motion of the object. One of the practical problems that become concern of researcher in the past few years is motion analysis. Motion of an object in each frame carries a lot of information about the pixels of moving objects which has an important role as the image descriptor.

In this thesis, we use SUED (Segmentation using edge-based dilation) algorithm to detect moving objects. The concept of the SUED algorithm is combining the frame difference and segmentation process to obtain optimal results than using them separately. The simulation results show the performance improvement of SUED algorithm using combination of wavelet and Sobel operator on edge detection, the number of frames for a true positive increased by 41 frames, then the false alarm rate decreased to 7% from 24% when only using Sobel operator. The combination of these two methods can also minimize noise region that effect detection and tracking process. The simulation results for tracking moving objects by Kalman filter show that there is error decreasing between detection and tracking process."

"This brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions, new proofs for results already established, new results within the subject, and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Brief has a short chapter on linear state space models and the Kalman filter. "