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"In this volume, the design, manufacture and control technology of precision machines are introduced to achieve the concrete requirements for precision engineering. The state-of-the-art of precision machining processes including precision turning, milling, grinding and lapping/polishing are discussed. The key components of precision machines are introduced as well, such as precision spindles, control systems, tools and grinding wheels etc. For design part, methods for the design and simulation of the general structure of precision machines as well as the key components are described in details. For manufacture part, fabrication and assembly technologies for different types of precision machines are introduced. For control part, control system, measurement and compensation technology for precision machines are discussed. The information provided in the book will be of interest to industrial practitioners and researchers in the field of precision machines and their control."

"Skripsi ini membahas tentang cara mengoperasikan laboratorium maya dalam melakukan percobaan motor arus searah. Terdapat dua jenis percobaan yang dapat dilakukan dengan laboratorium maya ini. Percobaannya adalah percobaan motor tak berbeban dan percobaan menggunakan torsi sebagai beban. Selain itu ada juga prosedur dan parameter yang mempengaruhi hasil percobaan. Penelitian ini banyak menggunakan studi literatur yang didapatkan dari banyak sumber. Dari hasil penelitian, dapat disarankan agar pengguna mau mengikuti prosedur yang telah diberikan. Dengan demikian, percobaan dapat berjalan dengan maksimal dan hasil yang didapat sesuai dengan kebutuhan pengguna.

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This thesis primarily explains about how to operate virtual laboratory when doing experiment about DC motor. There are two experiments that can be done by using this virtual laboratory. Those experiments are motor no load experiment and experiment using torque as rated load. Besides those, there are also procedures and parameters which affect the results of experiments. This research uses a lot of literature study, which gained from many sources. From the result of this research, users are advised to follow procedures that are stated. By doing so, the experiment can operate maximally and the results obtained can satisfy the needs of the user."

"Skizofrenia adalah gangguan jiwa yang serius dan kronis. Penyakit ini ditandai dengan gangguan dalam pemikiran, persepsi, dan tingkah laku. Karena gangguan-gangguan ini dapat memicu penderita Skizofrenia untuk bunuh diri atau mencoba bunuh diri, penderita Skizofrenia mempunyai usia harapan hidup yang lebih rendah dari populasi umum. Skizofrenia juga sulit untuk didiagnosis karena belum ada tes secara fisik untuk mendiagnosisnya dan gejala-gejalanya sangat mirip dengan beberapa gangguan jiwa lainnya. Dengan menggunakan Northwestern University Schizophrenia Data, penelitian ini bertujuan untuk mengklasifikasikan orang yang menderita Skizofrenia dan orang yang tidak menderita Skizofrenia. Data tersebut terdiri dari 392 observasi dan 65 variabel yang merupakan data demografis dan data kuesioner Scale for the Assessment of Positive Symptoms dan Scale for the Assessment of Negative Symptoms yang diisi oleh klinisi. Metode klasifikasi yang digunakan adalah machine learning dengan metode Support Vector Machines SVM dan Twin Support Vector Machines Twin SVM menggunakan MATLAB R2017a. Simulasi dilakukan dengan data dan persentase data training dan testing yang berbeda-beda. Pada setiap simulai, akurasi serta running time diukur. Validasi dan evaluasi performa dari model yang telah dioptimasi dilakukan dengan mengambil rata-rata dari sepuluh kali Hold-Out Validation yang dilakukan. Pada umumnya, metode Twin SVM berhasil mengklasifikasikan data Skizofrenia dengan lebih akurat dibandingkan dengan metode SVM. Metode Twin SVM dengan kernel Gaussian menghasilkan hasil akhir akurasi klasifikasi data Skizofrenia yang terbaik, yaitu 91,0 . Berdasarkan hasil akhir running time, metode SVM dengan kernel Gaussian untuk klasifikasi data Skizofrenia mempunyai running time yang paling cepat, 0,664 detik. Selain itu, metode SVM dengan kernel linear, metode SVM dengan kernel Gaussian, dan metode Twin SVM untuk klasifikasi data Skizofrenia berhasil mencapai akurasi hingga 95,0 dalam setidaknya satu simulasi.

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Schizophrenia is a severe and chronic mental disorder. This disorder is marked with disturbances in thoughts, perceptions, and behaviours. Due to these disturbances that can trigger Schizophrenics to commit suicide or attempt to do so, Schizophrenics have a lower life expectancy than the general population. Schizophrenia is also difficult to diagnose as there is no physical test to diagnose it yet and its symptoms are very similar to several other mental disorders. Using Northwestern University Schizophrenia Data, this research aims to distinguish people who are Schizophrenics and people who are not. The data consists of 392 observations and 65 variables that are demographic data as well as clinician filled Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms questionnaires. Classification methods that are used are machine learning with Support Vector Machines SVM and Twin Support Vector Machine Twin SVM using MATLAB R2017a. Simulations are done with different data and percentage of training and testing data. In each simulation, accuracy and running time are measured. Performance validation and evaluation of the optimized models are done by taking the average of ten times Hold Out Validations that were done. In general, Twin SVM successfully classified Schizophrenia data more accurately than the SVM method. Twin SVM with Gaussian kernel produced the best final accuracy in classifying Schizophrenia data, 91.0 . Based on the final running time, SVM with Gaussian kernel has the fastest running time in classifying Schizophrenia data, 0.664 seconds. Furthermore, SVM with linear kernel, SVM with Gaussian kernel, and Twin SVM managed to reach an accuracy of 95.0 in at least one simulation in classifying Schizophrenia data."