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"Sistem profiling kendaraan, telah dibuat untuk mendeteksi golongan kendaraan yang melewati pintu tol berdasarkan jumlah roda. Sistem dilengkapi oleh 64 transmitter dan receiver infra merah. LED infra merah digunakan sebagai transmitter dan TSOP 34838 sebagai receiver. Sedangkan mikrokontroler yang digunakan dalam sistem ini adalah ATMega8535. Data dari receiver dibaca oleh mikrokontroler, lalu dikirim ke PC untuk kemudian ditampilkan melalui GUI (Graphical User Interface) yang dibuat dengan bahasa pemrograman LabVIEW. Sistem didesain agar dapat menghasilkan profil tampak samping kendaraan dan menggolongkan kendaraan dalam golongan 1, 3, 4 atau 5. Hasil yang ditampilkan pada GUI LabVIEW berupa profil serta informasi tentang kendaraan yang lewat, diantaranya golongan, biaya yang harus dibayarkan, serta waktu lewatnya kendaraan tersebut.

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Vehicle profiling system has been designed in order to classify vehicle that passes through freeway gates based on the amount of the wheels. This system is equipped with 64 transmitters and receivers. Microcontroller will collect data from receiver then send it to PC. The data will be processed and displayed via GUI (Graphic User Interface) with LabVIEW. LabVIEW was used as the user interface display. The system was designed to produce an image of vehicle’s side profile and classify the vehicle within group 1, 3, 4, or 5. LabVIEW as GUI will display vehicle image and information about the vehicle’s class, the amount of fees to be paid, as well as the time when the vehicle passes the sensor."

"Multivariate Analysis for the Social Sciences provides clear guidelines combined with the insight needed to understand the methods and applications of multivariate statistics. This easy-to-follow book provides students in social, behavioral, and health science-whose focus is not primarily in mathematics-with an abundance of chapter-ending questions and answers, including: conceptual questions about the meaning of each method; questions that test the reader?s ability to carry out the computational procedures on simple datasets; and data analysis questions for using analytical packages to analyze both simplest case data and also to practice with more realistic datasets."

"Mental disorders are predicted to increase every year. Patients with severe mental disorders, such as schizophrenia, often engage in violent behavior. The treatment of such patients can use general nursing treatments (anger management with physical therapy, taking medicines regularly, and verbal and spiritual methods) and specialist nursing interventions (assertive training and family psychoeducation). This case report involved 11 patients, with the majority aged between 26 and 60 years, unemployed, high school graduates, unmarried, and with previous inpatient history. Generalist and specialist nursing interventions (assertive training and family psychoeducation) use Roy’s adaptation theory and Stuart’s stress adaptation approach. Nursing interventions were conducted sequentially, starting with generalist nursing interventions, followed by specialist ones. The method used was a pre–posttest in which each patient received generalist and specialist nursing interventions, assertive training, and family psychoeducation, each consisting of five sessions. Results of assertive training therapy and family psychoeducation showed a decrease in the signs and symptoms of violent behavior as well as an improvement in the patient’s ability to overcome the risk of violent behavior. The application of Roy’s adaptation theory and Stuart’s stress adaptation approach is potentially appropriate for the treatment of patients with a risk of violent behavior."

"Artikel ini bertujuan untuk membahas mengenai pengaruh antara kekerasan orangtua terhadap perilaku kekerasan anak. Dalam beberapa tahun belakangan, kasus kekerasan yang dilakukan oleh anak- anak cenderung meningkat. Studi- studi sebelumnya memaparkan bahwa tayangan kekerasan, teman sebaya, dan orangtua sebagai faktor yang menentukan perilaku kekerasan anak. Melengkapi studi tersebut, penulis berargumen bahwa kekerasan orangtua mempengaruhi perilaku kekerasan anak karena anak meniru perilaku yang dilakukan oleh orangtuanya, dimana anak laki-laki cenderung melakukan kekerasan fisik dan anak perempuan cenderung melakukan kekerasan verbal. Artikel ini juga ingin mengetahui bagaimana perilaku kekerasan dari orangtua berpengaruh terhadap perilaku kekerasan anak perempuan dan laki-laki. Artikel ini menggunakan metode pendekatan kuantitatif dengan teknik pengumpulan data simple random sampling. Total sampel dari sebanyak 105 responden anak kelas 1 SMPN 246 di Jakarta. Hasil dari penulisan artikel ini membuktikan bahwa anak yang pernah menerima kekerasan verbal dari orangtuanya cenderung melakukan perilaku kekerasan verbal kepada temannya, baik laki-laki maupun perempuan. Namun, hal tersebut tidak berlaku untuk kekerasan fisik.

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This article aims to discuss the influence of parental violence to child violence behavior. Within a few years, cases of violence by children increased. Previous studies have suggested that violence shows, peers, and parents are the factors that determine child violence behavior. Complementing the studies, this article argue that parental violence affects the behavior of child violence because children imitate behavior of their parents, where bpys tend to resort to physical violence and girls tend to resort to verbal violence. This article also want to know whether the socialization of violent behavior from parents are took effect on violent behavior of girls and boys. This article uses a quantitative method with simple random sampling data collection thecnique. The number of samples are 105 respondents from grade 1 SMPN 246 in Jakarta. The result proves that children who have received verbal violence from their parents tend to engage in verbal violence behavior toward their friends, both girl and boy. However, this does not apply to physical violence."

"Sebagian besar informasi yang beredar di internet merupakan konten video. Informasi video ini perlu dianalisis karena tidak semuanya yang beredar adalah video dengan konten yang baik. Banyak video dengan konten yang buruk beredar luas di internet dan dapat diakses oleh siapapun yang mengakses internet. Pada penelitian ini, dibuat sistem klasifikasi video pada Youtube dengan metode Symbolic Distance dan Focal Point menggunakan model pemrograman MapReduce pada Hadoop. Sistem klasifikasi ini mengidentifikasi tag yang tersemat pada setiap video di Youtube kemudian dibandingkan dengan matriks co-occurrence untuk mencari nilai symbolic distance pada sebuah video. Penggunaan metode Focal Point pada sistem klasifikasi bertujuan untuk meningkatkan akurasi dan focus untuk klasifikasi video. Dalam penelitian ini diukur juga kecepatan pemrosesan sistem klasifikasi dengan menggunakan Hadoop serta dicari faktor-faktor yang dapat mempengaruhi kecepatan pemrosesan. Untuk itu dilakukan 3 skenario pengujian berdasarkan ukuran InputSplit yang digunakan, jumlah node, serta konfigurasi pada YARN masing-masing dengan 3 ukuran file (500 MB, 1 GB, 1,5 GB) dengan masing-masing jumlah tag sebesar 58718, 119697, dan 160395 tag. Pada file berukuran 500 MB, 1 GB, 1,5 GB, penambahan jumlah node dapat mempercepat kecepatan rata-rata pemrosesan sebesar 0,2 detik, 5 detik, dan 16,3 detik. Kemudian dengan melakukan konfigurasi pada YARN, kecepatan pemrosesan dapat dipercepat hingga 47 detik, 277,1 detik, dan 354,3 detik pada file berukuran 500 MB, 1 GB, 1,5 GB. Dari pengujian juga diketahui semakin kecil InputSplit maka semakin tinggi kecepatan pemrosesan MapReduce. Namun jika mapper tidak dapat menangani jumlah split yang ada, maka kecepatan pemrosesan data akan menjadi lebih lambat dari sebelumnya.

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Most information that widely spread on the internet is video. This video information needs to be analyze because not all of the information have a good content. There are many video with bad content widely spread on the internet and anyone can access that video easily. In this research, Youtube Video Classification System with Symbolic Distance and Focal Point Method is made using a MapReduce from Hadoop framework. This system identifying the tag that assign in every Youtube video and then compare the tag with co-occurrence matrix to find the symbolic distance value for a single video. Focal Point in this system is useful to improve accuracy and focus of video classification.

This research will measure the processing speed of this classification system and then search the factor that can affect processing speed. For that, three skenarios are implemented based on InputSplit size, amount of node, and YARN configuration with three file size (500 MB, 1 GB, 1,5 GB) with the number of each tag are 58718, 119697, and 160395 tag. For file with 500 MB, 1 GB, and 1,5 GB size, increasing the amount of node from two to three can speed up the process for 0,2 second, 5 second, and 16,3 second. Optimize the YARN configuration can speed up the process for 47 second, 277, 1 second, and 354,3 second for file with size of 500 MB, 1 GB, and 1,5 GB. This Reasearch also discover that if the size of InputSplit is small, then the speed of data processing is faster. But if the mapper can?t handle the amount of the split, it can make the processing speed slower than before."