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Abstract-Blockchain is a distributed ledger system which provide underlying technology behind Bitcoin. Blockchain paradigm can be extended to provide a generalized framework for implementing decentralized computing resources. Some attempts has been made to visualize Blockchain transaction flow. This research aims to assess those attempts through systematic review.

Motorized vehicles in Indonesia consist of two-wheeled vehicles and four-wheeled vehicles. The number of motorized vehicles is increasing every year. The higher the vehicle volume the higher the level of traffic violations. Every violator will be charged a ticket by the ticketing officer if the vehicle user does not obey the driving rules. The ticketing process in Indonesia is still manually using paper by writing violations committed by violators on a piece of paper. This article is an attempt to make it easier for the public and police in traffic violations. This article is designed for vehicle license plate detection applications and traffic violation websites. The plate identification process begins with taking a plate image through a Raspberry Pi-based camera or webcam. The plate image results using the Raspberry Pi camera are carried out by processing the vehicle plate digital image by segmentation methods and Optical Character Recognition (OCR) using matlab. The vehicle plate character results obtained are used as input to identify traffic violations. The form of traffic violations can be seen on the traffic ticket website. Based on the results of OCR testing proved to be able to recognize the image of the vehicle plate. Raspberry Pi based camera for long distance or wireless communication. The results from the traffic ticket website are used as evidence to process motorists who have violated traffic.

Pengolahan suara atau pengenalan kata berkembang pesat sehingga dapat digunakan untuk berbagai aplikasi seperti menggerakan suatu sistem atau kontrol gerak dan media pembelajaran berbasis multimedia. Implementasi pengenalan suara dan deteksi citra pada penelitian ini menggunakan transducer mikrofon dan teknologi kinect. Penelitian ini bertujuan untuk menghasilkan sistem yang dapat mengidentifikasi dan mengenali suatu objek dengan perintah kata, seperti lingkaran, segitiga, segiempat dan segibanyak. Dalam pengolahan suara dilakukan ekstraksi ciri suara dengan Mel-Frequency Cepstrum Coeffecient (MFCC). Pemodelan kata dilakukan dengan menggunakan pemodelan statistik yaitu Hidden Markov Model (HMM). HMM mampu memberikan mekanisme yang efisien untuk memodelkan secara statistik keragaman dalam ucapan atau kata. Pengambilan data sampel dengan transducer mikrofon secara offline dan online. Pada penelitian ini pencocokan pola kata melalui proses pelatihan dan pengujian kata. Keluaran sistem ini berupa kata yang dikenali berdasarkan probabilitas tertinggi dan menampilkan bentuk benda berdasarkan kata yang dikenali. Prosesnya setelah kata dikenali, sistem akan mentracking citra benda berdasarkan bentuk benda kemudian menampilkan bentuk benda yaitu lingkaran, segitiga, segiempat dan segibanyak. Hasil pengujian dengan tranducer mirofon, untuk sumber terlatih 85%, sumber tidak terlatih 81,5%, dan pengujian dengan Kinect sumber tidak terlatih 84% sehingga sistem pengenalan kata dapat diimplementasikan dengan teknologi Kinect.

No abstract