Based on data from the Indonesian Child Protection Commission (KPAI) cases of violence against children from 2010 to 2015 continued to increase which from 2010 only 171 cases increased to 2015 as many as 6006 which means that every year cases of violence against children continues to increase at least 1000 cases each year. Changes in heart rate in humans can be known through the flow of blood that flows in blood vessels. As the heart beats, the flow in the blood vessels will move so that is when the condition of the heart rate can be measured. In this study a wireless heart rate condition data collection system will be developed and a heart rate sensing device that has the same ability as a device used in general medical activities. There are three main components, namely server, access point and devices. The three components are summarized in one system, the IoT system. The sensor data obtained is sent via a wireless network using the HTTP Request data sending method. With this method the data transmission is carried out through the HTTP protocol and received and processed by the server with a programming language which in this study used the PHP programming language.

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Tonal coarticulation is universally found to be greater in extent in the carryover direction compared to the anticipatory direction ([1], [2], [3], [4], [5]) leading to assimilatory processes. In general, carryover coarticulation has been understood to be due to intertio-mechanical forces, and, anticipatory effects are seen to be a consequence of parallel activation of articulatory plans ([6]). In this paper, we report on results from a set of Artificial Neural Networks (ANN) trained to predict adjacent tones in disyllabic sequences. Our results confirm the universal pattern of greater carryover effects in Mizo leading to tonal assimilation. In addition, we report on results from single-layered ANN models and Support Vector Machines (SVM) that predict the identity of V2 from V1 (anticipatory) consistently better than V1 from V2 (carryover) in Assamese non-harmonic #…V1CV2…# sequences. The directionality in the performance of the V1 and V2 models, help us conclude that the directionality effect of coarticulation in Assamese non-harmonic sequences is greater in the anticipatory direction, which is the same direction as in the harmonic sequences. We argue that coarticulatory propensity exhibits a great deal of sensitivity to the nature of contrast in a language.

In a soccer game the ability of humanoid robots that one needs to have is to see the ball object in real time. Development of the ability of humanoid robots to see the ball has been developed but the level of accuracy of object recognition and adaptation during matches still needs to be improved. The architecture designed in this study is Convolutional Neural Network or CNN which is designed to have 6 hidden layers with implementation of the robot program using the Tensorflow library. The pictures taken are used in the training process to have 9 types of images based on where the pictures were taken. Each type of image is divided into 2 classes, namely 2000 images for ball object classes and 2000 images for non-ball object classes. The test is done in real time using a white ball on green grass. From the architectural design and white ball detection test results obtained a success rate of 67%, five of the nine models managed to recognize the ball. The model can recognize objects with an image processing speed of a maximum of 13 FPS.Dalam pertandingan sepak bola kemampuan robot humanoid yang perlu dimiliki salah satunya adalah melihat objek bola secara real time. Pengembangan kemampuan robot humanoid untuk melihat bola telah dikembangkan tetapi tingkat akurasi pengenalan objek dan adaptasi saat pertandingan masih perlu ditingkatkan. Arsitektur yang dirancang pada penelitian ini yaitu Convolutional Neural Network atau CNN yang dirancang memiliki 6 hidden layer dengan implementasi pada program robot menggunakan library Tensorflow. Gambar yang diambil digunakan dalam proses training memiliki 9 jenis gambar berdasarkan tempat pengambilan gambar. Tiap jenis gambar terbagi menjadi 2 class yaitu 2000 gambar untuk class objek bola dan 2000 gambar untuk class objek bukan bola. Pengujian dilakukan secara real time dengan menggunakan bola berwarna putih di atas rumput hijau. Dari perancangan arsitektur dan hasil pengujian pendeteksian bola putih didapatkan persentase keberhasilan 67% yaitu lima dari sembilan model berhasil mengenali bola. Model dapat mengenali objek dengan kecepatan pengolahan gambar adalah maksimal 13 FPS.