Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.

Electricity is one of the most important human needs. In the presence of electricity it can facilitate human work. But it should be noted that too large and uncontrolled electricity use will be wasteful and get high costs. The problem is that electricity is not monitored accurately, easily and efficiently. This study aims to design an electric current monitoring device with an IoT system. IoT is a concept with the ability to transfer data by network, no need humans to humans or humans to PCs. In this concept, the SCT 013-000 electric current sensor is connected to the load, it will be show electric current value in the LCD, if the electric current which is determined exceeds the capacity, Wemos D1 including Wifi ESP 8266 will be sending a notification to the telegram. The system has been implemented with ironing load for 3.29%, the dispenser load is 0.20% and Magicom's get load for 1.07%. The delay time also has been implemented in the relay for 1.50 second when relay is on and 0.78 second when relay is off. When the notification send to the telegram also have a delay for 6.2 second. So, monitoring of electrical system using internet of things with smart current electric sensors has been done.

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IOT adalah sebuah paradigma baru yang bertujuan menjembatani kesenjangan antara dunia fisik dan perwakilannya dalam dunia digital. Terbatasnya tata ruang mengharuskan untuk memunculkan alat yang bisa menjangkau sudut-sudut ruangan yang tidak dapat dijangkau oleh manusia. Dari kebutuhan tersebut, maka dibuatlah robot pemadam kebakaran berbasis internet of things. Dengan penggunaan module microcontroller wemos, diharapkan dapat menggantikan sistem kabel LAN sebagai sarana untuk berkomunikasi data dari robot dengan aplikasi Blynk sehingga robot ini dapat menjangkau sudut-sudut ruangan dimana apabila menggunakan kabel sangat sulit untuk dijangkau. Robot pemadam kebakaran ini dirancang menggunakan microcontroller wemos. Microcontroller tersebut akan menjadi penghubung antara smartphone dengan rangkaian robot. Sistem penggerak robot menggunakan 2unit motor DC dengan transistor sebagai motor driver. Penyemprotan air menggunakan 1unit mini pump motor DC. Pengukuran suhu dan kelembapan dideteksi oleh sensor DHT11. Daya untuk menggerakan robot ini menggunakan 2 unit baterai 4.2 volt. Aplikasi yang digunakan adalah Blynk yang merupakan open source IoT server. Durasi pembacaan suhu dan kelembapan dilakukan selama 20 detik, dimana suhu dalam ruang adalah 29oC dengan kelembapan 70%.

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The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.

<span lang="EN">The automatic and manual IoT-based rail door (internet of things) is a door bar designed to be able to close and open automatically and manually. The automatic system works based on sensors that detect the presence of trains and system manual works based on the open and Close button on the smartphone. The components to be used are ATmega328 microcontrollers, infrared sensors, power supply, CCTV and android applications. Infrared sensor will detect the presence of the train and the gate will close automatically. Then the doorway will open when the train has crossed the automatic door bar. By the manual way, rail door control can be open and closed with Android smartphones in real-time with graphical display provided by CCTV. The whole process is connected to a WEB server where the program is embedded. Either it is automatic or manual control. From the tests that have been done, that the response data from the server is very fast, which is less than 1 second civil. For infrared sensor 1 there is an average delay of 0,687/sec and Infrared Sensor 2 is 3,449/sec. In realtime CCTV There is an average delay of 0,857/sec</span><span>.</span>