Penelitian ini bertujuan untuk merancang dan membuat sebuah alat monitoring arus dan tegangan dari panel surya, serta intensitas cahaya matahari yang mempengaruhi keluaran dari panel surya yang berbasis IoT dan bersifat realtime. Data hasil monitoring disimpan dan ditampilkan pada Thingspeak. Penelitian ini menggunakan metode Penelitian dan Pengembangan (Research and Development) model Borg and Gall. Hasil dari penelitian ini adalah nilai pembacaan sensor tegangan mampu mengukur nilai tegangan panel surya dengan nilai galat sebesar 1,12%, sensor arus mampu mengukur arus dengan galat sebesar 1,64%, dan sensor intensitas cahaya BH1750 dalam mengukur intensitas cahaya matahari memiliki persentase galat sebesar 1,24%, kemudian data alat monitoring dikirim dan ditampilkan pada ThingSpeak. Selisih waktu dari pembacaan alat dan data yang diterima oleh website berkisar selam 4-6 detik.

Robot adalah kumpulan perangkat elektronika terintegrasi yang dapat bergerak dengan kontrol manusia atau otomatis menggunakan program yang telah ditanamkan terlebih dahulu. Salah satu jenis robot yang banyak dikembangkan yaitu robot sepak bola beroda yang dirancang memiliki kemampuan mendeteksi, menggiring, dan menendang bola ke gawang lawan. Kemampuan penting yang harus dimiliki yaitu kemampuan robot mendeteksi posisi bola. Permasalahan yang dihadapi umumnya yaitu kamera hanya memiliki field of view (FOV) 73.98° pada jarak 1,5 meter sehingga ketika bola berada pada posisi titik buta robot, robot harus berputar untuk mendeteksi posisi bola. Untuk menghindari kehilangan citra bola, robot harus dibekali kemampuan penglihatan seluruh sisinya yaitu menggunakan omnidirectional camera, akan tetapi membutuhkan penyesuaian algoritma pada robot serta harganya relatif mahal sehingga tidak efektif. Sistem pendeteksi objek memanfaatkan omni vision camera yang menggunakan hyperbolic mirror yang diusulkan dapat mendeteksi objek di sekitar keliling robot. Omni vision camera mendeteksi objek secara realtime dengan metode color based filtering menggunakan algoritma Color Connected Components (CCC). Sistem pendeteksi objek menggunakan omni vision camera mampu mendeteksi objek secara 360° sudut visual robot ketika intensitas cahaya berada antara 60 hingga 210 lux. Kemampuan waktu pendeteksian lebih efisien dengan waktu rata-rata 6,33 detik lebih cepat dibandingkan dengan single camera vision yaitu 7,65 detik serta dapat mendeteksi objek bola dengan jarak terdekat 10 cm dan jarak terjauh 160 cm.  Robot is integrated electronic devices that can move with human control or autonomous using a program that has been implanted in advance. One type of robot that has been widely developed is a wheeled soccer robot which is designed to have the ability to detect, dribble, and kick the ball into the opponent's goal. An important ability that must be possessed is the robot's ability to detect the position of the ball. The common problem is that the camera only has a field of view (FOV) of 73.98° at a distance of 1.5 meters, so that when the ball is in the robot's blind spot position, the robot must rotate to detect the ball's position. To avoid losing the object image, the robot must be equipped with the ability to see all sides, using an omnidirectional camera, but requires algorithm adjustments on the robot and the price is relatively expensive so it is not effective. An object detection system utilize omni vision camera that using hyperbolic mirror is proposed which can detect objects around the robot's circumference. Omni vision camera detects objects in real time with the color based filtering method using the Color Connected Components (CCC) algorithm. The object detection system using an omni vision camera is capable of detecting objects 360° from the robot's visual angle when the light intensity is between 60 to 210 lux. The ability of detection time is more efficient with an average time of 6.33 seconds faster than single camera vision, which is 7.65 seconds and can detect image of objects with the closest distance of 10 cm and the farthest distance of 160 cm.

This research aims to design a measuring device for the speed and strength of kicks and punches in martial arts so that the quality of kicks and punches can be measured objectively. The types of taekwondo kicks that were tested were ap chagi and yeop chagi as well as yeop jireugi and momtong jireugi punches. This research was conducted in several steps, namely: literature study, preparing tools and materials, designing and modeling, making tools and testing and analyzing data. The way this tool works, the respondent kicks or punches the foot boxing pad that has the FSR sensor installed. At the same time the time is started by pressing the start button. After making a kick or punch, the time will be stopped by pressing the stop button. The pressure and time will be read by the NodeMCU ESP32, the data will be sent via bluetooth to the Blynk application and displayed with the TM1637. The results of the research "designing a measuring device for measuring the strength and speed of kicks and punches in martial arts with a force sensing resistor (fsr) sensor and NodeMCU ESP32" are: The average kick speed of ap chagi and yeop chagi is 0.10625 m/s and 0.0786 m/s. Meanwhile, the average speed of yeop jireugi and momotong jireugi are 0.1667 m/s and 0.0917 m/s, respectively. The average athlete has a kick power of ap chagi, yeop chagi, yeop jireugi and momtong jireugi is medium.

Hybrid Electric Vehicle (HEV) is vehicle with least two energy sources, such as Internal Combustion Engine (ICE) and Brushless DC Motor (BLDCM). BLDCM provide additional torque, to purpose of HEV can reach the set point speed according to the reference model. Commutation of BLDCM still complicated, because between the rotational speed of the motor and the speed of the rotary field on the stator should be kept synchronized. Self Commutation used to maintain synchronization between rotation of the rotor and rotary field velocity on BLDCM stator. In addition, this research also applies torque control strategy by using fuzzy-PI controller. Vehicle performance still follows the reference curve with steady state error of 0.1506 km/h and RMSE relative response speed HEV <2%.

Stripping the betel nut using a machine is considered to be more effective than manually, based on this I designed an automatic betel nut peeler control device using Arduino Mega as a mechanical and electrical system controller. In the design of this tool using a hollow iron frame material with 40X40 mm, with a length of 130 cm and a width of 30 cm. Furthermore, this tool is planned to be able to work with a capacity of 20 Kg/hour. After all components are completed, it will proceed to the tool development process to be able to perform performance tests. After testing, stripping using the manual method takes a relatively longer time, which is 11 minutes. While using the machine only takes 6 minutes. In this test, 1 kg or 74 young betel nut, yellow areca nut, dry areca nut, and wet old areca nut were used.