Automation is widely used in various industries to increase the speed, accuracy and effectiveness of production and reduce the risk of hazards. In general, the manufacturing industry has a product packaging and sorting process based on size, weight, shape, height and color. Currently, the sorting process in the industry is still mostly done manually which causes human error problems, so the development of industrial automation that is low cost, easy to maintain, efficient, user friendly and more accurate needs to be done. Programmable Logic Controller (PLC) is one of the most widely used for automation equipment in the industry and can be programmed to control machine operations. In this paper, the Mitsubishi Melsec PLC with ladder diagram programming using GX work2 has been developed to control multi-machine operation by product sorting and packaging based on product color. Multi-machine operation consists of three DC motors used for conveyors, two single solenoids, proximity sensors, inductive sensors and limit switches. The proximity sensors are used to detect the products based on color. The system is equipped with an emergency button and alarm system to facilitate the implementation of troubleshooting. To facilitate communication with operators and user-friendly systems, the human machine interface (HMI) is used as an interface for system monitoring and controlling. HMI uses Pro-face from Schneider which is programmed using GP-Pro EX4.0. The ladder diagram has been implemented in the PLC and tested on hardware to simulate the system. Based on test results, MELSEC PLC can communicate with HMI and control multi-operation machines with product sorting and packaging. The machine can separate and package metal products by colour and the packaging system can be monitored and controlled remotely automatically with HMI.

Coronavirus (Covid-19) first appeared in Wuhan, December 2019, and continues to spread rapidly to other countries. one of the countries infected with the Covid-19 virus is Indonesia. In Indonesia, the spread of this virus is very fast. Therefore, we need a detection system to detect people who are infected with this virus or not. Rapid detection of Covid-19 can contribute to control the spread of this disease. Chest x-ray images are one of the first imaging techniques to play an important role in the diagnosis of Covid-19. This research data uses chest x-ray images dataset in the Covid-19 cases. The data used in this study were 170 images data with 130 data for training data and 40 for testing data. In this study, the Neural Network, Support Vector Machine (SVM), and Convolutional Neural Network (CNN) methods were used, then applied to Stacking which is one of the methods of Ensemble Learning. The results of this study indicate that the best accuracy is obtained from the Stacking model with an accuracy of 95%.

Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.

ABSTRAKPenelitian ini bertujuan menginvestigasi algoritma kendali pada plant level air yang terdistribusi pada dua tangki. Terdapat dua metode yang digunakan yakni kendali PI-D yang ditempatkan pada masing-masing unit pengendali lokal (LCU) dan kendali Fuzzy sebagai unit pengendali utama (MCU) dimana performa sistem dapat di-monitoring pada Human Machine Interface (HMI). Kendali PI-D (tipe B) dipilih untuk mengantisipasi persoalan setpoint kick yang sering muncul ketika digunakan kendali PID konvensional. Hasil penelitian menunjukkan respons kendali PI-D dapat mengatasi setpoint kick ditandai dengan perubahan smooth ketika setpoint berubah. Respons kendali PI-D pada HMI menunjukkan hasil yang baik direpresentasikan melalui error steady state dapat bernilai 0, overshoot 0% dan settling time 0,01 detik. Disamping itu, kontrol Fuzzy yang digunakan pada MCU dapat menghasilkan nilai setpoint (SV) yang tepat untuk masing-masing LCU, sehingga diperoleh respons kendali di atas.Kata kunci: LCU, MCU, PI-D, Level Air, Logika Fuzzy ABSTRACTThis study aims to investigate the control algorithm on the plant water level distributed in two tanks. There are two methods used, namely PI-D control placed on each local control units (LCU) and Fuzzy control as the Main Control Unit (MCU) and synchronization for Human Machine Interface (HMI) and monitoring. The PID control (type-B) was chosen to anticipate the setpoint kick problem that often arises when conventional PID controls are used. The results showed that the PI-D control response was able to overcome the setpoint kick which represented by smooth acting of actuator response . Response of the PI-D control on the HMI shows a good response by a steady state error of 0, an overshoot of 0%, and a settling time of 0.01 seconds. Besides that, Fuzzy logic used in the MCU can produce the right setpoint (SV) value for each LCU, so that the control response above is obtained.Keywords: LCU, MCU, PI-D, Water Level, Fuzzy Logic