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Controlling room temperature and humidity in egg incubator systems is a process that is widely used in the farm. A good temperature and humidity for standard egg hatching is between 35℃ – 40℃, with humidity in the machine ranging from 50%-60%. The main problems of our research is to find the robustness of the fuzzy logic controller, using the proper parameter. Because while the particular parameter is applicable for one case, but after using several times, the controller lost its robustness. Therefore, this study aims to create a system to control the temperature and humidity of the egg incubator with fuzzy control using the Sugeno methods. In order to get the input and output values, namely by connecting the DHT22 sensor to measure temperature and humidity to be processed into the microcontroller, the value obtained from the sensor will then be processed. The use of fuzzy control is used to make several stages, namely fuzzification, rule, and defuzzification which after processing will be used as output weights for the actuators used. In order to get the robust parameter, test was carried out 5 times with a test time of 18 minutes to get a stable value from the tool. By applying this, it can be concluded whether the system is reliable during different situation. The result shows that the average time for the system to get a stable humidity is 302 second. On the other hand, the average time for the system to get stable temperature is 342 second. The Mean Squared error for temperature is 1,715, while the Mean Squared Error for Humidity is 5,294. It can be concluded that the system controlled by fuzzy controller is robust, has a fast response and reliable.

ABSTRAKPLC digunakan pada pengontrolan suhu pada pasteurisasi susu. Di sisi lain, MATLAB memiliki variasi pengontrol yang lebih luas dan dapat terhubung ke sistem Pasteurisasi Susu menggunakan OPC. Namun, penggunaan OPC dikhawatirkan akan menurunkan kinerja sistem karena waktu tunda dan waktu sampling. Penelitian ini bertujuan untuk membandingkan kinerja pengontrolan PID pada sistem pasteurisasi susu dari MATLAB dan PLC dengan memperhatikan pengaruh antarmuka OPC. Pada penelitian ini, kinerja peralihan sistem diukur dengan pengontrol yang berbeda, menggunakan metode penalaan yang berbeda dan berasal dari PLC dan MATLAB. Hasil penelitian menunjukkan bahwa penggunaan OPC tidak memberikan efek negatif pada kinerja sistem. Nilai rata-rata MSE setelah keadaan stabil pada pengontrol PI adalah 70,66 sedangkan pada pengontrol PID 1,16. Nilai MSE Pengontrol PID dengan Penalaan Cohen Coon adalah 0,356. Dapat disimpulkan bahwa pemilihan piranti pengontrol lebih memiliki efek signifikan terhadap kinerja dibandingkan dengan penggunaan OPC.Kata kunci: Pasteurisasi, Cohen Coon, PLC, MATLAB, OPC ABSTRACTPLC can be used to control temperature in milk pasteurization process. MATLAB has more usable Controllers that can be used in milk pasteurization process through OPC .The concern is that the use of OPC will reduce system performance due to delay and sampling time. This study aims to compare the performance of PID control in the milk pasteurization system usingMATLAB and PLC as controllersregarding the effect of using OPC interfaces. In this research, the transient response performance of the system was measured using different type of controllers, twhich use different tuning methods based on PLC and MATLAB. The results showed that the use of OPC did not have a negative effect on system performance. The MSE average in a steady condition for PI controller is 70,66, and for PID controller is 1,16. MSE result using Cohen Coon controller is 0,356. So it can be concluded that the choice of the control device has a more significant effect on performance than with the use of OPC.Keywords: Pasteurization, Cohen Coon, PLC, MATLAB, OPC

No abstract

Today's technology shows very significant developments in automation systems. Filling water in a tank is a process that is widely used in the oil industry. The process of filling and draining water in a closed water tank will raise difficulty for the operator to control the water level in the tank. Therefore, we will create a system to control the water level using a fuzzy controller. To get the value of fuzzification from the difference in the value of the set point and defuzzification connected to the motor pump. Fuzzy design system was obtained by making methods, fuzzification, defuzzification, and rules. The use of ultrasonic sensors during the experiment caused a spike in value because the ultrasonic sensor had not detected water but had no significant effect on the response to altitude levels. When the valve is opened, the controller has an effect on the resulting response. The more the valve is opened, the worse the response will be but the system remains stable. The fuzzy output will be connected to the PLC which will be connected to the Kepserver and tank to regulate the flow rate to the tank. Matlab is connected with PLC devices using Kepserver. The best performance is found in the 2× reduction valve opening response test with a fuzzification range of 8000-24000 while the performance value of time rise is 66.458 s, settling time is 110 s, steady state error is -2%, and overshoot is 9%.