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Majority of agricultural activity in Indonesia still relies on rainwater for irrigation. In the dry season, there are many non-productive agricultural lands. It leads to decrease in farmers’ income. To solve this problem, Aquaponic Planting System was offered, but the system is complicated. It needs adequate oxygen and proper pH to make nitrification bacteria alive to convert NH3 to NO3- and NO2. Therefore, a system which controls oxygen and pH is needed. For pH calibration, a curve fitting method was used to get an accurate measurement. For oxygen calibration, saturated dissolved oxygen water was used. It was obtained using a Potentiometric method. Moreover, Simple Moving Average was used to get a stable reading.

Laboratory monitoring, which is usually carried out by laboratory personnel, is now carried out in real time and in a modern way. The purpose of this laboratory monitoring is to determine the room temperature using a K-type thermocouple which is standardized with a standard thermometer. The results of these objectives are to know the validation value and to know the heuristic value of the K-type thermocouple from the sensor in the K-type thermocouple called the LM35 sensor. These results are obtained from a system that has been created named room temperature monitoring in real time using a K-type thermocouple. This system was established from the use of the damaged OHP (Over Head Projector) as a mini laboratory, then around the OHP were placed four pieces of glass on all four sides that had been coated with aluminum, and then placed the LM35 sensor which was placed in the corners of the OHP side and outside the glass coated with aluminum. Then the wire from the K-type thermocouple is inserted in the middle position in the OHP. The LM35 sensor that is inserted into the OHP is connected to the Raspberry pi Type B which serves as the brain of the system that is made. This system displays the ADC value and Temperature value when the Halogen lamp is turned on in an artificial laboratory (OHP). Monitoring measurements are carried out with 1 measurement on a standard thermometer and four measurements on a test instrument (Type-K Thermocouple). The results of the sensor characterization are test accuracy values ​​of 98.21%. Precision results are 99.81%. Linearity results are 99.91% and the sensor sensitivity result is 0.27.

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Smart irrigation as one of implementations of the Internet of Things (IoT) in agriculture aims to control and monitor water supply in accordance to crop needs. The control systems are designed using the Arduino Nano platform and the soil moisture sensor YL 69. The accuracy of the sensor is very influential on the performance of the control system, so that sensor calibration is done before it is applied to smart irrigation. The calibration method used in this research is the Gravimetric Water Content method. The media used in calibration is a mixture of soil and sand as Smart Irrigation as one of the implementations of the Internet of Things (IoT) in agriculture aims to control and monitor water supply in accordance to crop needs. The control system is designed using the Arduino Nano platform and the soil moisture sensor YL 69. The accuracy of the sensor is very influential on the performance of the control system, so that sensor calibration is done before it is applied to smart irrigation. The calibration method used in this research is the Gravimetric Water Content method. The media used in calibration is a mixture of soil and sand as much as 5% of the soil. Water weighed 4.71% from soil weight added to increase soil moisture level in calibration process. Based on the calibration results obtained from the sensor reading 201 shows 18.31% of soil moisture in dry soil. With nine times of water addition to soil obtained soil saturation point to water with sensor reading 633 which shows result 61.91% of soil moisture. The experimental results show the polynomial curve of order 3