Fig. 1. Block Diagram for Components Figure 1 depicts the outcomes of the design process, encompassing both a Block Diagram and a Flowchart. These visual representations pertain to the development of an Internet of Things-based automatic koi fish feeder, integrated with the fuzzy logic method. Fig. 2 Process Flowchart Diagram TABLE |. MANUAL MEASUREMENT RESULT TABLE 2. DIGITAL MEASUREMENT RESULT Based on the conducted tests, which encompassed three distinct methods—digital rulers, manual measurements, and ultrasonic sensor measurements—the following outcomes have been derived in Table 1, 2, and 3: Fig. 3 Hardware Schematic for The Proposed Model Within the circuit schematic of the Internet of Things- Based Koi Fish Automatic Feeding Tool, utilizing the Fuzzy Logic Method and NodeMCU, several crucial components play key roles. The schematic encompasses the Node MCU board modules, servo motors, and ultrasonic sensors, each serving a distinct purpose within the system. Primarily, NodeMCU functions as the central processing unit, essentially serving as the brain of this circuit design. With the formulated circuit schematic in Figure 3 as the foundation, the next step involves devising a plan for constructing a prototype of an automatic koi fish feeder based on the Internet of Things and employing the Mamdani fuzzy logic method. The subsequent section outlines the blueprint for the prototype's development, elaborating on key steps and considerations involved in the process. Fig. 4 Mobile Application Interface Plan TABLE 6. ERROR PERCENTAGE FOR BOTH MEASUREMENTS TABLE 4. ERROR RATE FOR DIGITAL MEASUREMENT TABLE 5. ERROR RATE FOR SENSOR MEASUREMENT Fig. 5 Input Membership Diagram The subsequent phase involves the determination of the membership function for the Internet of Things-based Koi fish automatic feeder, which operates on the principles of Fuzzy Logic in Equation 3: TABLE 3. ULTRASONIC SENSOR MEASUREMENT RESULT TABLE 7. FUZZY LOGIC MAMDANI PARAMETERS The subsequent fuzzy rules pertain to the operation of the Internet of Things-based Koi fish automatic feeder, employing the principles of Fuzzy Logic shown in Table 8: exceeding 40%, "ENOUGH" encompassing values falling within the range of 40% to 60%, and "MORE" corresponding to values exceeding 60% in Equation 4. In Figure 7, the fuzzy graph depicted above illustrates a distinctive behavior. When the curve's value in the output indicator remains below 20%, the rate of increase is notably stable. However, once it surpasses the 20% threshold, it exhibits a sharp ascent, eventually plateauing at an output level of 80, with the input stabilizing at 60. Furthermore, the graph below illustrates the functionality of the Internet of Things-based Koi fish automatic feeder, implementing the Fuzzy Logic method. From testing the Feed Volume by using fuzzy, the following results are obtained: During the third test shown in Figure 9, the average feed volume generated equated to 50.2%. Consequently, the recorded input value was 50.2%. The ensuing output registered at 50.7%, indicating that the feed volume remains categorized as medium shown in Figure 10. 55.3%. Consequently, the resulting output stood at 61.3%, indicating that the feed volume remains abundant. In the second test shown in Figure 8, the average feed volume generated amounted to 40.1%. Thus, the recorded input was 40.1%. Consequently, the resulting output stood at 22.7%, signifying that the feed volume remains relatively small.
