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Journal : Journal of System and Computer Engineering

Home-based Waste Monitoring System using Internet of Things with Fuzzy Logic Method Sumarlina, Sumarlina; Arda, Abdul Latief; Wardi, Wardi; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1538

Abstract

The accumulation of waste in certain locations, especially in residential areas, due to the continuous accumulation of waste can cause environmental disturbances such as disease and unpleasant odors. This system is designed to find out whether the trash bin is full or not by applying fuzzy logic, if the status of the trash bin can be cleaned, it will be handled immediately so that the waste does not accumulate and disturb people around. This system can find out when the last time the waste was taken and the location of the trash bin as a prototype which can later be applied to areas with a wider range by cleaning service officers in Mamuju City. This system uses the HC-SR04 sensor to detect the distance of the waste to the sensor and uses the Load Cell sensor to detect the weight of the waste. Several tests were carried out, first by measuring the accuracy of each sensor used, the HC-SR04 sensor accuracy was obtained at 96.68% with an error of 3.32%. While the accuracy of the load cell sensor is 90.68% with an error of 9.32%. The second test calculates the sensor response time and Blynk notification response since the sensor detects waste, the average HC-SR04 sensor detection response is around 0.83 seconds. For the response time of incoming notifications when there is movement in the HC-SR04 sensor area has an average of 2.65 seconds. While the Load Cell sensor response time is only around 0.53 seconds. For Blynk notification response time since the Load Cell sensor detects it has an average of 2.51 seconds. The third test calculates the response time of the two sensors (HC-SR04 and Load Cell) and the Blynk notification response since the two sensors detected the waste, the average response time of the two sensors finished detecting only around 0.91 seconds. For the response time of the trash bin status condition, if there is movement in the area of the two sensors, the condition of the trash bin will change and display the status of Normal, Needs Cleaning and Highly Needs Cleaning with an average response time of 1.18 seconds. The system successfully sends notifications according to the fuzzy rules and expected to speed up the waste handling process
Performance Evaluation of IoT-Based AC Control Using Multi-Modal Fuzzy Sensors A, Amiruddin; Arda, Abdul Latief; Jalil, Abdul; Achmad, Andani; Sahibu, Supriadi; Yuyun, Yuyun
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2649

Abstract

his study addresses the challenge of controlling Air Conditioner (AC) temperature in enclosed spaces in tropical climates, where improper operation often leads to thermal discomfort and excessive energy consumption. The research aims to develop and implement an Internet of Things (IoT)-based system for monitoring and controlling AC temperature by integrating multi-modal sensors and applying a fuzzy logic approach. The proposed system employs a DHT22 sensor to measure temperature and humidity, a thermopile sensor to capture human body temperature, and a PIR sensor to detect occupancy and movement within the room. Sensor data are processed using an ESP32 microcontroller with FreeRTOS-based multitasking and transmitted to the Blynk platform for real-time monitoring. Decision-making is carried out using fuzzy logic based on the temperature difference (ΔT) between body temperature and ambient conditions to automatically regulate AC operation. Experimental results indicate that the system performs reliably and provides adaptive control, achieving a fuzzy logic accuracy of 64.34% under real-world conditions. Furthermore, the automated control mechanism reduces energy consumption by 35.7% compared to conventional manual operation. Overall, the findings confirm that the integration of multi-modal sensing, IoT technology, and fuzzy logic can effectively enhance energy efficiency while maintaining thermal comfort in indoor environments.