Decision Support System for Determining the Amount of Production Based on Inventory Data and the Amount of Demand using the Tsukamoto Method

Yusfrizal,; Sovina, Mutiara; Harahap, Faisal Amir; Adhar, Deny; Fahrozi, Wirhan; Kurniawan, Helmi

doi:10.1109/icsima50015.2021.9526321

Cited by 1 publication

(1 citation statement)

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“…Broadly speaking, this Tsukamoto model has three stages in producing outputs in the form of crisp values, namely fuzzification, machine inference, and defuzzification [101]. Fuzzification is a process of converting a non-fuzzy (crisp) set into a fuzzy set [94], a non-fuzzy (crisp) input mapped to a fuzzy set form according to the variations of the input speech universe [102].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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Section: Fuzzy Logicmentioning

confidence: 99%