Optimasi Pengendalian Suhu dan Kelembapan Ruangan di Kota Yogyakarta Menggunakan Metode Fuzzy

Sunardi, Sunardi; Yudhana, Anton; Furizal, Furizal

doi:10.30865/jurikom.v9i6.5060

Cited by 1 publication

(1 citation statement)

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“…Fuzzification is the process of converting crisp (non-fuzzy) variables into fuzzy variables by using membership functions (20,44,45). Fuzzification allows mathematical representation of uncertainties in the system using fuzzy sets (44)(45)(46)(47)(48). The crisp variables that will be used as system inputs are converted into fuzzy sets using the membership function.…”

Section: Fuzzificationmentioning

confidence: 99%

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

Furizal,

Sunardi,

Yudhana

et al. 2024

IJE

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Energy consumption is a crucial aspect in the effort to optimize the utilization of resources and reduce energy wastage. Focusing on energy efficiency can result in operational cost savings, a reduction in greenhouse gas emissions, and support for environmental sustainability for future generations. Therefore, it is important to consider energy efficiency in daily life, especially in the use of electricity for electronic devices. This research aims to compare the energy efficiency of two different approaches to Air Conditioner (AC) usage: the manual method and the fuzzy logic method. The manual method involves eight tests with direct power measurements over a 30-minute period at various AC temperature settings, namely 18°C, 20°C, 23°C, 24°C, 25°C, 26°C, 27°C, and 30°C. On the other hand, the fuzzy logic method involves six tests allowing for dynamic temperature adjustments based on room conditions. The research findings indicate that the fuzzy logic method achieves lower average power consumption, except at 30°C, where the manual method is slightly more efficient (a difference of 140,745 watts). This difference is primarily attributed to the "cooling and fan" mode used at lower temperatures in the manual method, resulting in higher power consumption. Furthermore, this research reveals the potential of the fuzzy logic in optimizing AC power usage based on real-time conditions, achieving approximately a 41.96% energy savings. The primary contribution of this study is to provide practical insights into how the fuzzy logic method can significantly reduce AC energy consumption, support energy efficiency efforts, and contribute to environmental sustainability.

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