Pengatur Intensitas Cahaya Ruangan dengan Metode Fuzzy Logic Menggunakan PLC
DOI:
https://doi.org/10.12928/biste.v1i3.1033Keywords:
Intensitas Cahaya, Fuzzy Logic, Kendali Cahaya, PLC, Sensor LDRAbstract
Sistem penerangan ruangan konvensional kurang efisien dalam penggunaan energi karena hanya menggunakan prinsip menyalakan (on) dan mematikan (off) lampu serta tidak menghiraukan pengaruh dan kontribusi dari luar atau pencahayaan matahari. Untuk mengatasi permasalahan tersebut dibuatlah sistem pengendalian cahaya lampu penerangan secara otomatis menggunakan Programmable Logic Controller (PLC) Omron sebagai pengendali. Proses pengendalian intensitas cahaya lampu, pada ruangan memanfaatkan dua sensor cahaya berjenis Light Dependent Resistance (LDR) dengan menggunakan metode fuzzy Sugeno sebagai cara pengambilan keputusan dengan dua himpunan dan tiga variabel disetiap himpunan, sedangkan untuk mengubah kembali ke bentuk bilangan crisp atau defuzzyfikasi menggunakan metode Centroid. Kendali fuzzy logic sangat tepat digunakan untuk pengendalian sistem yang bersifat non-linear dan adaptif. Berdasarkan hasil pengujian, sistem yang dibangun dapat berjalan dengan baik dengan tingkat akurasi pengendalian sebesar 99,38% yang diperoleh dari perbandingan antara pengujian sistem langsung dan pengujian dengan Matlab.
Conventional room lighting systems are less efficient in energy use because they only use the principle of turning on (off) and turning off (off) lights and ignoring the influence and contribution of outside or solar lighting. To overcome these problems, a lighting control system was made automatically using the Omron Programmable Logic Controller (PLC) as a controller. The process of controlling light intensity, in a room utilizing two light sensors, type Light Dependent Resistance (LDR) using the Sugeno fuzzy method as a way of making decisions with two sets and three variables in each set, while to change back to the form of crisp numbers or defuzzification using the Centroid method. Fuzzy logic control is very appropriate to be used for controlling systems that are non-linear and adaptive. Based on the test results, the system built can run well with a level of accuracy of control of 99.38% obtained from the comparison between direct system testing and testing with Matlab.References
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