RIZKI ABILAILA, SISKA (2022) PENGUKURAN ENERGI LABORATORIUM PENDIDIKAN VOKASIONAL TEKNIK ELEKTRO UNIVERSITAS SULTAN AGENG TIRTAYASA MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM. S1 thesis, UNIVERSITAS SULTAN AGENG TIRTAYASA.
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Abstract
Penelitian ini memiliki tujuan untuk mengimplementasikan metode Adaptive Neuro – Fuzzy Inference System (ANFIS) dalam melakukan pengukuran energi di laboratorium Pendidikan Vokasional Teknik Elektro Universitas Sultan Ageng Tirtayasa dan mendapatkan hasil pengukuran pemakaian energi di laboratorium Pendidikan Vokasional Teknik Elektro Universitas Sultan Ageng Tirtayasa. Metode penelitian yang digunakan adalah metode Adaptive Neuro Fuzzy Inference System. Adapun parameter yang digunakan dalam penelitian ini antara lain daya, suhu, dan kelembaban. Tipe keanggotaan yang dibandingkan yaitu (1) trimf (segitiga), (2) trapmf (trapesium), (3) gbellmf (lonceng), dan (4) gaussmf (Gaussian). Masing-masing menggunakan model hybrid dan backpropagation. Dalam perhitungan akan menghasilkan nilai Roat Mean Square Error (RMSE) dan kemudian akan dianalisis guna mendapatkan nilai derajat keanggotaan yang sesuai. Berdasarkan hasil penelitian didapatkan nilai RMSE terbaik menggunakan tipe keanggotaan trapmf hybrid. Nilai RMSE pada training data sebesar 0,13554 dan nilai testing data sebesar 0,27914. Proses pengolahan ANFIS menggunakan nilai epoch sebanyak 500, error tolerance sebesar 0, parameter [3 3 3], dan terdiri dari 9 rules.
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | This researh aims to implement the Adaptive Neuro – Fuzzy Inference System (ANFIS) method to use energy measurement in laboratory of Vocational Electrical Engineering Education Sultan Ageng Tirtayasa University and obtain measure result in laboratory of Vocational Electrical Engineering Education Sultan Ageng Tirtayasa University. The research method used is the Adaptive Neuro – Fuzzy Inference System method. The parameters used in this research are power, temperature, and humidity. The types of membership function that were compared were trimf, trapmf, gbellmf, and gaussmf. Respectively using the hybrid and backpropagation models. In this research, the Root Mean Square Error (RMSE) value will be generated and then analyzed in order to obtain membership function result. Based on the research results, the best RMSE value was obtained using the trapmf hybrid membership type. The RMSE value on the training data is 0,13554 and the testing data value is 0,27914. The ANFIS processing uses an epoch value of 500, error tolerance of 0, parameter [3 3 3], and consist of 9 rules. Keywords: Optimization, Adaptive Neuro Fuzzy Inference System, ANFIS | |||||||||
| Uncontrolled Keywords: | Kata Kunci: Pengukuran, Adaptive Neuro Fuzzy Inference System, ANFIS | |||||||||
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
| Divisions: | 02-Fakultas Keguruan dan Ilmu Pendidikan 02-Fakultas Keguruan dan Ilmu Pendidikan > 83201-Jurusan Pendidikan Vokasional Teknik Elektro |
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| Depositing User: | Perpustakaan Pusat | |||||||||
| Date Deposited: | 20 Jul 2022 13:59 | |||||||||
| Last Modified: | 20 Jul 2022 13:59 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/14604 |
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