Design of Banana Ripeness Classification Device Based on Alcohol Level and Color Using a Hybrid Adaptive Neuro-Fuzzy Inference System Method

2021 ◽  
pp. 107-117
Author(s):  
M. R. E. Ariono ◽  
F. Budiman ◽  
D. K. Silalahi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method

scholarly journals Identifikasi Citra Digital Jenis Beras Menggunakan Metode Anfis dan Sobel

2021 ◽  
Vol 7 (2) ◽  
pp. 123-128
Author(s):  
Gansar Suwanto ◽  
Riza Ibnu Adam ◽  
Garno
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Texture Features ◽  
Image Method ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method ◽  
Sobel Edge Detection ◽  
The Many ◽  
Shape Characteristics

Rice is one of the leading national food products and superior agricultural products in Indonesia. The many types of rice in Indonesia make it increasingly difficult to distinguish rice by just relying on the eye. Because each type of rice has relatively different shape and texture characteristics. Therefore, digital images can be used as a first step in identifying types of rice. This study aims to identify the types of rice using image processing. Taking the value of the shape characteristics using the morphology method and compared with the sobel method. While taking the value of the texture features using the grayscale image method. Then, the value of the shape and texture do the grouping according to the type of rice. The data used in this study were 140 images. 100 of the 140 images were conducted training using the ANFIS (Adaptive Neuro Fuzzy Inference System) method by utilizing the value of the shape and texture of the image. The test was carried out 5 times using 140 images. The test results using the ANFIS (Adaptive Neuro Fuzzy Inference System) method by 85.2%. Meanwhile, sobel edge detection can affect accuracy by 3%.

scholarly journals Prediction of maximum in-cylinder pressure by adaptive neuro-fuzzy inference system method

2020 ◽  
Vol 788 ◽  
pp. 012066
Author(s):  
Farzad Jaliliantabar ◽  
Gholamhassan Najafi ◽  
Rizalman Mamat ◽  
Barat Ghobadian
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cylinder Pressure ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method

scholarly journals Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Mahdi Sedighkia ◽  
Asghar Abdoli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Physical Habitat ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method ◽  
Habitat Simulation ◽  
Fish Habitats

AbstractThis study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies.

Parasympathetic Nervous Signal Damping Using the Adaptive Neuro-Fuzzy Inference System Method to Control Overactive Bladder

2015 ◽  
Vol 40 (4) ◽  
pp. 197-201
Author(s):  
Mohanad A. Deaf ◽  
Mohamed A. A. Eldosoky ◽  
Ahmed M. El-Garhy ◽  
Hesham W. Gomma ◽  
Ahmed S. El-Azab
Keyword(s):  
Overactive Bladder ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method

scholarly journals Perancangan Sistem Deteksi Isyarat BISINDO Dengan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS)

KOMTEKINFO ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 50-61
Author(s):  
Nadia Intan Pratiwi ◽  
Ida Widaningrum ◽  
Dyah Mustikasari
Keyword(s):  
Sign Language ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Color Segmentation ◽  
Sign Languages ◽  
Inference System ◽  
Indonesian Government ◽  
Neuro Fuzzy ◽  
System Method ◽  
Background Image

Deafness is a condition where an individual's hearing cannot function normally. So, sign language was created which was used as a solution to the problem. In Indonesia, the sign languages that are known are SIBI (Indonesian Sign Language System) and BISINDO (Indonesian Sign Language). Although SIBI has been recognized by the Indonesian government, in its use it is less desirable. This research was conducted to identify empty hand signals. Where it will help the user naturally without additional assistance. Experiments carried out using a dataset that was demonstrated by 1 display. In the process, the characteristics of the hand are taken using the Histogram Oriented Gradient (HOG) method. Whereas to separate it from the background image, color segmentation is used. The results of the process are then taken to classify. The classification process uses the Adaptive Neuro-Fuzzy Inference System method. The results of the tests carried out resulted in an accuracy of 78.31%. The problem is done.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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