scholarly journals IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY

2021 ◽  
Vol 4 (2) ◽  
pp. 260-269
Author(s):  
Zulfauzi - ◽  
Budi Santoso ◽  
M. Agus Syamsul Arifin ◽  
Siti Nuraisyah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
City Area ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Rice Price ◽  
The Future ◽  
Anfis Method ◽  
Rice Prices

The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five years. With the accuracy value in rice price predictions based on MSE training, numely 99,9037% and based on the MSE test that is 99,8784%. While the accuracy values of rice price predictions based on MAPE training and testing are 93,2997% and 88,2782%, respectively. For the accuracy value of rice price prediction result based on the MSE and MAPE values respectively namely 99,8935% and 92,9212%. It can be concluded that the ANFIS method is very effectively used for the process of predicting a price or value in the future

Gas-Liquid Phase Recirculation in Bubble Column Reactors: Development of a Hybrid Model Based on Local CFD – Adaptive Neuro-Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 44 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Scale Up ◽  
Flow Simulation ◽  
Continuous Phase ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Different Dimensions

Abstract The Euler–Euler method and soft computing methods are recently utilized for the purpose of bubbly flow simulation and evolution of the dispersed and continuous phase in a two-phase reactor. Joining computational fluid dynamics (CFD) to the adaptive neuro-fuzzy inference system (ANFIS) method can enable the researchers to avoid several runs for heavy numerical methods (multidimensional Euler–Euler) to optimize fluid conditions. This overview can also help the researchers to carefully analyze fluid conditions and categorize their huge number of data in their artificial neural network nodes and avoid a complex non-structure CFD mesh. In addition, it can provide a neural geometry without limitation of an increasing mesh number in the fluid domain. In this study, gas and liquid circulation were considered as one of the main CFD factors in the scale-up of reactors used as an output parameter for prediction tool (ANFIS method) in different dimensions. This study shows that a combination of ANFIS and CFD methods provides the non-discrete domain in various dimensions and makes a smart tool to locally predict multiphase flow. The integration of numerical calculation and smart methods also shows that there is a great agreement between CFD results and ANFIS output depending on different dimensions.

scholarly journals Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

2019 ◽  
Vol 13 (1) ◽  
pp. 293
Author(s):  
Mujiarto Mujiarto ◽  
Asari Djohar ◽  
Mumu Komaro ◽  
Mohamad Afendee Mohamed ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Color Detection ◽  
Anfis Method ◽  
Arduino Microcontroller

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

scholarly journals Prototipe Penilaian Kinerja Tenaga Ahli PT. Inacon Luhur Pertiwi Dengan Pendekatan Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 108-122
Author(s):  
Handa Gustiawan
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Primary Data ◽  
Inference System ◽  
Analysis Process ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Data Reading

Inacon Luhur pertiwi PT. as amanagement consulting firm in carrying outits work on the project PNPM Urban withcontract number HK.02.03 / NMC / IBRD /SATKER-PK / 007/2012 dated 10 May 2012.By carrying out quantitative researchmethods, using primary and secondary dataas samples. Primary data retrieved byconducting an observation as anobservation instrument of expertsperformance assessment. Secondary datawas collected by observing the data,reading, studying and quoting from the bookof literature, as well as the resources thatare closely related to this study. The dataobtained will be used for purposes ofdescriptive data analysis process by usingAdaptive Neuro Fuzzy Inference System(ANFIS). ANFIS method is a method thatuses neural networks to implement fuzzyinference system. Fuzzy inference systemused is the fuzzy inference system modelsTagaki-Sugeno-Kang (TSK) withconsideration of simplicity and easycomputation. The result of this research isthe prototipe of expert performanceevaluation which can be implemented atInacon Luhur Pertiwi PT.

scholarly journals Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

2020 ◽  
Vol 202 ◽  
pp. 14008
Author(s):  
Siska Ayu Widiana ◽  
Suryono Suryono ◽  
Budi Warsito
Keyword(s):  
Food Security ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Inference System ◽  
Plant Seeds ◽  
Neuro Fuzzy ◽  
Anfis Method

Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106.

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