The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas
(1) Semarang State University
(2) Semarang State University
(3)
Abstract
The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.
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Muslim, M. A., Prasetiyo, B., & Alamsyah. 2016. Implementation Twofish Algorithm for Data Security in A Communication Network Using Library Chilkat Encryption Activex. Journal of Theoretical and Applied Information Technology, 84(3): 370-375.
Pramesti, A. A., Arifudim, R., & Sugiharti, E. 2016. Expert System for Determination of Type Lenses Glasses using Forward Chaining Method. Scientific Journal of Informatics, 3(2): 177- 188.
Muslim, M. A. & Retno, N. A. 2014. Implementasi Cloud Computing Menggunakan Metode Pengembangan Sistem Agile. Scientific Journal of Informatics, 1(1): 29-38.
Hristidis, H., Chen, S. H., Li, T., Luis, S. & Deng, Y. 2010. Survey of Data Management and Analysis in Disaster Situations. The Journal of Systems and Software. 83(1): 1071-1714.
Refonaa, J., Lakshimi, M., & Vivek, V. 2015. Analysis and Prediction of Natural Disaster Using Spatial Data Mining Technique. Proceeding of International Conference on Circuit, Power and Computing Technologies. 37 (2): 1-6.
Sugiharti, E. & Muslim, M. A. 2016. On-Line Clustering Of Lecturers Performance Of Computer Science Department Of Semarang State University Using K-Means Algorithm. Journal of Theoretical and Applied Information Technology, 83(1): 64-71.
Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A. & Chakraborty, B. 2016. A Review on Application of Data Mining Techniques to Combat Natural Disasters. Ain Shams Engineering Journal. 33(13): 1-14.
Pradhan, B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences. 51(1): 350-365.
Tavakkoli, P., Souran, D.M., Tavakkoli, S., Hatamian, M., Mehrabian, A. and Balas, V.E. 2015. Classification of The Liver Disorders Data Using Multi-Layer Adaptive Neuro-Fuzzy Inference System. Proceeding of In Computing, Communication and Networking Technologies, 1-4
Akcay, O. 2015. Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry. ISPRS International Journal Geo-Informatio, 4(1): 2131-2158.
Ahmad, F., Isa, N.A.M., Hussain, Z., Osman, M.K. and Sulaiman, S.N., 2015. A GA-Based Feature Selection and Parameter Optimization of An ANN in Diagnosing Breast Cancer. Pattern Analysis and Applications, 18(4): 861-870.
Chen, W., Panahi, M. and Pourghasemi, H.R., 2017. Performance Evaluation Of GIS-Based New Ensemble Data Mining Techniques of Adaptive Neuro-Fuzzy Inference System (ANFIS) With Genetic Algorithm (GA), Differential Evolution (DE), And Particle Swarm Optimization (PSO) for Landslide Spatial Modelling. Catena, 157(2017): 310-324.
Wan, S., Lei, T. C., & Chou, T. Y. 2010. A Novel Data Mining Technique of Analysis and Classification for Landslide Problems. Natural Hazards, 52(1): 211-230.
Altaher, A., Almomani, A. & Ramandas, S. 2012. Application of Adaptive Neuro-Fuzzy Inference System for Information Secuirty. Journal of Computer Science, 8(6): 983-986.
Pamuji, A., 2017. Fuzzy Logic Inference System for Determining the Quality Assesment of Student’s Learning ICT. Scientific Journal of Informatics, 4(1): 57-65.
Walia, N., Singh, H. & Sharma, A., 2015. ANFIS: Adaptive Neuro-Fuzzy Inference System-A Survey. International Journal of Computer Applications, 123(13): 32-38.
Kabir, M. M., Islam, M. M. & Murase, K. 2010. A New Wrapper Feature Selection Approach Using Neural Network. Neurocomputing, 73(16-18): 3273-3283.
Muslim, M. A., Nurzahputra, A., & Prasetiyo, B. 2018. Improving Accuracy of C4. 5 Algorithm Using Split Feature Reduction Model and Bagging Ensemble for Credit Card Risk Prediction. Proceeding of International Conference on Information and Communications Technology, 1(2018): 141-145.
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