scholarly journals A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

2018 ◽  
Vol 94 (2) ◽  
pp. 497-517 ◽  
Author(s):  
Omid Ghorbanzadeh ◽  
Hashem Rostamzadeh ◽  
Thomas Blaschke ◽  
Khalil Gholaminia ◽  
Jagannath Aryal
Keyword(s):  
Data Mining ◽  
Land Subsidence ◽  
Fuzzy Inference System ◽  
Cross Validation ◽  
Fuzzy Inference ◽  
Data Mining Technique ◽  
Inference System ◽  
Mining Technique ◽  
Neuro Fuzzy ◽  
Fold Cross Validation

Virtual Multiphase Flowmetering Using Adaptive Neuro-Fuzzy Inference System (ANFIS): A Case Study of Hai Thach-Moc Tinh Field, Offshore Vietnam

SPE Journal ◽  
2021 ◽  
pp. 1-15
Author(s):  
Tran Ngoc Trung ◽  
Trieu Hung Truong ◽  
Tran Vu Tung ◽  
Ngo Huu Hai ◽  
Dao Quang Khoa ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Cross Validation ◽  
Fuzzy Inference ◽  
Gas Condensate ◽  
Series Data ◽  
Process Conditions ◽  
Inference System ◽  
Data Smoothing ◽  
Neuro Fuzzy ◽  
Fold Cross Validation

Summary For any oil and gas company, well-testing and performance-monitoring programs are expensive because of the cost of equipment and personnel. In addition, it may not be possible to obtain all of the necessary data for a reservoir for a period of time because of production demand constraints or changes in surface process conditions. To overcome these challenges, there are many studies on the implementation and value of virtual flowmetering (VFM) for real-time well performance prediction without any need for a comprehensive well-testingprogram. This paper presents the VFM model using an adaptive neuro-fuzzy inference system (ANFIS) at Hai Thach-Moc Tinh (HT-MT) gas-condensate field, offshore Vietnam. The ANFIS prediction model can tune all its membership functions (MFs) and consequent parameters to formulate the given inputs to the desired output with minimum error. In addition, ANFIS is a successful technique used to process large amounts of complex time series data and multiple nonlinear inputs-outputs (Salleh et al. 2017), thereby enhancing predictability. The authors have built ANFIS models combined with large data sets, data smoothing, and k-fold cross-validation methods based on the actual historical surface parameters such as choke valve opening, surface pressure, temperature, the inlet pressure of the gas processing system, etc. The prediction results indicate that the local regression “loess” data smoothing method reduces the processing time and gives both clustering algorithms the best results among the different data preprocessing techniques [highest value of R and lowest value of mean squared error (MSE), error mean, and error standard deviation]. The k-fold cross-validation technique demonstrates the capability to avoid the overfitting phenomenon and enhance prediction accuracy for the ANFIS subtractive clustering model. The fuzzy C-mean (FCM) model in the present study can predict the gas condensate production with the smallest root MSE (RMSE) of 0.0645 and 0.0733; the highest coefficient of determination (R2) of 0.9482 and 0.9337; and the highest variance account of 0.9482 and 0.9334 for training and testing data, respectively. Applied at the HT-MT field, the model allows the rate estimation of the gas and condensate production and facilitates the virtual flowmeter workflow using the ANFIS model.

scholarly journals PEMETAAN BENTUK DAN PENCEGAHAN PENIPUAN E-COMMERCE

2020 ◽  
pp. 851
Author(s):  
Ni Komang Arista Dewi ◽  
Luh Putu Mahyuni
Keyword(s):  
Data Mining ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Digital Publishing ◽  
Privacy Concern ◽  
Inference System ◽  
Neuro Fuzzy

Seiring berkembangnya transaksi jual beli, penipuan elektronik juga turut meningkat sehingga mengakibatkan banyak konsumen yang telah mengalami kerugian akibat penipuan yang terjadi. Tujuan dari penelitian ini adalah untuk mengetahui jenis penipuan yang dapat terjadi dalam perdagangan elektronik dan pencegahan yang dapat dilakukan. Dalam penelitian ini disajikan review dengan metode pendekatan interpretif atas artikel terkait, dengan proses pemetaan pada artikel yang dikumpulkan melalui situs Google Cendekia, Elsevier, Springer, Taylor & Francis, dan MDPI (Multidisciplinary Digital Publishing Institute). Dari sumber tersebut, 105 artikel berhasil dikumpulkan, setelah proses seleksi artikel berdasarkan 10 tahun terahir dan kesesuaian pembahasan akhirnya diperoleh 55 artikel. Hasil penelitian ini adalah ditemukan berbagai jenis penipuan pada keempat kategori e-coomerce serta penipuan pada sistem pebayaran dan penipuan pada e-commerce yang menyangkut pelanggan. Metode modern pendeteksi penipuan juga disajikan dalam penelitian ini, seperti data mining, jaringan bayesan, algoritma, mesin pendukung vector, pemrograman genetik, pohon pengambilan keputusan, Adaptive Neuro-Fuzzy Inference System, Situs Web Bantuan Perlindungan (PAW), dan Model Privacy Antecedent-Privacy Concern-Outcomes (APCO). Dengan penjabaran pada hasil penelitian ini, konsumen diharapkan untuk lebih berhati-hati saat melakukan transaksi di situs e-commerce agar terhindar dari berbagai tindak penipuan.

scholarly journals Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang)

2014 ◽  
Vol 8 (2) ◽  
pp. 121
Author(s):  
Robynson Amseke ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Cross Validation ◽  
Subjective Evaluation ◽  
Computer Application ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Testing Techniques ◽  
Credit Data ◽  
Fold Cross Validation

AbstrakSalah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya timdalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif.Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 (56,35% dan 55,03%). Kata kunci—classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit  AbstractOne of the causes of non-performing loans come from the internal, that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation(CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques. Preprocessed initial-credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm(57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software(56,35% and55,03%, respectively). Keywords—classification based on association, CBA, data mining, classification, credit risk 

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