scholarly journals Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

2021 ◽  
Author(s):  
Andri Irfan Rifai
Keyword(s):  
Neural Network ◽  
Economic Growth ◽  
Data Mining ◽  
Support Vector Machine ◽  
Predictive Modeling ◽  
Disaster Risk ◽  
Support Vector ◽  
Community Satisfaction ◽  
Reconstruction Project ◽  
Artificial Neural Network Ann

Natural disasters can occur anytime and anywhere, especially in areas with high disaster risk. The earthquake that followed the tsunami and liquefaction in Palu, Indonesia, at the end of 2018 had caused tremendous damage. In recent years, rehabilitation and reconstruction projects have been implemented to restore the situation and accelerate economic growth. A study is needed to determine whether the rehabilitation and reconstruction that has been carried out for three years have met community satisfaction. The results of further analysis are expected to predict the level of community satisfaction for the implementation of rehabilitation and other reconstruction. The method used in this paper is predictive modeling using a data mining (DM) approach. Data were collected from all rehabilitation and reconstruction activities in Palu, Sigi, and Donggala with the scope of the earthquake, tsunami, and liquefaction disasters. The analysis results show that the Artificial Neural Network (ANN) and the support vector machine (SVM) with a DM approach can develop a community satisfaction prediction model to implement rehabilitation and reconstruction after the earthquake-tsunami and liquefaction disasters.

Estimating Interval of the Number of Errors for Embedded Software Development Projects

2014 ◽  
Vol 2 (3) ◽  
pp. 40-50 ◽  
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakasima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Software Development ◽  
Previous Investigation ◽  
Interval Estimation ◽  
Embedded Software ◽  
Point Estimation ◽  
Support Vector ◽  
Development Projects ◽  
Artificial Neural Network Ann

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

Data Mining Methods for Crude Oil Market Analysis and Forecast

2010 ◽  
pp. 184-203 ◽  
Author(s):  
Jue Wang ◽  
Wei Xu ◽  
Xun Zhang ◽  
Yejing Bao ◽  
Ye Pang ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Crude Oil ◽  
Nonlinear Models ◽  
Oil Price ◽  
Support Vector ◽  
Public And Private ◽  
Governmental Agencies ◽  
Artificial Neural Network Ann ◽  
Price Analysis

In this study, two data mining based models are proposed for crude oil price analysis and forecasting, one of which is a hybrid wavelet decomposition and support vector Machine (SVM) model and the other is an OECD petroleum inventory levels based wavelet neural network model (WNN). These models utilize support vector regression (SVR) and artificial neural network (ANN) technique for crude oil prediction and are made comparison with other forecasting models, respectively. Empirical results show that the proposed nonlinear models can improve the performance of oil price forecasting. The findings of this research are useful for private organizations and governmental agencies to take either preventive or corrective actions to reduce the impact of large fluctuation in crude oil markets, and demonstrate that the implications of data mining in public and private sectors and government agencies are promising for analyzing and predicting on the basis of data.

scholarly journals Perbandingan Teknik Klasifikasi Neural Network, Support Vector Machine, dan Naive Bayes dalam Mendeteksi Kanker Payudara

2020 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Derisma Derisma ◽  
Fajri Febrian
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Accuracy Rate ◽  
Cancer Disease ◽  
Network Support

Abstrak: Kanker payudara merupakan jenis kanker yang sering ditemukan oleh kebanyakan wanita. Di Indonesia Kanker payudara menempati urutan pertama pada pasien rawat inap di seluruh rumah sakit. Tujuan dari penelitian ini adalah melakukan diagnosis penyakit kanker payudara berbasis komputasi yang dapat menghasilkan bagaimana kondisi kanker seseorang berdasarkan akurasi algoritma. Penelitian ini menggunakan pemrograman orange python dan dataset Wisconsin Breast Cancer untuk pemodelan klasifikasi kanker payudara. Metode data mining yang diterapkan yaitu Neural Network, Support Vector Machine, dan Naive Bayes. Dalam penelitian ini didapat algoritma klasifikasi terbaik yaitu algoritma Kernel SVM dengan tingkat akurasi sebesar  98.9 % dan algoritma terendah yaitu Naive Bayes senilai 96.1 %.   Kata kunci: kanker payudara, neural network, support vector machine, naive bayes   Abstract: Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python orange programming and Wisconsin Breast Cancer dataset for a modeling and application of breast cancer classification. The data mining methods that were applied in this study were Neural Network, Support Vector Machine, dan Naive Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 98.9 % accuracy rate and Naïve Beyes was the lowest with 96.1 % of accuracy rate.   Keywords: breast cancer, neural network, support vector machine, naive bayes

Perbandingan Metode Support Vector Machine (SVM) dan Artificial Neural Network (ANN) pada Klasifikasi Gizi Balita

2021 ◽  
Vol 2021 (1) ◽  
pp. 1036-1043
Author(s):  
Harifa Hananti ◽  
Kartika Sari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kasus kekurangan gizi atau gizi buruk pada balita menyebar hampir di seluruh provinsi yang ada di Indonesia. Provinsi Sulawesi Barat merupakan salah satu provinsi yang memiliki nilai persentase kekurangan gizi pada balita, sehingga dari faktor-faktor yang mempengaruhi gizi balita sangat penting untuk dilakukan dalam pengklasifikasian. Data yang digunakan adalah data dari Puskesmas Salissingan pada Tahun 2018. Penelitian ini bertujuan untuk melakukan pengklasifikasian dan mendapatkan metode terbaik pada gizi balita (gizi baik & gizi kurang) di Puskesmas Salissingan Sulawesi Barat dengan metode support vector machine (SVM) dan artificial neural network (ANN). Metode klasifikasi yang terbaik dalam melihat ukuran ketepatan klasifikasi adalah metode SVM dan ANN. Dari hasil analisis diperoleh ukuran ketepatan klasifikasi pada metode ANN (accuracy=94,82%, precision=51.00%, recall=51.09%, dan AUC=0.910), sedangkan pada metode SVM (accuracy=94,46%, precision=46.08%, recall=50.59%, dan AUC=0.900) dan dari hasil ukuran tersebut diperoleh bahwa metode yang terbaik dalam pengklasifikasian gizi balita di Puskesmas Salissingan Sulawesi Barat adalah ANN.

scholarly journals Probabilistic estimation of software development effort techniques using machine learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1085
Author(s):  
Dr P. Vidya Sagar ◽  
Dr Nageswara Rao Moparthi ◽  
Venkata Naresh Mandhala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Support Vector ◽  
Development Effort ◽  
Probabilistic Estimation ◽  
Software Development Effort ◽  
Artificial Neural Network Ann ◽  
High Level

Precisely assessing programming exertion is likely the greatest test confronting for programming engineers. Assessments done at the prop-osition arrange has high level of incorrectness, where prerequisites for the degree are not characterized to the most reduced subtle elements, but rather as the venture advances and necessities are explained, exactness and certainty on appraise increments. It is vital to pick the correct programming exertion estimation systems for the forecast of programming exertion. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been utilized on guarantee dataset for forecast of programming exertion in this article.  

Cancer Detection Using Aritifical Neural Network and Support Vector Machine: A Comparative Study

2013 ◽  
Vol 65 (1) ◽  
Author(s):  
Sharifah Hafizah Sy Ahmad Ubaidillah ◽  
Roselina Sallehuddin ◽  
Nor Azizah Ali
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Human Life ◽  
Support Vector ◽  
Svm Classifier ◽  
Malignant Tumours ◽  
Cancer Dataset ◽  
Cancer Data ◽  
Standard Data ◽  
Artificial Neural Network Ann

Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs  which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets including benign and malignant tumours is specified to classify with proposed methods. The performance of both classifiers is evaluated using four different measuring tools which are accuracy, sensitivity, specificity and Area under Curve (AUC). This research has shown that the SVM classifier can obtain good performance in classifying cancer data compare to ANN classifier.

scholarly journals Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Thabo Michael Bafitlhile ◽  
Zhijia Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Optimization Method ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Climatic System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Semi Arid

The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.

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