Comparison of Artificial Neural Network and Gaussian Naïve Bayes in Recognition of Hand-Writing Number

2018 ◽  
Author(s):  
Herman ◽  
Lukman Syafie ◽  
Dolly Indra ◽  
As'ad Djamalilleil ◽  
Nirsal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Artificial Neural ◽  
Hand Writing

scholarly journals Komparasi Algoritma Data Mining Menggunakan Forward selection Pada Prediksi Harga Jagung

2021 ◽  
Vol 5 (2) ◽  
pp. 157
Author(s):  
Mohamad Efendi Lasulika ◽  
Andi Bode
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Forward Selection ◽  
Artificial Neural ◽  
Type Data

Meningkatnya volume produksi jagung yang diperdagangkan dan kecenderungan harga jagung dipasaran menuntut kebijakan pemerintah dalam mengendalikan stabilasi harga jagung. Sehingga pemerintah kesulitan dalam menentukan atau memprediksi harga komoditi yang akan datang, namun pada penelitian ini hanya fokus pada beberapa algoritma klasifikasi untuk mengetahui algoritma apakah yang mempunyai tingkat akurasi tertinggi dalam hal prediksi harga jagung sehingga dapat digunakan dalam melakukan prediksi harga beberapa hari kedepannya. Dalam data mining ada beberapa algoritma klasifikasi yang dapat digunakan, seperti K-Neural Network, Artificial Neural Network, Naïve Bayes, Regresi Linear, C-45 dll, namun pada penelitian ini hanya fokus pada dua metode saja yaitu Naïve Bayes dan K- Neural Network. Berdasarkan hasil penelitian yang dilakukan metode K-nn merupakan metode yang sangat bagus atau baik dalam melakukan prediksi ataupun klasifikasi, hala ini dapat dilihat dari hasil RMSE yang di hasilkan yaitu 0,05, metode ini mampu menghasilkan nilai terbaik walaupun tanpa adanya penambahan metode lain seperti forward selection, sementara itu untuk naïve bayes metode ini juga merupakan metode terbaik dalam melakukan prediksi ataupun klasifikasi, akan tetapi naïve bayes mempunyai beberapa kekurangan apabila digunakan untuk type data univariate ataupun numerical. Penambahan forward selection kepada pengolahan data dapat membantu menghasilkan akurasi yang baik pula. Walaupun tanpa forward selection K-NN dan Naïve bayes merupakan metode komputasi yang sangat baik dalam prediksi ataupun klasifikasi. Kata kunci: Harga Jagung, Komparasi Metode, K-Neural Network, Naïve bayes, forward selection

scholarly journals Prediksi Hipertensi menggunakan Decision Tree, Naïve Bayes dan Artificial Neural Network pada software KNIME

Techno Com ◽  
2020 ◽  
Vol 19 (4) ◽  
pp. 353-363
Author(s):  
Mayanda Mega Santoni ◽  
Nurul Chamidah ◽  
Nurhafifah Matondang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Network Data ◽  
Artificial Neural ◽  
Fold Cross Validation

Hipertensi merupakan salah satu penyakit tidak menular yang dapat menyebabkan kematian karena meningkatkan resiko munculnya berbagai penyakit seperti gagal ginjal, gagal jantung, bahkan stroke. Resiko hipertensi disebabkan oleh beberapa faktor penyebab seperti usia, keturunan, pola makan dan olahraga, dan merokok. Teknologi artificial intelligence yakni machine learning dimanfaatkan di bidang kesehatan khususnya prediksi penyakit hipertensi. Pada penelitian ini diimplementasi tiga algoritma machine learning yakni decision tree, naïve bayes dan artificial neural networks. Data yang digunakan pada penelitian ini sebanyak 274 data yang diperoleh dari hasil kuesioner dengan 26 pertanyaan, dimana 25 pertanyaan adalah variabel faktor resiko dan satu pertanyaan merupakan kelas yang menyatakan responden memiliki riwayat hipertensi atau tidak. Data diolah menggunakan platform analisis data yakni KNIME. Sebelum data diolah untuk membangun model klasifikasi menggunakan decision tree, naïve bayes dan artificial neural network, data dipraproses terlebih dahulu dengan melakukan imputasi missing value, oversampling dan normalisasi data. Selanjutnya pembagian data menggunakan 5-fold cross validation. Model klasifikasi yang diperoleh dievaluasi menggunakan nilai akurasi, recall dan precision. Hasil evaluasi dari eksperimen yang dilakukan diperoleh bahwa algoritma artificial neural network memiliki tingkat performa lebih baik dibandingkan decision tree dan naïve bayes dengan nilai akurasi sebesar 94.7%, recall sebesar 91.5% dan precision sebesar 97.7%.

scholarly journals Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods

2021 ◽  
Vol 7 (1) ◽  
pp. 10-20
Author(s):  
Fatri Nurul Inayah ◽  
Sri Suryani Prasetiyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dengue Fever ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Hemorrhagic Fever ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Artificial Neural

Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods Naïve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.

scholarly journals Oversampling Method on Classifying Hypertension Using Naive Bayes, Decision Tree, and Artificial Neural Network

2020 ◽  
Vol 4 (4) ◽  
pp. 635-641
Author(s):  
Nurul Chamidah ◽  
Mayanda Mega Santoni ◽  
Nurhafifah Matondang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Missing Values ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Training Data ◽  
Validation Data ◽  
Artificial Neural

Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records. Oversampling in this study is applied to hypertension dataset where hypertensive class has a small number of records when compared to the number of records for non-hypertensive classes. This study aims to evaluate the effect of oversampling on the classification of hypertension dataset consisting of hypertensive and non-hypertensive classes by utilizing the Naïve Bayes, Decision Tree, and Artificial Neural Network (ANN) as well as finding the best model of the three algorithms. Evaluation of the use of oversampling on hypertension dataset is done by processing the data by imputing missing values, oversampling, and transforming data into the same range, then using the Naïve Bayes, Decision Tree, and ANN to build classification models. By dividing 80% of data as training data to build models and 20% as validation data for testing models, we had an increase in classification performance in the form of accuracy, precision, and recall of the oversampled data when compared without oversampling. The best performance in this study resulted in the highest accuracy using ANN with 0.91, precision 0.86 and recall 0.99.

scholarly journals Prediction Model of Student Achievement in Business Computer Disciplines

2020 ◽  
Vol 15 (20) ◽  
pp. 160
Author(s):  
Pratya Nuankaew ◽  
Wongpanya Nuankaew ◽  
Direk Teeraputon ◽  
Kanakarn Phanniphong ◽  
Sittichai Bussaman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Data Science ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Disruptive Technology ◽  
Artificial Neural ◽  
Bayes Algorithm

An educational program that does not accept the change of disruptive technology will inevitably result in future destruction. There are two objectives including (1) to construct reasonable students’ dropout prediction model for business computer disciplines, and (2) to evaluate the model performance. Data collected consists of 2,017 records from students who enrolled in the business computer program at the School of Information and Communication Technology, the University of Phayao. Research tools are divided into two parts. (1) Modelling; it consisted of the Artificial Neural Network Algorithm, Decision Tree Algorithm, and Naïve Bayes Algorithm. (2) Model testing; it consisted of the confusion matrix performance, accuracy, precision, and recall measurement. It is a clear innovation in the research that the researcher combines the knowledge of data science in analysis to improve the academic achievement of students in higher education in Thailand. From the analysis results, its show that the model developed from using Artificial Neural Network algorithms has the highest accuracy in the first three data sets (89.04%, 92.70% and 93.71%), and the last model is appropriate for Naïve Bayes algorithm (91.68%). Finally, it is necessary to conduct additional research and present research results to relevant parties and organizations.

scholarly journals Writer Identification Based on Hand Writing using Artificial Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 257-264
Author(s):  
Rosalia Arum Kumalasanti ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Interaction ◽  
Social Relations ◽  
Image Data ◽  
Training Phase ◽  
Testing Phase ◽  
Training Stage ◽  
Artificial Neural ◽  
Hand Writing

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

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