scholarly journals Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach

2020 ◽  
Vol 5 (3) ◽  
pp. 75
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani ◽  
Des Alwine Zayanti ◽  
Indah Meiliana Sari
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Naïve Bayes ◽  
Classification Systems ◽  
Fuzzy Approach ◽  
Recycling Industry ◽  
Average Accuracy ◽  
Input Variables ◽  
The Right

Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

scholarly journals Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Original Model ◽  
Model Based ◽  
Accuracy Level ◽  
Conveyor Belts ◽  
Sorting Process ◽  
Input Variables

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

scholarly journals Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier

2021 ◽  
Vol 5 (2) ◽  
pp. 630
Author(s):  
I Putu Ananda Miarta Utama ◽  
Sri Suryani Prasetyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Hybrid Classifier ◽  
Review Analysis ◽  
Tourism Sector ◽  
Average Accuracy ◽  
The Media ◽  
The Right

In the hotel tourism sector, of course, it cannot be separated from the role of social media because tourists tend to share experiences about services and products offered by a hotel, such as adding pictures, reviews, and ratings which will be helpful as references for other tourists, for example on the media online TripAdvisor. However, tourists' many experiences regarding a hotel make some people feel confused in determining the right hotel to visit. Therefore, in this study, an aspect-based analysis of reviews on hotels is carried out, which will make it easier for tourists to determine the right hotel based on the best category aspects. The dataset used is the TripAdvisor Hotel Reviews dataset which is already on the Kaggle website. And has five aspects, namely Room, Location, Cleanliness, Registration, and Service. A review analysis was carried out into positive and negative categories using the Random Forest, SVM, and Naive Bayes based Hybrid Classifier methods to solve this problem. In this study the Hybrid Classifier method gets better accuracy than the classification using one algorithm on multi-aspect data, namely the Hybrid Classifier got an average accuracy 84%, Naïve Bayes got an average accuracy 82.4%, Random Forest got an average accuracy 82.2%, and use SVM got an average accuracy 81%

scholarly journals User Loyalty Prediction Using Naive Bayes Method in "Udatari" an art Performance Marketplace

2021 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Ngurah Agus Sanjaya ER ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
K Value ◽  
Customer Lifetime ◽  
Average Accuracy ◽  
Silhouette Index ◽  
The Right ◽  
Past Experiences ◽  
Naive Bayes Method

Udatari is the first traditional dance platform in Indonesia which provides information about traditional events such as, dance tutorials, group dancer and dance attributes. The tight competition in the startup world, requires Udatari as a new startup to manage application users optimally. Knowing loyal users will help startups determine the right marketing strategy. In this study, the method used for clustering is the K-Means method where this method seeks to classify existing data into several groups provided that the data in one group have the same characteristics as each other. The model used for the clustering process is RFM, namely recency, frequency and monetary. The purpose of this clustering is to get the segmentation of users who have different Customer Lifetime Value. The second method for conducting classification is the Naïve Bayes method, where this method predicts future opportunities based on past experiences. The purpose of this classification is to predict new users into the user segmentation obtained from the clustering results. From the results of this study, the optimum k value for K-Means are 3 clusters with the largest CLV value in the second cluster where testing on this method uses the Silhouette Index. Furthermore, for the test results of the Naïve Bayes method, the average accuracy value is 97.44% where the accuracy of each class is 92.31% for cluster 0 (first cluster), 100% for the second cluster and 100% for the third cluster. Keywords: K-Means, Naïve Bayes, Loyalty, Segmentation, RFM

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

Improving Techniques for Naïve Bayes Text Classifiers

2010 ◽  
pp. 111-127
Author(s):  
Han-joon Kim
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Systems ◽  
Classification Model ◽  
Learning Approaches ◽  
Learning Framework ◽  
The Em Algorithm ◽  
Meta Learning ◽  
Text Classifiers

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.

scholarly journals PENERAPAN DATAMINING DENGAN METODE KLASIFIKASI UNTUK STRATEGI PENJUALAN PRODUK DI UD.SELAMAT SELULAR

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sinta Maulina Dewi ◽  
Agus Perdana Windarto ◽  
Dedy Hartama
Keyword(s):  
Data Collection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Collection Method ◽  
Data Collection Method ◽  
Sales Strategy ◽  
Bayes Algorithm ◽  
Small Shop ◽  
The Right

In the current era of globalization, developments in various fields of business are accelerating. Both in the culinary field and other fields. One of the most sought after business developments is in the field of counters or credit sales. UD.Selamat Selular was founded in 2010, which only has a small shop with no employees to date which has more than 20 employees. This business continues to develop in ever-increasing business competition. Therefore a sales strategy is needed so that it is not inferior to other trading businesses. In this research, it is necessary to test the previous data in order to find out the right sales strategy using Naïve Bayes. The data collection method was conducted by questionnaire and interview with a questionnaire of 160 respondents. From the results of this study it can be concluded that the model formed using the Naïve Bayes algorithm produces an algorithm of 0.650 so that it is classified as Excellent Classification.Keywords: Datamining, Naïve Bayes, Sales Strategy.

scholarly journals KLASIFIKASI TEKS MENGGUNAKAN CHI SQUARE FEATURE SELECTION UNTUK MENENTUKAN KOMIK BERDASARKAN PERIODE, MATERI DAN FISIKDENGAN ALGORITMA NAIVEBAYES

Compiler ◽  
2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Siti Anisah ◽  
Anton Setiawan Honggowibowo ◽  
Asih Pujiastuti
Keyword(s):  
Feature Selection ◽  
Error Rate ◽  
Classification System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Chi Square ◽  
Oracle Database ◽  
Category O ◽  
The Difference ◽  
Bayes Algorithm

A comic has its own characteristics compared the other types of books. The difference between comic and other books can be seen from the category o f period, material and physical. Comicand other booksneeded an application o f classification system. Looking for the problem, classification system was made using Chi Square Feature Selection and Naive Bayes algorithm to determine the comic based on the period, material and physical. Delphi programming language and Oracle Database are used to build the Classification System. Chi Square Feature Selection acquired trait a comic is in 0.10347 and which not comic is in 1.9531. Furthermore, data is classified by the Naive Bayes algorithm. From 120 titles o f comic that consists 60 titles o f comic and non comicused to build classesfor trainand 60 titles o f comic and non comic used to test. The results o f Naive Bayesalgorithm for comic is 96,67%with 3.33% error rate, and non comic is 90% with 10% error rate. The classification to determine comic is good.

scholarly journals KLASIFIKASI PENGENALAN BUAH MENGGUNAKAN ALGORITMA NAIVE BAIYES

2019 ◽  
Vol 2 (2) ◽  
pp. 83-88
Author(s):  
Arif Saputra
Keyword(s):  
Fuzzy Logic ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Development Platform ◽  
Apple Varieties ◽  
Software Methodology ◽  
Average Accuracy ◽  
Bayes Algorithm ◽  
Conducting Research ◽  
And Performance

Manually sorting varieties of apples result in high costs, subjectivity, boredom, and inconsistencies associated with humans. A means is needed to distinguish between types of apples and, therefore, some reliable techniques are necessary to identify varieties quickly and without damage. The purpose of conducting research is to investigate the application and performance for Naive Bayes algorithm for apple varieties. This software methodology involves image acquisition, preprocessing, segmentation and analysis classification varieties for apple. The prototype of Apple's classification system was built using the MATLAB R2017 development platform environment. The results in this study indicate that the estimated average accuracy, sensitivity, precision, and specificity are 81%, 73%, 100%, and 70%, respectively. MLP-Neural shows that performance of the Naive Bayes technique is consistent with Principal, Fuzzy Logic, and Neural analysis with 89%, 91%, 87%, and 82% respectively in terms of accuracy. This study shows that Naif Bayes has excellent potential for identifying nondestructive and accurate apple varieties.

scholarly journals Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

TEM Journal ◽  
2021 ◽  
pp. 1738-1744
Author(s):  
Joseph Teguh Santoso ◽  
Ni Luh Wiwik Sri Rahayu Ginantra ◽  
Muhammad Arifin ◽  
R Riinawati ◽  
Dadang Sudrajat ◽  
...  
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
T Test ◽  
Bayes Method ◽  
Average Accuracy ◽  
Difference Test ◽  
Naive Bayes Method ◽  
Better Than

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

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