scholarly journals Opinion Mining on Culinary Food Customer Satisfaction Using Naïve Bayes Based-on Hybrid Feature Selection

2019 ◽  
Vol 15 (1) ◽  
pp. 468 ◽  
Author(s):  
Oman Somantri ◽  
Dyah Apriliani
Keyword(s):  
Feature Selection ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Classification Model ◽  
Consumer Ratings ◽  
Bayes Algorithm ◽  
Restaurant Owners

<p>Conducting an assessment of consumer sentiments taken from social media in assessing a culinary food gives useful information for everyone who wants to get this information especially for migrants and tourists, in th other hand that information is very valuable for food stall and restaurant owners as information in improvinf food quality. Overcoming this problem, a sentiment analysis classification model using naïve bayes algorithm (NB) was applied to get this information. This problem occurs is the level of accuracy of classification of consumer ratings of culinary food is still not optimal because the weight of values in the data preprocessing process are not optimal. In this paper proposed a hybrid feature selection models to overcome the problems in the process of selecting the feature attributes that have not been optimal by using a combination of information gain (IG) and genetic algorithm (GA) algorithms. The result of this research showed that after the experiment and compared to using others algorithms produce the best of the level occuracy is 93%.</p>

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

scholarly journals Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain

2020 ◽  
Vol 1 (1) ◽  
pp. 19-26
Author(s):  
Rakhmi Khalida ◽  
Siti Setiawati
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Traffic Violations ◽  
The Government ◽  
Bayes Algorithm ◽  
User Friendly

Abstract   The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impression. Twitter social media is one of the objects to express opinions because it is user friendly, updated topics, and openly accesses tweets. Opinions on Twitter are collected, then the preprocessing stage is performed, then the selection of information gain features helps reduce noise caused by irrelevant labels, the next step is the classification of sentiments with the Naïve Bayes algorithm and finally polarity sentiments. This research resulted in an accuracy of 41.82%, a precision of 50.51% and a recall of 45.45%.   Keywords: Sentiment analysis, E-ticketing, Information Gain, Naive Bayes   Abstrak   Pemerintah Indonesia melakukan langkah perubahan untuk memperbaiki sistem pelayanan publik dalam pelanggaran berlalu-lintas yaitu dengan menerapkan sistem e-Tilang. Sistem ini menjadi solusi mendisiplinkan para pengendara kendaraan bermotor dari banyaknya melakukan pelanggaran berlalu-lintas. Keberadaan e-Tilang juga menjadi solusi mencegah kenakalan penegak hukum dari pungutan liar, istilah damai ditempat, hingga akuntabilitas uang denda. Dalam penelitian ini melakukan analisis sentimen tentang sistem e-Tilang atau opinion mining untuk mengelompokan ragam komentar masyarakat yang memberikan kesan positif, negatif atau netral. Media sosial Twitter menjadi salah satu objek untuk menyampaikan opini karena user friendly, topik ter-update, dan terbuka mengakses tweet. Opini pada twitter dikumpulkan, lalu dilakukan tahapan preprocessing, selanjutnya dengan seleksi fitur information gain membantu mengurangi noise yang disebabkan oleh label-label yang tidak relevan, tahap selanjutnya adalah klasifikasi sentimen dengan algoritma Naïve Bayes dan terakhir sentimen polarity. Penelitian ini menghasilkan accuracy 41,82%, presisi 50,51% dan recall 45,45%.   Kata kunci: Analisis sentimen, E-Tilang, Information Gain, Naive Bayes

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

scholarly journals Top-k Feature Selction Untuk Deteksi Penyakit Hepatitis Menggunakan Algoritme Naïve Bayes

2020 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Riska Wibowo ◽  
Henny Indriyawati
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Decision Making Process ◽  
Accuracy Rate ◽  
Chemical Substances ◽  
Drugs And Alcohol ◽  
Bayes Algorithm

Abstract. Becoming one of the society health problems in the world, hepatitis is an inflammation liver disease caused by a virus, bacterial infection, chemical substances including drugs and alcohol. In this research, for the dataset of hepatitis having high dimensionality, its value for each attribute was calculated using weight information gain method. Then, the attributes were selected by using top-k methods and were classified by using Naïve Bayes Algorithm respectively. This research showed that 9 out of 20 attributes had chosen to be the highest top-9 with an accuracy rate of 85.57%. Later on, this research can be useful for a consideration in a decision making process for various subjects related to feature selection and Naïve Bayes Algorithm method and also for predicting hepatitis.Keywords: data mining, weight information gain, Naïve Bayes algorithmAbstrak. Penyakit hepatitis merupakan masalah kesehatan masyarakat di dunia. Penyakit hepatitis merupakan penyakit peradangan hati yang disebabkan oleh virus, infeksi bakteri, zat-zat kimia termasuk obat-obatan dan alkohol. Pada penelitian ini, dataset hepatitis yang memiliki data berdimensi tinggi akan dihitung nilai bobot dari masing-masing atribut menggunakan metode weight information gain. Setelah dihitung nilai bobot dilakukan pemilihan atribut, atribut yang dipilih menggunakan metode top-k. Kemudian dilakukan klasifikasi menggunakan algoritme Naïve Bayes. Hasil penelitian menunjukkan dari 20 atribut, terpilih top-9 tertinggi dengan nilai akurasi 85.57%. Dengan adanya penelitian ini dapat digunakan sebagai bahan pertimbangan dan pengambilan keputusan pada berbagai bidang yang berkaitan dengan metode feature selection, algoritme Naïve Bayes, dan di dalam memprediksi penyakit hepatitis.Kata Kunci: data mining, weight information gain, algoritma Naïve Bayes

scholarly journals Sentimen Analisis Komentar Toxic pada Grup Facebook Game Online Menggunakan Klasifikasi Naïve Bayes

2020 ◽  
Vol 5 (3) ◽  
pp. 356
Author(s):  
Renaldy Permana Sidiq ◽  
Budi Arif Dermawan ◽  
Yuyun Umaidah
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Text Processing ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Classification Model ◽  
Testing Data ◽  
F Measure

Toxic comments are comments made by social media users that contain expressions of hatred, condescension, threatening, and insulting. Social media users who are on average still teenagers with a nature that still cannot be controlled completely becomes a matter of great concern when they comment, their comments can be studied as text processing. Sentiment analysis can be used as a solution to identifying toxic comments by dividing them into two classifications. Where the data used amounted to 1,500 taken from social media Facebook in the private group Arena of Valor community. The dataset is divided into 2 classes: toxic and non-toxic. This research uses Naive Bayes with TF-IDF transformation and Information Gain feature selection and use distribution ratio 80:20. It will be compared the results of the evaluation where Naive Bayes without transformation, using TF-IDF transformation, and TF-IDF using Information Gain feature selection. The results of the comparison of evaluations from confusion matrix that have been carried out obtained the best classification model is to use the ratio of training and testing data 80:20 with TF-IDF transformation resulting in an accuracy of 75%, precision of 63%, recall of 67%, and F-measure of 64%.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

scholarly journals Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 884
Author(s):  
Antonio García-Domínguez ◽  
Carlos E. Galván-Tejada ◽  
Ramón F. Brena ◽  
Antonio A. Aguileta ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Activity Classification ◽  
Environmental Sound ◽  
Non Invasive ◽  
Akaike Criterion ◽  
Data Source ◽  
Feature Selection Techniques

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

scholarly journals OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

2019 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Emha Taufiq Luthfi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Education Institution ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Education Development ◽  
Main Requirement ◽  
Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy. 

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