Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek

2021 ◽  
Vol 5 (6) ◽  
pp. 1120-1126
Author(s):  
Jessica Widyadhana Iskandar ◽  
Yessica Nataliani
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Brand Image ◽  
Naïve Bayes ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Public ◽  
Unique Shape ◽  
Design Aspect

The Samsung Galaxy Z Flip 3 is one of the gadgets that are currently popular among the public because of its unique shape and features. Youtube is one of the social media that can be accessed and enjoyed by the public, one of which is gadget review content on the GadgetIn channel. Youtube can provide information, whether people accept or are interested in this new gadget or not. This study aims to determine the sentiment of a gadget producer. Based on the results of the analysis and testing that has been carried out on the Youtube comments of the Samsung Galaxy Z Flip 3 gadget with a total of 9,597 comments, more users gave positive opinions in the design aspect and negative opinions on the price, specifications and brand image aspects. By using the CRISP-DM model and comparing the Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) classification methods, it is proven that the SVM classification model shows the best results. The average accuracy of SVM is 96.43% seen from four aspects, namely the design aspect of 94.40%, the price aspect of 97.44%, the specification aspect of 96.22%, and the brand image aspect of 97.63%.  

scholarly journals RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

2019 ◽  
Vol 9 (6) ◽  
pp. 4866
Author(s):  
Rajni Rajni ◽  
Amandeep Amandeep
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Early Stage ◽  
Human Life ◽  
Naïve Bayes ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor ◽  
Fast Pace ◽  
Bayes Algorithm

<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

scholarly journals Prediksi Harga Minyak Kelapa Sawit Dalam Investasi Dengan Membandingkan Algoritma Naïve Bayes, Support Vector Machine dan K-Nearest Neighbor

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Deny Haryadi ◽  
Rila Mandala
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Harga minyak kelapa sawit bisa mengalami kenaikan, penurunan maupun tetap setiap hari karena faktor yang mempengaruhi harga minyak kelapa sawit seperti harga minyak nabati lain (minyak kedelai dan minyak canola), harga minyak mentah dunia, maupun nilai tukar riil antara kurs dolar terhadap mata uang negara produsen (rupiah, ringgit, dan canada) atau mata uang negara konsumen (rupee). Untuk itu dibutuhkan prediksi harga minyak kelapa sawit yang cukup akurat agar para investor bisa mendapatkan keuntungan sesuai perencanaan yang dibuat. tujuan dari penelitian ini yaitu untuk mengetahui perbandingan accuracy, precision, dan recall yang dihasilkan oleh algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam menyelesaikan masalah prediksi harga minyak kelapa sawit dalam investasi. Berdasarkan hasil pengujian dalam penelitian yang telah dilakukan, algoritma Support Vector Machine memiliki accuracy, precision, dan recall dengan jumlah paling tinggi dibandingkan dengan algoritma Naïve Bayes dan algoritma K-Nearest Neighbor. Nilai accuracy tertinggi pada penelitian ini yaitu 82,46% dengan precision tertinggi yaitu 86% dan recall tertinggi yaitu 89,06%.

scholarly journals Comparative analysis on bayesian classification for breast cancer problem

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Wan Nor Liyana Wan Hassan Ibeni ◽  
Mohd Zaki Mohd Salikon ◽  
Aida Mustapha ◽  
Saiful Adli Daud ◽  
Mohd Najib Mohd Salleh
Keyword(s):  
Breast Cancer ◽  
Bayesian Networks ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Likelihood Estimation ◽  
Predictive Distribution ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.

scholarly journals COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS: HOLDOUTS AS ACCURACY ESTIMATION

2016 ◽  
Vol 1 (1) ◽  
pp. 13 ◽  
Author(s):  
Debby Erce Sondakh
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Decision Rules ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Accuracy Estimation ◽  
F Measure

Penelitian ini bertujuan untuk mengukur dan membandingkan kinerja lima algoritma klasifikasi teks berbasis pembelajaran mesin, yaitu decision rules, decision tree, k-nearest neighbor (k-NN), naïve Bayes, dan Support Vector Machine (SVM), menggunakan dokumen teks multi-class. Perbandingan dilakukan pada efektifiatas algoritma, yaitu kemampuan untuk mengklasifikasi dokumen pada kategori yang tepat, menggunakan metode holdout atau percentage split. Ukuran efektifitas yang digunakan adalah precision, recall, F-measure, dan akurasi. Hasil eksperimen menunjukkan bahwa untuk algoritma naïve Bayes, semakin besar persentase dokumen pelatihan semakin tinggi akurasi model yang dihasilkan. Akurasi tertinggi naïve Bayes pada persentase 90/10, SVM pada 80/20, dan decision tree pada 70/30. Hasil eksperimen juga menunjukkan, algoritma naïve Bayes memiliki nilai efektifitas tertinggi di antara lima algoritma yang diuji, dan waktu membangun model klasiifikasi yang tercepat, yaitu 0.02 detik. Algoritma decision tree dapat mengklasifikasi dokumen teks dengan nilai akurasi yang lebih tinggi dibanding SVM, namun waktu membangun modelnya lebih lambat. Dalam hal waktu membangun model, k-NN adalah yang tercepat namun nilai akurasinya kurang.

KOMPARASI ALGORITMA KLASIFIKASI PADA ANALISIS REVIEW HOTEL

2018 ◽  
Vol 14 (2) ◽  
pp. 261
Author(s):  
Lila Dini Utami
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Service Providers ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Auc Value

At this time the freedom to express opinions in oral and written forms about everything is very easy. This activity can be used to make decisions by some business people. Especially by service providers, such as hotels. This will be very useful in the development of the hotel business itself. But the review data must be processed using the right algorithm. So this study was conducted to find out which algorithms are more feasible to use to get the highest accuracy. The methods used are Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). From the process that has been done, the results of Naïve Bayes accuracy are 71.50% with the AUC value is 0.500, Support Vector Machine is 72.50% with the AUC value is 0.936 and the accuracy results if using the k-Nearest Neighbor algorithm is 75.00% with the AUC value is 0.500. The use of the k-Nearest Neighbor algorithm can help in making more appropriate decisions for hotel reviews at this time.

scholarly journals KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL

2019 ◽  
Vol 11 (1) ◽  
pp. 11-16
Author(s):  
Mohamad Efendi Lasulika
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Types ◽  
Neural Network Algorithm ◽  
Bayes Algorithm

One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.

scholarly journals KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER

2019 ◽  
Vol 15 (2) ◽  
pp. 267-274
Author(s):  
Tati Mardiana ◽  
Hafiz Syahreva ◽  
Tuslaela Tuslaela
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbor

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif.  Penelitian ini menggunakan data realtime dari  tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil  pengujian  dengan  confusion  matrix  diperoleh  nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine  sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine  dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.  

Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

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