scholarly journals Comparison of the Performance of the k-Nearest Neighbor, Naïve Bayes Classifier and Support Vector Machine Algorithm With SMOTE for Classification of Bully Behavior on the WhatsApp Messenger Application

2020 ◽  
Author(s):  
Irwansyah Saputra ◽  
Puput Irfansyah ◽  
Erlando Doni Sirait ◽  
Dwi Dani Apriyani ◽  
Michael Sonny
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Bully Behavior

scholarly journals PREDIKSI FINANCIAL DISTRESS PERUSAHAAN PERTAMBANGAN BATUBARA DI BURSA EFEK INDONESIA MENGGUNAKAN SUPPORT VECTOR MACHINE, K-NEAREST NEIGHBOR DAN NAIVE BAYES CLASSIFIER

2020 ◽  
Vol 4 (6) ◽  
pp. 967-978
Author(s):  
Nurmayanti Alifia ◽  
Brady Rikumahu
Keyword(s):  
Support Vector Machine ◽  
Financial Distress ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Naïve Bayes Classifier

Dalam lima tahun terakhir, industri batubara mengalami penurunan volume ekspor yang berdampak pada menurun nya kinerja keuangan perusahaan yang bergerak pada industri batubara. Pada penelitian ini prediksi financial distress dilakukan menggunakan metode data mining yaitu menggunakan model Support Vector Machine, k-Nearest Neighbor, dan Naive Bayes Classifier dengan menggunakan 5 rasio keuangan sebagai parameter inputnya, yaitu Current Ratio, Debt to Assets Ratio, Quick Ratio, Return on Asset, dan Working Capital to Total Assets Ratio. Hasil Penelitian menunjukan bahwa tingkat akurasi prediksi dengan model K-Nearest Neighbor adalah sebesar 89,5% pada data uji dan 88,6% pada data latih, model Naïve Bayes Classifier adalah sebesar 84,5% pada data uji dan 82,3% pada data latih, sedangkan model Support Vector Machine dengan menggunakan fungsi kernel RBF C=10 dan nilai Gamma=2 adalah sebesar 94,7% pada data uji dan 86,5% pada data latih. Dengan ini maka disimpulkan model SVM memiliki kinerja paling baik diantara model KNN dan NBC.

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%.

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 SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER

2021 ◽  
Vol 2 (2) ◽  
pp. 101-107
Author(s):  
Akhmad Muzaki ◽  
Arita Witanti
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Local Elections ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Public Sentiment

The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%

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.

Sign in / Sign up

Export Citation Format

Share Document