scholarly journals FILTERING SPAM EMAIL MENGGUNAKAN METODE NAIVE BAYES

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Aria Dadi Wibisono ◽  
Sampurna Dadi Rizkiono ◽  
Agus Wantoro
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Email Spam

Spam adalah email yang tidak diminta yang berisi promosi produk, pornografi, virus dan content-content yang tidak penting, yang dikirim ke banyak orang. Masalah spam dapat diatasi dengan adanya aplikasi filtering email, yaitu aplikasi yang secara otomatis mendeteksi sebuah email, apakah email tersebut spam atau bukan. Naive Bayes merupakan metode Klasifikasi sederhana. Metode ini memanfaatkan teorema probabilitas yaitu mencari peluang terbaik, dengan memprediksi probabilitas di masa depan berdasarkan informasi di masa sebelumnya. Tujuan utama penelitian ini adalah mengkaji penerapan metode Naive Bayes untuk menentukan email spam dan email ham. Hasil pengujian aplikasi terhadap 5 email yang terdiri dari 2 email spam dan 3 email ham.

scholarly journals IMPLEMENTASI PENDETEKSIAN SPAM EMAIL MENGGUNAKAN METODE TEXT MINING DENGAN ALGORITMA NAÏVE BAYES DAN DECISION TREE J48

2021 ◽  
Vol 9 (2) ◽  
pp. 244-252
Author(s):  
Rizka Safitri Lutfiyani ◽  
Niken Retnowati
Keyword(s):  
Text Mining ◽  
Decision Tree ◽  
Naive Bayes ◽  
Word List ◽  
Naïve Bayes ◽  
Stop Word ◽  
Email Spam

Email cukup populer sebagai salah satu media komunikasi digital. Hal tersebut dikarenakan proses pengiriman pesan dengan email yang mudah. Sayangnya, kebanyakan pesan dalam email adalah email spam. Spam adalah pesan yang tidak diinginkan penerima pesan karena spam biasanya berisi pesan iklan maupun pesan penipuan. Ham adalah pesan yang diinginkan penerima pesan. Salah satu cara untuk menyortir pesan-pesan tersebut adalah dengan melakukan pengklasifikasian pesan email menjadi spam maupun ham. Naïve Bayes dan decision tree J48 ialah algoritma yang dapat digunakan untuk mengklasifikasikan pesan email. Oleh karena itu, penelitian ini bertujuan membandingkan efektifitas algoritma Naïve Bayes dan decision tree J48 dalam penyortiran email spam. Metode yang digunakan adalah text mining. Data yang berisi teks pesan email berbahasa Inggris akan diproses terlebih dahulu sebelum diklasifikasikan dengan Naïve Bayes dan decision tree J48. Tahap pra proses tersebut meliputi tokenisasi, pembuangan stop word list, stemming, dan seleksi atribut. Selanjutnya, data teks pesan email akan diproses dengan algoritma Naïve Bayes dan decision tree J48. Algoritma Naïve Bayes adalah algoritma pengklasifikasi yang berdasarkan pada teori keputusan Bayesian sedangkan algoritma decision tree J48 ialah pengembangan dari algoritma decision tree ID3. Hasil penelitian ini adalah algoritma decision tree J48 mendapat akurasi yang lebih tingggi dari algoritma Naïve Bayes. Algoritma decision tree J48 mendapat 93,117% sedangkan Naïve Beyes memiliki akurasi 88,5284%. Kesimpulan dari penelitian ini adalah algoritma decision tree J48 lebih unggul dibanding Naive Bayes untuk menyortir email spam jika dilihat dari tingkat akurasi masing-masing algoritma.

scholarly journals Analysis of Naïve Bayes Algorithm for Email Spam Filtering across Multiple Datasets

2017 ◽  
Vol 226 ◽  
pp. 012091 ◽  
Author(s):  
Nurul Fitriah Rusland ◽  
Norfaradilla Wahid ◽  
Shahreen Kasim ◽  
Hanayanti Hafit
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Spam Filtering ◽  
Multiple Datasets ◽  
Bayes Algorithm ◽  
Email Spam

scholarly journals Indonesian language email spam detection using N-gram and Naïve Bayes algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 2012-2019
Author(s):  
Yustinus Vernanda ◽  
Seng Hansun ◽  
Marcel Bonar Kristanda
Keyword(s):  
Data Exchange ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayesian Filtering ◽  
Spam Filter ◽  
N Gram ◽  
Bayes Algorithm ◽  
Rest Api ◽  
Email Spam ◽  
F Measure

Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.

scholarly journals An Efficient Email Spam Detection using Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 5258-5262
Keyword(s):  
Naive Bayes ◽  
High Volume ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The People ◽  
E Mail ◽  
Email Spam

This research paper proposes the electronics mail is small known as E-mail is used for communication between the people to person. E mail is providing as an necessary contribution for messaging by internet. Spam e mails are the unwanted messages that arise in high volume and are used by spammers for revealing users personal credentials. These e mails are regularly some sort of company/control announcement or viruses that the user receive without any notification. So as to defeat it, there need aid exactly existing frameworks that still don't keep them from striking. Therefore, there is a require should manufacture and proficient framework that adequately detects and more keeps the spam messages In those server utilizing the Naïve bayes classifier. Naïve bayes classifier is a mainstream statistical classifier utilized fundamentally for content arrangement

scholarly journals Pengukuran Kinerja Spam Filter Menggunakan Graham's Naïve Bayes Classifier

2013 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Julio Adisantoso ◽  
Wildan Rahman
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Rule Based ◽  
Spam Filter ◽  
Content Based Filtering ◽  
Email Spam

<p>Email spam telah menjadi masalah utama bagi pengguna dan penyedia jasa Internet. Pendekatan heuristic telah dilakukan untuk menyaring spam seperti black-listing atau rule-based filtering, namun hasilnya kurang memuaskan sehingga pendekatan berbasis konten (content-based filtering) menggunakan pengklasifikasi naïve Bayes lebih banyak digunakan saat ini. Penelitian ini bertujuan membandingkan pengklasifikasi naïve Bayes multinomial yang menggunakan atribut boolean dengan versi Graham, dan juga membandingkan kinerja dari dua metode untuk data latih, yaitu train-everything (TEFT) dan train-on-error (TOE). Hasil evaluasi menunjukkan bahwa naïve Bayes multinomial memiliki kinerja lebih baik dibanding versi Graham. Di samping itu, metode data latih menggunakan TEFT dapat meningkatkan akurasi model klasifikasi dibanding metode TOE.</p><p>Kata kunci: filter spam, naïve Bayes, metode training</p>

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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