scholarly journals Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram

2018 ◽  
Vol 2 (2) ◽  
pp. 200
Author(s):  
Agung Nugroho
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
N Gram ◽  
Naive Bayes Method

Social media is currently an online media that is widely accessed in the world. Microblogging services such as Twitter allow users to write about various things they experience or write reviews of a product, service, public figures and so on. This can be used to take opinion or sentiment towards an entity that is being discussed on social media such as Twitter. This study utilizes these data to determine public opinion or sentiment regarding public perceptions of the issue of rising electricity tariffs. Opinion taking is based on three classes namely positive, negative and neutral. Users often use non-standard word abbreviations or spelling, this can complicate the process and accuracy of classification results. In this study the authors apply text-preprocessing in handling these problems. For feature extraction, n-gram and classification methods are used using the Naive Bayes classifier. From the results of the research that has been done, the most negative sentiments are formed in response to the issue of the increase in basic electricity tariffs. In addition, from the results of testing with the method of cross validation and confusion matrix it is known that the accuracy of the naïve Bayes method reaches 89.67% before applying n-gram, and the accuracy rate increases 2.33% after applying n-gram characters to 92.00%. It is proven that the application of the n-gram extraction feature can increase the accuracy of the naïve Bayes method.

scholarly journals PENERAPAN DATA MINING DENGAN ALGORITMA NAIVE BAYES CLASSIFIER DALAM MEMPREDIKSI PEMBELIAN CAT

2021 ◽  
Vol 9 (01) ◽  
pp. 19-23
Author(s):  
Fitriana Harahap ◽  
Nidia Enjelita Saragih ◽  
Elida Tuti Siregar ◽  
Husin Sariangsah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Sales Manager ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
Paint Product ◽  
Product Sales ◽  
Naive Bayes Method

Companies need several types of communication technology that can predict customer purchase interest, the goal is that the company can properly consider product sales and determine the company's paint product supply. So far, the decision of the Home Smart sales manager has been made by looking at the closeness of the supplier relationship and how many sponsors are funding the company. So that sometimes the product cannot compete with other companies. The Naive Bayes classifier algorithm is one of the algorithms included in the classification technology. The application of the Naive Bayes method is expected to predict paint purchases from suppliers. From 60 paint purchase data tested with the Naive Bayes method, the results reached 80% of the accuracy of the predictions. Of the 60 tested paint purchase data, 48 paint purchase data were successfully classified correctly.

scholarly journals COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY

2020 ◽  
Vol 17 (1) ◽  
pp. 37-42
Author(s):  
Yuris Alkhalifi ◽  
Ainun Zumarniansyah ◽  
Rian Ardianto ◽  
Nila Hardi ◽  
Annisa Elfina Augustia
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Total Sample ◽  
Naïve Bayes ◽  
Food Assistance ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

scholarly journals Klasifikasi sinopsis novel menggunakan metode naïve bayes classifier

Repositor ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 125
Author(s):  
Vinna Rahmayanti ◽  
Setio Basuki ◽  
Hilman Hilman
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Training Data ◽  
The Novel ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Romantic Comedy ◽  
Document Frequency

It is undeniable that technological progress is developing very quickly in the field of computers, now with computers the work that was originally done by humans can be taken over by computers to help human work itself, like case studi of this research is a system that can classification the text like synopsis into genre group. Genre is the style of story in a novel, there are many genres in the novel that are expected to be romantic, comedy, mystery, horror and others, by knowing the genre of the novel the reader will be able to know the story style of the novel. The method used in this research is TF-IDF (Term Frequency Inverse Document Frequency) and Naïve Bayes Classifier. The TF-IDF method is used to get the weight of each word contained in the resulting document is used in the Naïve Bayes Classifier method to get the synopsis classification results into genre. Based on the evaluation using a confusion matrix using 600 training data and 200 test data obtained an accuracy of 80.5%.AbstractIt is undeniable that technological progress is developing very quickly in the field of computers, now with computers the work that was originally done by humans can be taken over by computers to help human work itself, like case studi of this research is a system that can classification the text like synopsis into genre group. Genre is the style of story in a novel, there are many genres in the novel that are expected to be romantic, comedy, mystery, horror and others, by knowing the genre of the novel the reader will be able to know the story style of the novel. The method used in this research is TF-IDF (Term Frequency Inverse Document Frequency) and Naïve Bayes Classifier. The TF-IDF method is used to get the weight of each word contained in the resulting document is used in the Naïve Bayes Classifier method to get the synopsis classification results into genre. Based on the evaluation using a confusion matrix using 600 training data and 200 test data obtained an accuracy of 80.5%.

scholarly journals IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN CONFUSION MATRIX DALAM ANALISIS SENTIMEN TERHADAP PELAYANAN TRANSPORTASI UMUM SELAMA PANDEMI COVID-19 PADA MEDIA SOSIAL TWITTER

2020 ◽  
Vol 8 (1) ◽  
pp. 64-75
Author(s):  
Riskania Riskania ◽  
Farid Thalib
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Pandemi COVID-19 memberikan dampak diberbagai aspek. Salah satu yang terkena dampak adalah transportasi umum. Transportasi umum mengalami penurunan jumlah penumpang yang signifikan, seperti Transjakarta sebesar 34,52%, MRT  94,11%  dan KRL 78,69%. Penurunan ini disebabkan oleh kebijakan yang dikeluarkan untuk mendukung upaya pemerintah dalam pencegahan penyebaran virus Covid-19, seperti memangkas jam operasional, mengurangi perjalanan yang akan dijadwalkan sampai pembatasan penumpang setiap gerbong. Kebijakan ini memicu opini penumpang mengenai pelayanan yang diberikan. Opini tersebut dapat dituangkan melalui berbagai media salah satunya Twitter. Opini penumpang yang tertuang didalam twitter mengenai pelayanan transportasi umum dapat bersifat positif atau pun negatif. Opini penumpang dapat digunakan sebagai data dalam melakukan analisis sentimen, data ini dapat diperoleh dengan menggunakan teknik crawling. Analisis sentimen dilakukan untuk mengetahui kecenderungan opini penumpang mengenai pelayanan transportasi umum selama pandemi Covid-19. Data yang didapatkan sebanyak 650 data yang diberikan label positif dan negatif. Data dibagi menjadi data latih sebanyak 60 % atau 390 data, dan data uji 40% atau 260 data. Data ini dapat digunakan untuk proses pembuatan model mechine learning menggunakan Metode algoritma Naïve Bayes Classifier. Hasil pembentukan model mechine learning ini memiliki tingkat akurasi sebesar 83,8%  yang dihasilkan dari pengujian data uji dengan menggunakan confusion matrix.

scholarly journals Pemanfaatan Big Data Media Sosial Dalam Menganalisa Kemenangan Pilkada

2019 ◽  
Vol 18 (1) ◽  
pp. 101
Author(s):  
Dewa Ayu Putri Wulandari ◽  
Made Sudarma ◽  
Nyoman Paramaita
Keyword(s):  
Big Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
N Gram

Pemilihan Calon Gubernur  dan  Wakil  Gubernur Bali 2018 akan  melalui  beberapa  tahapan  pemilu  mulai  dari penentuan  bakal  calon  Gubernur  dan  Wakil  Gubernur  Bali hingga tahapan penghitungan suara. Dalam pemilihan Gubernur dan  Wakil  Gubernur  Bali  masyarakat  dapat  terlibat  langsung dalam tahapan pemungutan suara yang akan dilaksanakan pada tanggal 27 Juni 2018 (KPU, 2018). Sehingga dapat memunculkan banyak  komentar  atau  pendapat,  tidak  hanya  komentar  positif dan   netral   tapi   juga   komentar   yang   negatif.   Penelitian   ini diharapkan   mampu   untuk   melakukan   riset   atas   komentar masyarakat  yang  mengandung  sentimen  baik  atau  positif,  sama sekali tidak mengandung senrimen atau netral dan mengandung sentimen   buruk   atau   negatif. Dalam   penelitian   ini   metode digunakan untuk preprocessingdata menggunakan tokenisasi N-gram.  N-gram  adalah  token  yang  terdiri  dari  tiga  kata  setiap satu token. Pada  tahap  stemming  menggunakan algoritma  Nzief Adriani.   Untuk   proses   klasifikasinya   menggunakan   metode Naïve   Bayes   Classifier (NBC).Pada   pengujian   data   calon Gubernur  akurasi tertinggi diperoleh  dari  klasifikasi  data KBS-Ace  pada  data  yang  diambil  dari  Twitter  dengan  nilai  akurasi 89%, presisi  91%  dan  recall  94%  dan  akurasi  terendah  pada saat proses kalsifikasi data KBS-Ace pada media sosial Facebook. Kata  Kunci—Analisa  Sentimen,  Calon  Gubernur  Bali  2018, Naive Bayes Classifier

Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

2020 ◽  
Vol 2 (1) ◽  
pp. 22
Author(s):  
Mohammad Zoqi Sarwani ◽  
Muhammad Shubkhan Salafudin ◽  
Dian Ahkam Sani
Keyword(s):  
Social Media ◽  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Surrounding Environment ◽  
Testing Data

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.

IMPLEMENTASI LEXICON BASED DAN NAIVE BAYES PADA ANALISIS SENTIMEN PENGGUNA TWITTER TOPIK PEMILIHAN PRESIDEN 2019

2019 ◽  
Vol 24 (2) ◽  
pp. 140-153
Author(s):  
Gusti Nur Aulia ◽  
Eka Patriya
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Web Server ◽  
Data Preprocessing ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Data Filtering ◽  
Naïve Bayes Classifier ◽  
Twitter Data

Pilpres saat ini cukup menyita perhatian, karena berbagai rumor yang beredar. Masyarakat juga menjadi sasaran elit politik, dimana suara mereka merupakan penentu keberlangsungan arah politik untuk lima tahun kedepan. Opini-opini positif, netral maupun negatif dapat menimbulkan ancaman munculnya berita bohong (hoax). Salah satu sarana yang digunakan masyarakat dalam mengekspresikan pilihan politiknya adalah melalui media sosial salah satunya twitter. Data seperti opini publik dapat diolah menjadi sebuah informasi yang bermanfaat, salah satunya melalui analisis sentimen. Pada penelitian ini, akan dilakukan analisis sentimen pada Twitter tentang pemilihan presiden 2019. Tahapan analisis sentimen pada penelitian ini terdiri dari akuisisi data, pre-processing, klasifikasi data, evaluasi data dan visualisasi data. Preprocessing dilakukan dengan case folding, normalisasi data, filtering, ubah kata baku, stopword dan stemming. Penelitian ini melakukan 2 metode yaitu dengan metode Lexicon Based dan Naïve Bayes Classifier. Hasil akhir dari analisis kemudian dihitung nilai akurasi menggunakan confusion matrix dan di visualisasikan menggunakan web server. Penentuan sentimen prediksi dilakukan menggunakan metode Lexicon Based dan Labelisasi dengan perhitungan secara manual. Data latih dan data uji akan digunakan dalam proses pelatihan dan pengujian menggunakan Naive Bayes Classifier. Hasil klasifikasi yang dilakukan oleh metode Naive Bayes Classifier disebut sentimen aktual. Perhitungan tingkat keakurasian antara sentimen prediksi terhadap sentimen aktual menggunakan pengujian confusion matrix. Hasil yang didapatkan adalah tingkat akurasi antara sentimen prediksi dan sentimen aktual dengan Lexicon Based sebesar 64,49% pada data uji dan pada data latih sebanyak 94,2% serta dengan menggunakan Labelisasi dan Naive Bayes Classifier sebesar 86,53% pada data uji dan data latih sebesar 94,08%. Hasil penelitian ini diharapkan dapat membantu melakukan riset atas opini masyarakat pada Twitter mengenai Pilpres 2019 yang mengandung sentimen positif, negatif atau netral.

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