Levels of Political Participation Based on Naive Bayes Classifier

Hidayatillah, Rumaisah; Mirwan, Mirwan; Hakam, Mohammad; Nugroho, Aryo

doi:10.22146/ijccs.42531

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…In this study, the features used in processing the dataset are TF-IDF and N-Gram. N-Gram is a combination of adjectives that often appear for showing a sentiment [12]. An example of using N-Gram can be seen in Table 1.…”

Section: Feature Selectionmentioning

confidence: 99%

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Isnain

Marga

Alita

2021

Indonesian J. Comput. Cybern. Syst.

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The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

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Section: Feature Selectionmentioning

confidence: 99%

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Isnain

Marga

Alita

2021

Indonesian J. Comput. Cybern. Syst.

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“…This study will use the main dataset from the Ganjar Pranowo campaign in Indonesia. This dataset has been used in previous studies using the Naïve Bayes algorithm [14].…”

Section: Principal Component Analysis Sentiment Analysis Classificationmentioning

confidence: 99%

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Nugroho¹,

Sumpeno²,

Fujisawa³

et al. 2021

IJIES

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Campaigns on online social media are becoming intriguing because all parties can compete with each other. Various content can be created to support or defeat one another. Interaction is important to control the battlefield in the face of disinformation. The campaign team must be able to increase interaction and reporting it as a form of achievement. The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media. This is because there is no standardization in measuring these interactions. This fact inspires to build a model for measuring campaign interaction on social media. This model will be able to help the campaign team. This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America. The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN). The research stages were arranged using the Nassi-Shneiderman Framework. This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner. The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%. Furthermore, at the testing stage using the crossvalidation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset. This classification model has been tested using several datasets from several candidate social media accounts. We also created a dataset campaign interaction. This study shows that the proposed model can outperform previous studies. So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.

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“…Dalam hal ini didukung dengan adanya pemilu guna mencari seorang kepala negara. Di Indonesia, pemilu biasanya diselenggarakan secara serentak lima tahun sekali [1]. Pada tahun 2019 ini dilakukan pemilihan presiden Indonesia.…”

Section: Pendahuluanunclassified

Analisis Sentimen Calon Presiden Indonesia 2019 Berdasarkan Komentar Publik Di Facebook

Santoso¹,

Nugroho²

2019

Jurnal Eksplora Informatika

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Dalam penelitian ini akan dilakukan analisis sentimen terhadap calon presiden Indonesia tahun 2019 berdasarkan komentar publik di jejaring sosial Facebook. Selanjutnya akan melalui beberapa tahapan dalam melaukan analisis sentimen, antara lain adalah tahap pengumpulan data, data correction, preprocessing data, dan klasifikasi menggunakan Naïve Bayes Classifier serta dilakukan asosiasi teks. Hasil dari penelitian ini diperoleh bahwa calon presiden Joko Widodo didapatkan postingan sebanyak 40 data dan calon presiden Prabowo Subianto didapatkan 12 data postingan dengan pengumpulan data pada tanggal 17 april 2019 sampai 22 mei 2019. Dari data sebanyak 5.000 komentar yang dipilih secara acak dan melalui tahap preprocessing menghasilkan polaritas sentimen, Joko Widodo memeperoleh 85% untuk sentimen positif, 15% sentimen negatif. Sedangkan Prabowo Subianto memperoleh 76% sentimen positif, dan 24% sentimen negatif. Untuk hasil klasifikasi menggunakan Naïve Bayes Classifier memperoleh hasil tingkat akurasi sebesar 86,4%, serta kata yang berasosiasi dengan kata masyarakat terhadap Joko Widodo didapatkan kata upaya, mental, dan kondisi untuk sentimen positif dan kata pemerintahan, pembangunan, kelompok untuk sentimen negatif. Sedangkan yang berasosiasi dengan kata masyarakat terhadap Prabowo Subianto didapatkan kata sistem, berkomitmen, dan kritis untuk sentimen positif dan kata bodohi, kelayakan, diusung untuk sentimen negatif.

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Levels of Political Participation Based on Naive Bayes Classifier

Cited by 11 publications

References 16 publications

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Analisis Sentimen Calon Presiden Indonesia 2019 Berdasarkan Komentar Publik Di Facebook

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