Implementasi Sentimen Analisis Komentar Channel Video Pelayanan Pemerintah Di Youtube Menggunakan Algoritma Naïve Bayes

Saputra, Pramana Yoga; Subhi, Dian Hanifudin; Winatama, Fahmi Zain Afif

doi:10.33795/jip.v5i4.259

Cited by 10 publications

(11 citation statements)

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“…A high accuracy value of classification meets value of 100%. The accuracy value of previous studies on text classification was above 60% in average [8] [12]. Through applying the combination of preprocessing techniques, it is expected that it can result a classification model with a higher accuracy.…”

Section: Introductionmentioning

confidence: 91%

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The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Saputro

Hermawan

2021

IJCIT

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Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model.

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Section: Introductionmentioning

confidence: 91%

“…This research implemented sentiment analysis on comments on YouTube [8]. It used Naïve Bayes Classifier (NBC) method to analyze the sentiment on government instances.…”

Section: Related Workmentioning

confidence: 99%

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Saputro

Hermawan

2021

IJCIT

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“…Research that has been done related to decision support systems, among others. Research on the Implementation of Sentiment Analysis of Government Service Video Channel Comments on Youtube Using the Naïve Bayes Algorithm Method conducted (Saputra et al, 2019) using 3 criteria, namely positive, negative and neutral. This research produces an application that can provide information on how to measure the level of public satisfaction with government services through Yo uTube.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies

Udayana¹,

Indrawan²,

Putra³

2023

CNAHPC

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Sentiment analysis is the process of classifying a text dataset as positive, negative or neutral. Youtube is one of the popular media used to provide responses to a problem. In the Jokowi era, infrastructure development was carried out massively and evenly, one of which was in Bali Province, namely the construction of the Mengwi-Gilimanuk Toll Road. The construction of the Mengwi-Gilimanuk Toll Road consumed a lot of people's agricultural land, which resulted in various pro and con responses from the community. From these problems, sentiment analysis is carried out to get community reviews related to the object being analyzed by utilizing algorithms to be able to classify opinions, in the construction of this system the naïve bayes algorithm is used with testing methods namely accuracy, precision, and recall. From the sentiment analysis conducted by utilizing 18 video links on YouTube with 701 comments, it produces positive sentiment as much as 50.64%, negative sentiment as much as 7.70% and neutral sentiment as much as 39.23%.

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“…YouTube menarik pengguna dengan memanfaatkan kekurangan Televisi(TV), yaitu dengan iklan yang sedikit, durasi video yang bervariasi dan YouTube menyajikan berbagai video yang dapat di pilih pengguna [6]. Dengan adanya aplikasi ini, pemberian informasi dapat menyebar dengan lebih luas dan cepat, dan pengguna dapat mengekspresikan pendapatnya dalam bentuk komentar [7] Pada kolom komentar ini masyarakat dapat dengan bebas berpendapat. Pada penelitin ini didapatkan data komentar sebanyak 4783 data komentar.…”

Section: Pendahuluanunclassified

Implementation of the Naive Bayes Classifier Algorithm for Classification of Community Sentiment About Depression on Youtube

Mulyani¹,

Novita²

2022

J. Tek. Inform. (JUTIF)

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Depression is a disease that knows no age, gender and social status. WHO states that more than 264 million people suffer from depression, people with depression will continue to grow if public knowledge about mental health is still low, especially in Indonesia. This can be known from the way the community responds to a case. This study aims to determine public sentiment towards people with depression by classifying comments using the Niave Bayes Classifier (NBC) algorithm and adding the Term Frequency-inverse Document Frequency (TF-IDF) method as a feature extraction method. Sentiment used as data is obtained from YouTube comments on several news media accounts such as tvOneNews, Kompas TV, Tribunnews, Official iNews, VIVACOID, CNN Indonesia and Tribun Jateng, so that 4783 data are obtained with training data of 3826 and 957 testing data. This sentiment was analyzed by giving three classes, namely positive, neutral and negative. The results of the sentiment analysis were dominated by positive sentiment of 93.31%, followed by negative comments of 6.68% while neutral sentiment was 0%, and the accuracy of the NBC Algorithm was 84.11%.

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Implementasi Sentimen Analisis Komentar Channel Video Pelayanan Pemerintah Di Youtube Menggunakan Algoritma Naïve Bayes

Cited by 10 publications

References 0 publications

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies

Implementation of the Naive Bayes Classifier Algorithm for Classification of Community Sentiment About Depression on Youtube

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