Analisis Sentimen Pengguna Twitter Terhadap Perpanjangan PPKM Menggunakan Metode K-Nearest Neighbor

Asro'i, Arief; Februariyanti, Herny

doi:10.31294/jki.v10i1.12624

Cited by 4 publications

(4 citation statements)

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“…This research uses the K-Nearest Neighbor (K-NN) algorithm and tweet data with the keyword "PPKM" collected during a certain period. The model training results achieved an accuracy score of 69.5%, which shows the model's ability to classify Twitter user sentiment towards PPKM (Asro'i & Februariyanti, 2022).Next "Sentiment Analysis on Twitter Social Media Towards Student Academic Information System Services at Universitas Brawijaya with the K-Nearest Neighbor Method". This research focuses on analyzing the sentiment of Twitter users towards the Brawijaya University Student Academic Information System Service (SIAM UB).…”

Section: Literature Review Related Researchmentioning

confidence: 95%

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

Olajuwon,

Kusrini,

Kusnawi

2024

SinkrOn

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This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .

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Section: Literature Review Related Researchmentioning

confidence: 95%

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

Olajuwon,

Kusrini,

Kusnawi

2024

SinkrOn

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“…Pada tahapan preprosessing yaitu proses menyiapkan data yang akan diolah lebih lanjut, tujuan yaitu untuk memastikan kebersihan data dan mengurangi gangguan, dilakukan proses data preprocessing agar data menjadi lebih bersih [12]. Proses data preprocessing memiliki peran penting dalam analisis sentimen terhadap data yang diperoleh dari media sosial.…”

Section: Preprosessingunclassified

Analisis Sentimen Menggunakan Metode Naive Bayes Terhadap Childfree

Agustin,

Sa'adah,

Umaidah

2024

dinamik

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Childfree terjadi pada individu atau pasangan yang secara sukarela memilih untuk tidak memiliki anak. Fenomena Childfree semakin menarik perhatian masyarakat modern dan mempengaruhi keputusan hidup banyak individu. Data yang digunakan diperoleh dengan melakukan crawling data pada sosial media twitter. Dalam analisis sentimen kali ini menggunakan metode naïve bayes, data akan diflasifikasikan menjadi dua hasil, yaitu sentimen negatif dan positif. Setelah itu, data tersebut akan dinilai menggunakan pengujian dengan confusion matrix untuk mengukur akurasi, recall, dan precision. Hasil pengujian dengan Rapidminer menunjukan tingkat akurasi naive bayes yang tinggi sebesar 96%, untuk sentimen positif menghasilkan nilai precision 84,13%, dan nilai recall 100%. Sedangkan untuk sentimen negatif menunjukkan nilai precision 94,92%, dan recall 100%. Hasil klasifikasi tersebut menghasilkan 254 tweet negatif, dan 74 tweet positif. Analisis sentimen menggunakan metode Naïve Bayes dapat memberikan wawasan yang berharga tentang pandangan dan sentimen masyarakat terhadap Childfree. Hasil penelitian ini penting untuk memahami dan menghargai keragaman pandangan terkait topik ini. Implikasi penelitian ini dapat digunakan untuk pengambilan keputusan yang lebih baik dalam konteks sosial, budaya, dan kebijakan yang berkaitan dengan keputusan Childfree.

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“…He is a lecturer who teaches at 3 (three) higher education institutions in Central Java. Labeling is done by human labor with the assumption that only living things can feel emotions or sentiments, while machines do not (Asro'i & Februariyanti, 2022). Categories or classes in data labeling consist of positive, negative, and neutral classes.…”

Section: Data Labelingmentioning

confidence: 99%

Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java

Albab,

Utami,

Ariatmanto

2023

SinkrOn

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Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.

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Analisis Sentimen Pengguna Twitter Terhadap Perpanjangan PPKM Menggunakan Metode K-Nearest Neighbor

Cited by 4 publications

References 0 publications

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting

Analisis Sentimen Menggunakan Metode Naive Bayes Terhadap Childfree

Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java

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