Perancangan Digital Enterprise Architect Smart course Pada Industri Pendidikan

Firmansah, R Arif; Indrajir, Richardus Eko; Dazki, Erick

doi:10.33372/stn.v8i2.876

Cited by 1 publication

(1 citation statement)

References 11 publications

(11 reference statements)

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“…Data collection in this study was carried out by collecting tweet set data from Twitter with the help of the Python library, namely Snscrape. Snscrape is commonly used for data mining on social media, including Twitter with certain keyword filters [37]. The keyword used in this study was "BBM naik" and the number of tweets collected was 5,000 data.…”

Section: Data Crawlingmentioning

confidence: 99%

Analyzing Twitter Users’ Sentiments on the Surge of Fuel Oil Prices in Indonesia using the K-Nearest Neighbor Algorithm

Budianti,

Marvyna,

Sukaridhoto

2024

E3S Web of Conf.

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Sentiment analysis offers an effective solution for automating the classification of text data based on polarity, facilitating the assessment of public opinion. Among various social media platforms, Twitter stands out as a significant source of concise textual data reflecting users’ viewpoints on diverse topics. Notably, the recent surge in the price of fuel oil (BBM) in Indonesia has sparked considerable discussion and expression on Twitter. In this study, our objective was to perform a comprehensive sentiment analysis of Twitter users’ reactions to the rising fuel prices in Indonesia by employing the K-Nearest Neighbor (K-NN) algorithm. The research followed a structured approach encompassing data collection, text preprocessing, data labeling, feature extraction, data splitting, classification, and algorithm performance evaluation. The results revealed a dominance of negative sentiments among the 5,000 collected tweet data. The sentiments were categorized as 54.6% negative, 31.8% positive, and 13.6% neutral. This indicates a prevailing level of dissatisfaction and concern expressed by Twitter users regarding the fuel price increase. The K-NN algorithm’s classification performance was most promising when evaluated in an 80:20 data ratio experiment, yielding an accuracy rate of 65%, precision of 74%, recall of 45%, and an error rate of 35%. These findings suggest that the K-NN algorithm is valuable for effectively gauging public sentiment towards the escalating fuel prices in Indonesia. This research highlights the potential of sentiment analysis and the K-NN algorithm in assessing public reactions to significant events, providing valuable insights for policymakers and stakeholders in the energy sector.

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Section: Data Crawlingmentioning

confidence: 99%