Sentimen Analisis Twitter Terhadap Penyelenggaraan Gojek Traveloka Liga 1 Indonesia

Setyobudi, Wahyu; Alwi, Aslan; Astuti, Indah Puji

doi:10.24269/jkt.v2i1.68

Cited by 7 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sosial media bukan hanya untuk mencari teman atau sharing tapi dapat dimanfaatkan untuk melakukan promo dagang atau jual beli sampai dengan promo partai politik (Buntoro, 2017). Data teks yang berkembang di twitter pada umumnya merupakan hasil presentasi suatu hal yang sedang berkembang pada masyarakat (Setyobudi et al, 2018). Dengan begitu trending topic pada twitter merupakan cerminan dari trend yang sedang berkembang di masyarakat.…”

Section: Pendahuluanunclassified

Penerapan Algoritma Support Vector Machine Pada Analisis Sentimen Hashtag Twitter

Umar

Sunardi²,

Ardhiansyah³

2022

Jur. Ris. Kom.

View full text Add to dashboard Cite

The development of the creative industry in Indonesia is marked by the emergence of content creators such as YouTubers and celebrities. With the emergence of content creators, people in these professions must be more creative and come up with new things in accordance with community trends in accordance with applicable laws in Indonesia. Twitter is one of the social media that can be used to share online and provide information in accordance with the prevailing trends in society. Hashtags or hashtags on Twitter are often used by users to add comments so that when the hashtag is used a lot it will become a trending topic. By analyzing sentiment on trending topics, positive and negative tendencies will be obtained that can help the content creator profession to create content. This study uses the Support Vector Machine (SVM) method to conduct a sentiment analysis process about the twitter hashtag. The data used is hashtag data on twitter. The data that has been obtained is then carried out by text preprocessing, then weighting, and finally sentiment analysis using SVM. This study uses tweet data taken on October 29, 2022 as many as 21 data, namely “BeliPulsaDiGrab” which has two sentiment classes, namely positive sentiment as much as 99% and negative 1% data

show abstract

Section: Pendahuluanunclassified

Penerapan Algoritma Support Vector Machine Pada Analisis Sentimen Hashtag Twitter

Umar

Sunardi²,

Ardhiansyah³

2022

Jur. Ris. Kom.

View full text Add to dashboard Cite

show abstract

“…Beberapa penelitian terdahulu juga melakukan analisis sentimen dengan mengulas hal-hal sebagai berikt : komparasi fitur seleksi [111]; kinerja KPU Pemilu [112]; kepuasan pengguna penyedia layanan komunikasi seluler [113]; kepuasan dalam proses pendidikan [114]; kehadiran Mass Rapid Transit (MRT) [115]; kebijakan pemindahan Ibukota Republik Indonesia [1], [116], [117]; kebijakan ganjil genap [118]- [120]; jasa ojek online [121], [122]; jasa ekspedisi barang [123]; impor beras [124]; hotel [125]; ujaran kebencian [6]; evaluasi kinerja dosen [126], [127]; Dewan Perwakilan Rakyat [128]; debat calon presiden [129]; debat calon gubernur [130]; dataset Indonesia [131]; cyberbullying [132]; calon gubernur [133]- [136]; berita kebakaran hutan [137]; asuransi [138]; acara televisi [139], [140]. Berdasarkan kuantitas riset, dapat diketahui bahwa ulasan sentimen analisis dengan topik kebijakan pemindahan ibukota Republik Indonesia, kebijakan ganjil genap, jasa ojek online, evaluasi kinerja dosen, calon gubernur dan acara televisi.…”

Section: Gambar 2 Hasil Klasifikasi Studi Kasus Kajian Analisis Sentimen Sumber : Hasil Penyandian Axial Menggunakan Nvivo 12 Plusunclassified

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Singgalen

2021

Journal-ISI

View full text Add to dashboard Cite

This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

show abstract

“…After all words are scored, the total score will be calculated. A post gets a positive label if the total score is greater than 0, a negative label if it is less than 0, and a neutral label if equal to 0 (Setyobudi et al, 2018). Some examples of positive, negative, and neutral content are shown in Table 2, Table 3, and Table 4 respectively…”

Section: Sentiment Analysis On Posts Contentmentioning

confidence: 99%

Sentiment Analysis of Covid-19 Vaccination Posts on Facebook in Indonesia With Crowdtangle

Rianto

Pratama

2021

JRisInf

View full text Add to dashboard Cite

COVID-19 was declared a pandemic by the World Health Organization (WHO) due to the virus's uncontrolled spread around the world, including in Indonesia. The Indonesian government has made a number of steps to stop COVID-19 from spreading, one of which is COVID-19 vaccinations. However, not everyone thinks the vaccination is a good idea. Just like in many other countries, Indonesian people responded in different ways to COVID-19 vaccination posts on social media, be it from government officials/agencies or news portals. Their responses can be used to help the government decide on a better vaccination strategy that will help minimize the virus spread and end the pandemic in Indonesia. Using the lexicon method in determining the content sentiment in COVID-19 vaccination posts on Facebook, this research found that unlike news portals that tend to post a more balanced content (36% positive, 20% negative, and 44% neutral out of 23,623 posts with min score = -19, max score = 24, mean score = 0.25, and SD = = 1.43), government accounts posted much more positive content, both in terms of quality (min score = -15, max score = 40, mean score = 4.16, SD = 6.76 ) and quantity (69% classified as positive) than they did the neutral (15%) and the negative content (16%) out of 723 posts. Subsequent analysis with Two-Way ANOVA tests discovered that COVID-19 vaccination posts by the news portal accounts can elicit more varied reactions from the public than government accounts that tended to elicit only positive reactions. It is also confirmed that both the content sentiment of COVID-19 vaccination posts in Indonesia and the account types making the posts as well as their interaction terms do have an impact on how the public responses to them.

show abstract

Sentimen Analisis Twitter Terhadap Penyelenggaraan Gojek Traveloka Liga 1 Indonesia

Cited by 7 publications

References 0 publications

Penerapan Algoritma Support Vector Machine Pada Analisis Sentimen Hashtag Twitter

Penerapan Algoritma Support Vector Machine Pada Analisis Sentimen Hashtag Twitter

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Sentiment Analysis of Covid-19 Vaccination Posts on Facebook in Indonesia With Crowdtangle

Contact Info

Product

Resources

About