DBSCAN algorithm: twitter text clustering of trend topic pilkada pekanbaru

Abstract

Social media is one of the most common sources used to communicate, such as Twitter. Every tweet on Twitter contains data such as text which when collected can be processed into information. Data processed from Twitter tweet will create a trend which can be used for information such as in education, economics, politics, etc. This then created the concept of text mining. Text mining techniques are needed to find an interesting pattern in search of trends based on Twitter text with topics related to Pilkada Pekanbaru 2017. This research is intended to cluster Twitter text data using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. This research was conducted with several experiments using different Eps and MinPts parameters for 2,184 text data which has been through several stages, such as cleaning, duplication removal, pre-processing like stemming and stopwords. Based on the highest average of Silhouette Index, Eps 0.1 and MinPts 10 with SI = 0.413 were chosen as paramaters, thus forming 31 clusters. According to the frequency of word occurrences in the cluster, the highest are “kpu”, followed by “firdaus”, “kota”, “pasang”, and “ayat”. As can be seen that the candidate pairs most often appear on cluster results are Firdaus-Ayat, and based on the results of Pilkada 2017, Firdaus-Ayat was chosen as Mayor and Vice Mayor of Pekanbaru.

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

“…However, it models only those topic labels that are based on Wikipedia articles. Capdevila et al (2017) developed Tweet-SCAN, which is based on DBSCAN and used a hierarchical Dirichlet process and Jensen-Shannon distance for event discovery from Mustakim et al (2019) employed DBSCAN algorithm for clustering of trending tweet topic pilkada pekanbaru. However, DBSCAN has limitations in handling a large number of sparse texts.…”

“…Table 11 shows the details of these datasets. (Korshunova et al 2019;Belford et al 2016;Kumar and Vardhan 2019;Ni et al 2018;Yan et al 2012;Fang et al 2017;Fang et al 2014;Muliawati and Murfi 2017;Prakoso et al 2018;Pornwattanavichai et al 2020;Capdevila et al 2017;Mustakim et al 2019;Zhang and Zhang 2020;Ali and Balakrishnan 2021;Abdulwahab et al 2022;Yu et al 2017;Indra and Pulungan 2019;Curiskis et al 2020;He et al 2020b;Fang et al 2017;Karami et al 2018Wandabwa et al 2021…”

Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve bayes dan Decission Tree

“…Teknik pre-processing memiliki pengaruh yang signifikan terhadap performa algoritma pembelajaran mesin. Perancangan database dan analisis yang baik dapat mengurangi masalah kehilangan data melalui processing [4].…”

“…Informasi vaksinasi serta tata cara pencegahan virus ini telah tersebar diberbagai media sosial [3]. Media sosial ialah salah satu sumber yang sangat umum digunakan untuk berkomunikasi, berbagi dokumen serta data dengan jumlah komunitas yang besar [4]. Salah satu media sosial tersebut yakni Facebook yang di dalamnya ada terdapat informasi yang sangat berharga sebagai alat penentu kebijakan dengan jumlah opini yang besar [5].…”

Mini-Batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

“…means) and (mini-batch k-means) (Feizollah et al, 2014), density-based (DBSCAN) (Indah et al, 2019), hierarchical (network analysis map) (Chua & Nohuddin, 2017), and grid-based (STING) (Han et al, 2012).…”