Identifying Social Media Conversation Topics Regarding Electric Vehicles in Indonesia Using Latent Dirichlet Allocation

Anwar, Muchamad Taufiq; Utami, Mailia Putri; Ambarwati, Laksmi; Arohman, Abdul Wahid

doi:10.1109/cyberneticscom55287.2022.9865493

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…The most recurring theme for participant feedback is "very beneficial", "very helpful", "satisfactory", "thank you", "adding insights", and "more training like this". These results mean that the as Latent Dirichlet Allocation to identify the most recurring topics in the participants' feedback (18).…”

Section: Resultsmentioning

confidence: 81%

Analysing Teacher Training Participants' Feedback Using Natural Language Processing

Nugrohadi¹,

Dula

Anwar

et al. 2022

KSS

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To assess the quality-of-service PGRI Jawa Tengah provided to its community through its teacher training programs, it needed to understand the input from the community itself. However, analysing and gaining insight from thousands of written feedback is hard and tedious. This research aimed to extract information from training participants’ feedback using Natural Language Processing techniques. The result showed that the most recurring theme for participant feedback is “very beneficial”, “very helpful”, “satisfactory”, “thank you”, “adding insights”, and “more training like this”. This means that most participants are satisfied with the training held by PGRI Jawa Tengah and expect more of this training to be held in the future. Based on these findings, PGRI Jawa Tengah should hold more teacher training programs in the future that are similar or greater in quality than the already established training. Keywords: teacher training; participants feedback; text mining; natural language processing

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Section: Resultsmentioning

confidence: 81%