Kenduri: Jurnal Pengabdian Dan Pemberdayaan Masyarakat
Vol. 6 No. 2 (2026): May-August

Behind The Trend: Analisis Tren dan Sentimen Publik Terhadap Brainrot Content di Media Sosial Menggunakan Python dan Google Colab

Indri Surya Ningsih (Universitas Muhammadiyah Jakarta)
Hafizh Umar Haq (Universitas Muhammadiyah Jakarta)
Muhammad Ravlyansyah (Universitas Muhammadiyah Jakarta)
Lantip Nurrohman (Universitas Muhammadiyah Jakarta)
Muhammad Naufal Razani (Universitas Muhammadiyah Jakarta)
Syamil Ghufron Rabbani (Universitas Muhammadiyah Jakarta)
Sitti Nurbaya Ambo (Universitas Muhammadiyah Jakarta)
Jumail Jumail (Universitas Muhammadiyah Jakarta)
Nurvelly Rosanti (Universitas Muhammadiyah Jakarta)
Yana Adharani (Universitas Muhammadiyah Jakarta)
Rully Mujiastuti (Universitas Muhammadiyah Jakarta)
Popy Meilina (Universitas Muhammadiyah Jakarta)



Article Info

Publish Date
23 Aug 2026

Abstract

Brainrot content, short-form social media content characterized by low-quality yet highly addictive information, has grown rapidly among younger audiences and is associated with declining attention span and mental well-being. This community service activity aims to improve students' digital literacy and analytical skills in reading public trends and sentiment toward brainrot content through a webinar and workshop titled "Behind The Trend". The activity was held online via Zoom Meeting on 26 June 2026, featuring two speakers from the Dicoding community (DBS Foundation and PIJAK). The method consisted of a conceptual webinar followed by a hands-on workshop covering YouTube comment web scraping, data cleaning, text preprocessing, N-Gram and WordCloud generation, and lexicon-based sentiment analysis using Python on Google Colab. Evaluation was conducted through Google Form-based pre-test and post-test. The pre-test involving 18 respondents recorded an average correctness of 81.1%, while the post-test involving 23 respondents showed an increase with an average score of 95.2 out of 100. The activity was attended by 53 registered participants from various universities. These results indicate that a viral-trend case-study training approach effectively improves participants' understanding of data scraping, text mining, and sentiment analysis concepts.

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Journal Info

Abbrev

kenduri

Publisher

Subject

Humanities Environmental Science Social Sciences

Description

The journal includes, but is not limited to the following fields: Teaching & Learning in Science Education Material Learning in Science Education Learning Media/Multimedia in Science Education Evaluation & Assessment in Science Education Higher Order Thinking Skills in Science Education Science, ...