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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Prosiding Konferensi Ilmiah Dasar
Ferrinda Prafitasari
UNIVERSITAS VETERAN BANGUN NUSANTARA
Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pencegahan Perundungan dan Kekerasan Seksual di Lingkungan Sekolah Dasar: Pengabdian Mutiara Dana Elita; Geby Adellestia; I Made Ratih Rosanawati; Ferrinda Prafitasari; Rifqi Nur Cholis; Fuqohak Asmar
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 April - Juni
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.6364

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan, kesadaran, dan keterampilan praktis peserta dalam mengidentifikasi, mencegah, dan merespons kasus perundungan serta kekerasan seksual di lingkungan sekolah dan mengidentifikasi strategi yang efektif dalam menciptakan lingkungan belajar yang aman bagi peserta didik. Pengabdian ini menggunakan metode penelitian tindakan (action research). Subjek pengabdian ini meliputi guru dan karyawan di sekolah dasar. Hasil kegiatan menunjukkan peningkatan dalam pemahaman peserta mengenai definisi, bentuk-bentuk dan dampak perundungan serta kekerasan seksual peningkatan kesiapan dalam menerapkan prosedur penanganan kasus di lapangan. Kendala yang ditemukan meliputi resistensi psikologis terhadap topik kekerasan seksual yang masih dianggap tabu, kesenjangan persepsi mengenai batasan disiplin dan kekerasan, dan perbedaan latar belakang peserta. Kendala tersebut diatasi melalui pendekatan komunikatif berbasis regulasi, diferensiasi metode penyampaian, serta rekomendasi program pelatihan berkelanjutan kepada pihak sekolah. Kegiatan ini menegaskan bahwa penguatan kapasitas guru dan karyawan sekolah merupakan investasi strategis yang paling mendasar dalam membangun ekosistem sekolah yang aman dari segala bentuk kekerasan.
Tren Penelitian Deep Learning pada Platform Media Sosial: Systematic Literature Review Tahun 2021–2026 Ferrinda Prafitasari; Meidawati Suswandari; Ratna Anggita Sari; Lunggani Kartika Dewi; Nurratri Kurnia Sari
Prosiding Konferensi Ilmiah Dasar Vol 7 (2026): Pendekatan Deep Learning dan Pemanfaatan Artificial Intelligence dalam Pendidikan Dasa
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of social media has generated massive and complex amounts of data, creating the need for more effective analytical methods. Deep learning has emerged as one of the approaches capable of automatically identifying patterns within such data; however, systematic studies that comprehensively map its development across social media platforms remain limited. This study aims to analyze research trends in deep learning on social media platforms during the period of 2021–2026 using the Systematic Literature Review (SLR) approach. The analysis focuses on annual publication trends, the most frequently studied social media platforms, the deep learning models employed, dominant research topics, and future research opportunities. Data were collected through a literature search of scientific articles in the Google Scholar and Garuda databases based on predefined inclusion and exclusion criteria, resulting in nine eligible articles for analysis. The findings indicate that research on the application of deep learning in social media has shown significant advancements in model architectures, ranging from single models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) to hybrid models and Transformer-based architectures, including BERT-LSTM and IndoBERT. These approaches have been predominantly applied to sentimen analysis, fake news detection, and hate speech detection. The findings further reveal that CNN remained the most widely used model during the 2021–2026 period, although a gradual shift toward Transformer-based models such as BERT and IndoBERT has begun to emerge. This trend highlights the potential of Transformer-based architectures to improve semantic context understanding and multimedia data processing in future research.
Co-Authors Fuqohak Asmar Geby Adellestia I Made Ratih Rosanawati Lunggani Kartika Dewi Meidawati Suswandari, Meidawati Mutiara Dana Elita Nurratri Kurnia Sari Ratna Anggita Sari Rifqi Nur Cholis