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ANALISIS PENGGUNAAN SOSIAL MEDIA DALAM PEMBENTUKAN KARAKTER PESERTA DIDIK DI SEKOLAH DASAR Siswoyo, Andika Adinanda; Pratama Roda’i, Nur Indah Putri Ayu; Ramadhani, Nadia; Zaini, Moh.
Cognitive: Jurnal Pendidikan dan Pembelajaran Vol. 2 No. 3 (2024): Desember
Publisher : CV. Jendela Gagasan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61743/cg.v2i3.81

Abstract

This study aims to analyze the impact of social media use on the character development of students at SDN Bunder 1, Pademawu District, Pamekasan Regency. Using a descriptive qualitative approach, the study involved 26 sixth-grade students as informants. With the rise of social media platforms such as TikTok, YouTube, and Instagram, concerns have emerged regarding their effects on children's behavior and personality. Through qualitative research methods, this study seeks to examine how social media influences students' social interactions and character formation. The findings show that all students use social media, with TikTok being the most popular platform. Social media provides benefits in learning, with students reporting positive effects such as increased self-confidence and ease in making friends. However, challenges also arise as students may imitate negative behaviors they encounter on social media. Therefore, the role of parents and teachers is crucial in supervising and guiding children's use of social media.
Analisis Bibliometrik Global Pemanfaatan Artificial Intelligence Dalam Penelitian Resistensi Antimikroba Periode 2015–2025 Nurrahma, Alfiya Hana; Kusuma, Ikhwan Yuda; Ramadhani, Nadia; Nufus, Ayatun; Saputri, Abelia; Haryanti, Sri; Putri, Renanda Nabila
Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025)
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/snppkm.v4i1.1358

Abstract

Antimicrobial resistance (AMR) is a growing global health threat. Artificial Intelligence (AI) offers innovative approaches to the detection, prediction, and management of resistance through advanced data analysis. This study aims to map the research landscape of AI utilisation in AMR for the period 2015–2025 through bibliometric analysis. A search on PubMed (4 September 2025) yielded 2,078 documents (1996–2025), and after applying year restrictions, 2,009 articles were analysed. The total output of countries reached 11,943 publications, with China (5,570), the United States (2,896), and India (755) as the main contributors. Four thematic clusters were identified: machine learning, bacterial resistance, deep learning, and antimicrobial stewardship. The results showed a sharp increase in publications until 2025, with the journals Antibiotics, Frontiers in Microbiology, and Scientific Reports dominating. The study confirmed the important role of AI in supporting resistance detection and precision medicine, but challenges such as data limitations, algorithmic bias, and global research inequality still require attention.