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Membangun Solidaritas melalui Turnamen Esports Mobile Legends di Komunitas Gaming Pedesaan Fitrianto, Rizal Akbar; Editya, Arda Surya; Husaini, Arinda Putri; Ekavanda, Wirahandy; Ferdyansyah, Moch. Arief
Nusantara Community Empowerment Review Vol. 2 No. 2 (2024): Nusantara Community Empowerment Review
Publisher : LPPM UNUSIDA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/ncer.v2i2.1328

Abstract

Turnamen Esports Mobile Legends di komunitas pedesaan menunjukkan potensi besar dalam membangun solidaritas dan semangat kompetitif. Penelitian ini bertujuan untuk mengeksplorasi dampak turnamen terhadap komunitas gaming pedesaan. Peserta terbatas dari kalangan pelajar SD-SMP, kompetisi ini menyoroti kolaborasi antargenerasi, potensi esports dalam mempersatukan komunitas, serta kebutuhan akan pengembangan yang lebih luas untuk mencapai dampak yang lebih besar. Penelitian ini bertujuan untuk mengeksplorasi dampak turnamen terhadap komunitas gaming pedesaan. Metode penelitian melibatkan observasi langsung dan wawancara dengan peserta serta penyelenggara turnamen. Hasil menunjukkan peningkatan interaksi sosial dan motivasi antar generasi di desa. Mobile Legends Esports tournaments in rural communities show great potential in building solidarity and competitive spirit. This research aims to explore the impact of tournaments on rural gaming communities. Limited to elementary and middle school students, this competition highlights intergenerational collaboration, the potential of esports in uniting communities, and the need for broader development to achieve greater impact. This research aims to explore the impact of tournaments on rural gaming communities. The research method involves direct observation and interviews with participants and tournament organizers. The results show increased social interaction and motivation between generations in the village.
Forensic Analysis of Drones Attacker Detection Using Deep Learning Editya, Arda Surya; Kurniati, Neny; Alamin, Mochammad Machlul; Pramana, Anggay Luri; Lisdiyanto, Angga
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48183

Abstract

Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.