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SOSIALISASI DAN IMPLEMENTASI SISTEM INFORMASI TANGGAP DARURAT BENCANA ALAM KOTA MEDAN Abdul Chaidir Hrp; Rian Farta Wijaya; Sukrianto Sukrianto; Dwika Ardya; Ahmad Helmy; Siti Mentari
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 5 No. 2 (2025): Desember 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i2.5322

Abstract

Abstract: The city of Medan is one of the regions in North Sumatra that is vulnerable to natural disasters such as floods, fires, and extreme weather events. Limited access to accurate information, low community preparedness, and the absence of integrated disaster information systems remain major challenges in disaster risk reduction efforts. This community service activity aims to improve community preparedness and response capacity through the socialization and implementation of a web-based Disaster Response Information System for Medan City. The methods used include problem identification, system development, socialization, training, and system implementation involving community representatives and local stakeholders. The results indicate an increase in community understanding of disaster mitigation and emergency response procedures, as well as the utilization of the information system as a medium for early warning, reporting, and coordination. The implementation of the disaster response information system is proven to support faster information dissemination and strengthen community participation in disaster management efforts.            Keywords: disaster mitigation; information system; community service; disaster response; Medan City  Abstrak: Kota Medan merupakan salah satu wilayah di Provinsi Sumatera Utara yang memiliki tingkat kerawanan tinggi terhadap bencana alam seperti banjir, kebakaran, dan cuaca ekstrem. Keterbatasan akses informasi yang akurat, rendahnya kesiapsiagaan masyarakat, serta belum terintegrasinya sistem informasi kebencanaan menjadi permasalahan utama dalam upaya pengurangan risiko bencana. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kesiapsiagaan dan kapasitas tanggap bencana masyarakat melalui kegiatan sosialisasi dan implementasi Sistem Informasi Tanggap Bencana berbasis web di Kota Medan. Metode pelaksanaan meliputi identifikasi masalah, pengembangan sistem, sosialisasi, pelatihan, dan implementasi sistem dengan melibatkan perwakilan masyarakat dan pemangku kepentingan lokal. Hasil kegiatan menunjukkan adanya peningkatan pemahaman masyarakat terhadap mitigasi bencana dan prosedur tanggap darurat, serta pemanfaatan sistem informasi sebagai media peringatan dini, pelaporan kejadian, dan koordinasi. Implementasi sistem informasi tanggap bencana terbukti mendukung percepatan penyebaran informasi dan memperkuat partisipasi masyarakat dalam penanggulangan bencana. Kata kunci: mitigasi bencana; sistem informasi; pengabdian masyarakat; tanggap bencana; Kota Medan
Sosialisasi Etika Berkomunikasi Digital sebagai Upaya Pencegahan Bullying di Lingkungan SMK Negeri 1 Stabat: Penelitian Dwika Ardya; Muhammad Irfan Sarif; Afif Asri; Dhimas Prayogi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4550

Abstract

The rapid development of digital technology among students brings both positive impacts and challenges, one of which is the increasing risk of digital bullying in school environments due to limited understanding of digital communication ethics. This Community Service Program (PKM) aims to improve students’ understanding of digital communication ethics as an effort to prevent bullying at SMKN 1 Stabat. The methods employed include socialization activities, interactive discussions, and the presentation of case studies related to communication behavior in digital spaces. Evaluation was conducted by comparing students’ levels of understanding before and after the program. The results indicate significant improvements across all assessed aspects. Understanding of digital communication ethics increased from 40% to 85%, knowledge of digital bullying rose from 45% to 90%, and students’ awareness of the impacts of bullying improved from 60% to 88%. These percentage increases demonstrate that the socialization of digital communication ethics is effective in enhancing students’ digital literacy and contributes to the creation of a safe and bullying-free school environment. This program is expected to serve as a sustainable preventive education model within educational institutions.
Analysis and Classification of Emergency Conditions Endangering Humans Based on Operational Data Using Random Forest and Support Vector Machine Methods (Case Study: UPT Basarnas Medan) Dwika Ardya; Muhammad Iqbal; Muhammad Irfan Syarif
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.532

Abstract

Operation Search and Rescue (SAR) in phase DETRESFA demands fast and accurate decision-making because it involves real, life-threatening situations. The Medan Basarnas UPT faces challenges in classifying four main categories of incidents: ship accidents (Y1), accidents requiring special handling (Y2), natural disasters (Y3), and conditions endangering humans (Y4), which have so far been done manually and subjectively. This study aims to build a decision support system based on data collection. machine learning to improve the efficiency of resource deployment through objective classification of emergency conditions. Performance comparisons were conducted between the algorithms Random ForestAnd Support Vector Machine(SVM) based on operational features such asresponse time, number of victims, number of personnel, and distance of the incident. The test results show thatRandom Forestprovides superior performance compared to SVM across all evaluation metrics, with accuracy 86.4%, AUC value 93.9%, F1-score 85.5%, And Matthews Correlation Coefficient (MCC) 0.759. AnalysisConfusion Matrixconfirm that Random Foresthas better stability in recognizing operational feature patterns for most target categories, including its more consistent ability in handling less dominant classes than SVM. Although the Y2 category is still a challenge for both models, Random Forestproven to be much more robust with an accuracy of 49.6% compared to SVM which only achieved 16.5%. This research proves that Random Forestis a more reliable and consistent model to support SAR practitioners in improving the accuracy of field responses, efficiency of resource deployment, and minimizing the risk of loss of life.