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All Journal JURTEKSI Madani: Multidisciplinary Scientific Journal
Desmarini, Mutia
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Pemanfaatan Robot Berbasis AI untuk Pemantauan dan Respon Bencana Alam Manisa, Bunga Nurul; Desmarini, Mutia; Lubis, M. Luthfi; Rahman, Aulia; Pratama, Putra; Akbar, M. Rabbani
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 1 (2025): February
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

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This research discusses the utilization of artificial intelligence (AI)-based robots in monitoring and responding to natural disasters in Indonesia. Disasters such as earthquakes, floods, and forest fires demand rapid and effective handling, yet they are often hindered by difficult terrain and safety risks. Robotics and AI emerge as innovative solutions, enabling automated navigation, object detection, and real-time data collection in hazardous environments. This study aims to explore the use of AI robots in disaster management, employing a literature review methodology that identifies the challenges and opportunities in their application. The results indicate that robots can enhance the efficiency and safety of rescue teams, although there are still challenges related to battery life and communication infrastructure. Proposed solutions include the development of more efficient batteries and the integration of advanced technologies to improve operational effectiveness. This research is expected to serve as a foundation for the development of more adaptive and efficient robots in natural disaster situations.
MAMDANI FUZZY LOGIC ANALYSIS FOR ANIMAL MEDICINE STOCK OPTIMIZATION Desmarini, Mutia; sriani
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.4070

Abstract

Abstract: The management of veterinary drug stocks at the Veterinary Clinic Technical Implementation Unit (UPTD) of the North Sumatra Province Plantation and Livestock Service faces obstacles in the form of discrepancies between supply and demand, resulting in excess stock and budget waste. Uncertain demand for drugs is a factor that complicates decision-making in stock provision. This study aims to optimize drug stock management using the Mamdani fuzzy logic method, which is capable of handling data uncertainty and modeling information linguistically. Three input variables are used, namely initial stock, demand, and number of visits, with the output being the final stock. The process involves fuzzification, inference based on IF–THEN rules, and defuzzification using the centroid method. The results show that the developed system has a good accuracy level with a MAPE value of 17.52%, which means that this model is effective in providing optimal and efficient drug stock recommendations in a veterinary clinic environment. Keywords: fuzzy mamdani; optimization; animal drug stock.
Co-Authors Akbar, M. Rabbani Aulia Rahman Lubis, M. Luthfi Manisa, Bunga Nurul Putra Pratama Sriani