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All Journal Journal Transformation of Mandalika Journal Of Applied Multidisciplinary Studies (JAMS)
Jayaun
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Penerapan V-Model dalam pengembangan sistem penerimaan siswa baru Menggunakan PHP dan mySQL Jayaun; Dedi Supriyadi
Journal Transformation of Mandalika, e-ISSN: 2745-5882, p-ISSN: 2962-2956 Vol. 3 No. 2 (2022): Februari
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jtm.v3i2.867

Abstract

The rapid development of technology and communication requires humans to act faster with attention to efficiency and effectiveness. SDIT Insan Kamil as an institution engaged in the field of Education. The expected goal of this research is to build a P2DB information system that is used to store and utilize new student registrations owned by SDIT Insan Kamil, making it easier to find solutions to the problems at hand. For the data collection method, the researcher did by direct observation. New student enrollment is done by knowledge creation, knowledge sharing and knowledge application. System development method with the V-Model method. The result of this research is to show that the application of new student registration is the solution to the problem.
Neuromorphic Computing Model Based on Spiking Neural Network for an Efficient and Resilient Tsunami Early Warning System in Indonesia’s Small Islands Jayaun, Jayaun; Jayaun
Journal Of Applied Multidisiplinary Studies Vol 1 No 2 (2025): Mei 2025
Publisher : Sekolah Tinggi Ilmu Ekonomi Ganesha

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Abstract

This study aims to develop a fault-tolerant neuromorphic computing system for tsunami early detection in Indonesia’s small islands, which face significant limitations in energy and network infrastructure. The research was conducted over a three-month period (January–March 2025) using a simulated experimental approach with ocean wave data obtained from BMKG and NOAA. The system model was designed using a Spiking Neural Network (SNN) that mimics biological neuron activity to adaptively recognize ocean wave anomaly patterns. Simulation results show a detection accuracy rate of 94%, maintaining stable performance above 85% even under 25% signal interference. Furthermore, the system’s power consumption was recorded at only 0.42 watts—approximately 40–60% more efficient than conventional CNN-based models. The implications of this study include scientific contributions to the development of adaptive and energy-efficient artificial intelligence, as well as practical benefits for agencies such as BMKG and BNPB in designing autonomous and resilient tsunami early warning systems for remote and underdeveloped regions. In the future, this system has the potential to serve as a prototype for edge computing–based disaster mitigation solutions powered by artificial intelligence, particularly relevant for archipelagic nations.
Co-Authors Dedi Supriyadi Jayaun, Jayaun