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All Journal Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT) Jurnal INFOTEL
Azikin, Thahir
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pembentukan Desa Tangguh Bencana Covid-19 untuk Antisipasi Penyebaran dan Dampaknya di Provinsi Sulawesi Tenggara Jaya, Laode M Golok; Ngii, Edward; Mangidi, Uniadi; Azikin, Thahir; Welendo, La
Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT) Vol 2, No 2 (2020)
Publisher : Vokasi Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (936.199 KB) | DOI: 10.33772/jpmit.v2i2.14266

Abstract

The spread of the Corona virus (Covid-19) has recently become increasingly worrying, not only because of the inadequate handling due to the lack of mass rapid tests and the obstacle to the examination method using polymerase chain reaction (PCR) techniques. On the other hand, the habit of people who ignore and underestimate the spread of Covid-19 infection has become the main trigger for the increasing number of positive patients of Covid-19. This is even more worrying if the scale of our review is not only urban but also rural, where the potential for exposure to Covid-19 is getting bigger. Therefore, Halu Oleo University through the Integrated Community Service program (KKN) Thematic Prevention of Covid-19 has a big agenda of increasing public awareness both in cities and in rural areas who are very prone to exposure to Covid-19 to minimize the impact that occurs due to the spread of Covid-19. The purpose of this activity is to form a Covid-19 Disaster Resilient Village to anticipate its spread and impact in Southeast Sulawesi Province. This KKN is a multidisciplinary activity from various scientific fields involving students and lecturers from various departments. The method implemented is socialization as an effort to increase public awareness through social media that is easily accessed and understood by the community while still implementing social distancing and with strict health protocols, involving village and community officials. Another method is to collect data on the profiles of people who are vulnerable to exposure and have an impact on their health and economy, in addition to physical activities such as spraying with disinfectants at houses of worship. Other methods carried out include making booklets using local languages, and making tutorials on using Zoom and Google Meet online media meetings so that learning activities of school students and village officials can continue during the Covid-19 pandemic.The results of this KKN Thematic activity are expected to become a learning medium for students as part of the Merdeka Campus Program, building villages that are resilient to the Covid-19 disaster, maintaining the economy and community activities, and become a forum for community service for lecturers.
A Systematic Mapping Study On Multi-Algorithm Methods For Optimizing Transportation Systems Agustan, Agustan; Soeparyanto, Try Sugiyarto; Azikin, Thahir; Welendo, La; Mangidi, Uniadi; Isnawaty, Isnawaty
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1328

Abstract

The integration of multi-algorithm methods has emerged as a transformative approach in addressing complex challenges within modern transportation systems. This study presents a systematic mapping review to explore the application, effectiveness, and potential advancements of multi-algorithm techniques across diverse transportation domains, including road, rail, air, and maritime transport. By synthesizing findings from 23 selected studies, this research identifies key algorithmic paradigms, such as machine learning (ML), genetic algorithms (GA), optimization models and hybrid frameworks, and their functional roles in enhancing decision making, resource allocation, and system efficiency. The analysis reveals that multi-algorithm systems offer significant advantages in managing uncertainty, processing large-scale datasets, and generating high-probability solutions for real-time operations. In particular, ML algorithms demonstrate robust capabilities in predictive maintenance and demand forecasting, while GA-based approaches excel in dynamic environments such as traffic signal optimization and UAV path planning. Despite these advances, critical challenges persist, including the need for high-quality data, scalable algorithm design, and seamless integration with existing infrastructure. Furthermore, certain promising methods such as the whale optimization algorithm (WOA) and graph neural networks (GNN) remain underutilized, highlighting opportunities for future exploration. This study underscores the necessity for interdisciplinary collaboration and methodological innovation to overcome deployment barriers and enhance the sustainability of intelligent transportation systems (ITS). Ultimately, multi-algorithm approaches have substantial potential to drive the evolution of transportation networks toward greater efficiency, resilience, and adaptability in an increasingly complex and dynamic mobility landscape.
Co-Authors Agustan Agustan, Agustan Edward Ngii Isnawaty Isnawaty, Isnawaty Jaya, Laode M Golok La Welendo, La Mangidi, Uniadi Try Sugiyarto Uniadi Mangidi