Development of Early Landslide Detection Tool as Optimization of Disaster Mitigation Understanding for Early Childhood in Wonosobo

Early childhood is a crucial period with immense potential and high vulnerability to natural disasters. In Wonosobo Regency, landslides pose a serious threat, necessitating concrete efforts to enhance the preparedness of young children. This study aims to develop an early landslide detection tool to improve disaster mitigation understanding among early childhood children. The research method employed is Research and Development (R&D) using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques include observation, interviews, and documentation, which were subsequently analyzed. The study results indicate that the developed early detection tool is highly feasible, with an expert media evaluation score of 100%, making the product suitable for further testing. Trials showed that this tool could provide fast and accurate warnings about potential landslides, significantly enhancing preparedness and disaster mitigation understanding among young children in the Wonosobo Regency. The implications of this research highlight the importance of integrating early detection technology into disaster mitigation education, which can improve the safety and understanding of young children regarding future disasters.

Authors

Institutions

Publication Details

Journal
Golden Age Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini
Published
2024-12-31
DOI
https://doi.org/10.14421/jga.2024.94-04
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of Early Landslide Detection Tool as Optimization of Disaster Mitigation Understanding for Early Childhood in Wonosobo

Edi Waluyo, Nabila Zakiyyatul Af'idah
Golden Age Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini
Landslides and related hazards
article

Development of Early Landslide Detection Tool as Optimization of Disaster Mitigation Understanding for Early Childhood in Wonosobo

Edi Waluyo, Nabila Zakiyyatul Af'idah
article en

Abstract

Early childhood is a crucial period with immense potential and high vulnerability to natural disasters. In Wonosobo Regency, landslides pose a serious threat, necessitating concrete efforts to enhance the preparedness of young children. This study aims to develop an early landslide detection tool to improve disaster mitigation understanding among early childhood children. The research method employed is Research and Development (R&D) using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques include observation, interviews, and documentation, which were subsequently analyzed. The study results indicate that the developed early detection tool is highly feasible, with an expert media evaluation score of 100%, making the product suitable for further testing. Trials showed that this tool could provide fast and accurate warnings about potential landslides, significantly enhancing preparedness and disaster mitigation understanding among young children in the Wonosobo Regency. The implications of this research highlight the importance of integrating early detection technology into disaster mitigation education, which can improve the safety and understanding of young children regarding future disasters.

Golden Age Jurnal Ilmiah Tumbuh Kembang Anak Usia DiniVol. 9(4)
State University of Semarang (ID)
Climate action
Openalex Percentile: Top 61%
Landslides and related hazards
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.