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All Journal Journal of Geoscience, Engineering, Environment, and Technology Jurnal Penelitian Pendidikan IPA (JPPIPA)
Muksin, Umar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Engineering Environmental Science Materials Science & Nanotechnology Physics

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The Influence of Slope, Rock Characteristics and Meteorological Data to Landslide: A Case Study in The Northernmost Sumatra, Indonesia Idris, Mochammad V.; Muksin, Umar; Syukri, Muhammad
Journal of Geoscience, Engineering, Environment, and Technology Vol. 9 No. 04 (2024): JGEET Vol 09 No 04 : December (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2024.9.04.19420

Abstract

Landslides are natural disasters that can be caused by high rainfall intensity. Aceh Besar Regency has been hit by landslides 40 times from 2014 to 2023. Aceh Besar Regency serves as an economic connecting route from other cities to the provincial capital of Aceh, Banda Aceh. Therefore, when landslides occur in that area, it will disrupt the economic stability and logistic distribution to other regions. The weighted overlay method maps landslide-prone areas in Aceh Besar Regency. This research aims to create a map of landslide-prone areas in Aceh Besar Regency based on parameters causing landslides, namely slope inclination, rainfall, rock type, soil type, and land cover. The research findings indicate that almost the entire region of Aceh Besar has a moderate to high potential for landslide disasters. The main factor causing landslides in Aceh Besar is its topography, which is dominated by mountains and hills, and the moderate to very high rainfall intensity. The landslide-prone disaster map is validated by landslide incidents recorded by BPBD Aceh Besar from 2014 to 2023, showing results that align with the historical data. This map can be used by relevant authorities and the general public to undertake landslide disaster mitigation in Aceh Besar Regency.
Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System Method Ardana, Dedy; Irwandi, Irwandi; Muksin, Umar; Idris, Mochammad Vicky
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.10148

Abstract

This study analyzes rainfall prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method to improve model accuracy, particularly in extreme rainfall events. The objective of this study is to evaluate rainfall prediction using the ANFIS method to enhance model accuracy, especially in predicting extreme rainfall occurrences. The results indicate a moderate positive correlation with R² of 0.55, demonstrating good model performance at low rainfall levels (<20 mm) but a tendency to underestimate high-intensity rainfall (>60 mm). Residual analysis reveals a distribution around zero without systematic bias, though significant outliers (>20 or <-20) suggest the need for accuracy improvement. Monthly RMSE exhibits fluctuations, with the best performance observed in the June-July-August (JJA) season and notable challenges in December-January-February (DJF) due to extreme variability. Annual RMSE is also higher in extreme rainfall years (2018, 2023) compared to stable years (2019, 2020). The implementation of ANFIS enhances prediction sensitivity by incorporating additional variables such as temperature and humidity, leading to more accurate forecasts, particularly in extreme weather conditions. This study is further supported by STEM research at Universitas Syiah Kuala, which emphasizes the importance of artificial intelligence in climate data analysis to improve weather prediction accuracy. The ANFIS-based approach applied in this research aligns with  STEM studies, which highlight the integration of artificial intelligence in meteorology to mitigate hydrometeorological disaster risks
Co-Authors Ardana, Dedy Idris, Mochammad V. Idris, Mochammad Vicky Irwandi Irwandi Muhammad Syukri