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All Journal Jurnal Ilmiah MTG Journal of Degraded and Mining Lands Management IndoMath: Indonesia Mathematics Education
Ulfa, Uli
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Energy Environmental Science

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Analisis Potensi Longsor Menggunakan Metode Weighted Overlay di Daerah Aliran Sungai Progo Ulfa, Uli
Jurnal Ilmiah MTG Vol 14, No 1 (2023): Jurnal Ilmiah MTG Volume 14 No. 1 Tahun 2023
Publisher : Jurusan Teknik Geologi Fakultas Teknologi Mineral UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jmtg.v14i1.10345

Abstract

Kawasan DAS Progo merupakan salah satu wilayah konservasi yang saat ini banyak mengalami kerusakan. Hal ini terjadi karena perubahan fisik akibat peralihan pemanfaatan lahan terutama untuk bangunan yang tanpa disadari memberi pengaruh besar terhadap pergerakan tanah penyebab longsor. Selain itu, DAS Progo berada pada wilayah pegunungan dan perbukitan yang dapat memungkinkan terjadinya bencana tanah longsor. Oleh karena itu, penelitian ini bertujuan untuk menganalisis potensi rawan longsor pada kawasan DAS Progo sebagai upaya mitigasi bencana sehingga dapat mencegah dan meminimalisir terjadinya kerusakan. Penelitian ini menggunakan metode Weighted Overlay dalam melakukan analisis pemetaan daerah rawan longsor di DAS Progo. Berdasarkan hasil analisis dapat disimpulkan bahwa terdapat faktor alami dan manajemen yang mempengaruhi terjadinya longsor di DAS Progo. Faktor curah hujan dan kemiringan lereng memberikan pengaruh yang dominan terhadap potensi rawan longsor pada kawasan DAS Progo. Secara umum kawasan ini didominasi dengan tingkat potensi Sedang-Tinggi. Hasil analisis pemetaan didapatkan bahwa daerah yang memiliki potensi bahaya longsor Sangat Tinggi pada kecamatan Pakem Kabupaten Sleman dan kecamatan Cepogo Kabupaten Boyolali. Kawasan dengan potensi rawan longsor Tinggi berada pada wilayah dengan morfologi perbukitan dan pegunungan, baik pada kompleks pegunungan kulonprogo, maupun kompleks gunungapi Merapi, Merbabu, Sindoro, dan Sumbing.
The Effectiveness of Google Meet on Mathematics Problem Solving Ability of Class XI Students Proborini, Ellen; Ulfa, Uli
IndoMath: Indonesia Mathematics Education Vol 5, No 2 (2022)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/indomath.v5i2.29

Abstract

The purpose of this study was to determine the effectiveness of Google Meet on students' mathematical problem solving abilities. The population of this study were students of class XI SMK Tunas Harapan Pati for the 2020/2021 academic year. This study uses a quantitative research method with an experimental method, where the treatment is only carried out once in the sample class which is estimated to be able to have an influence on learning outcomes. Data collection techniques using documentation and test methods. The results showed that the problem solving ability of students was more effective if it was done using google meet in learning mathematics on the opportunity material for students of class XI APL SMK Tunas Harapan Pati. This is evidenced by the increase in value from 669.57 to 78.
Soil moisture monitoring for drought disaster mitigation using remote sensing on the volcanic landscape, Yogyakarta, Indonesia Sarastika, Tiara; Susena, Yusuf; Aji, Krishna; Ulfa, Uli
Journal of Degraded and Mining Lands Management Vol. 12 No. 5 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.125.8975

Abstract

Monitoring soil surface moisture is a crucial aspect of drought detection and management, significantly contributing to informed decision-making in agriculture and natural resource management. Remote sensing technologies have been widely applied to predict surface soil moisture. This study aimed to develop and analyze a surface soil moisture prediction model based on remote sensing data for drought mitigation. This research was conducted in the administrative area of Sleman Regency, Yogyakarta Special Region Province. This study employed a method that integrates field measurements with remote sensing-derived data to develop a predictive model of surface soil moisture. The model was constructed using the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The modeling results yielded an R² value of 0.62 and an RMSE of 0.83. The model was then applied to generate spatial estimates of surface soil moisture for the period from May to October 2024. Spatially, all six months exhibited a consistent distribution pattern, with low soil moisture levels concentrated in the central southern part of the study area. The findings can serve as a basis for highlighting land management in line with SDG Goal 15, which aims to protect, restore, and enhance the sustainable use of terrestrial ecosystems, sustainably manage forests, halt and reverse land degradation, and halt biodiversity loss.
Co-Authors Aji, Krishna Proborini, Ellen Sarastika, Tiara Susena, Yusuf