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Landslide susceptibility mapping based on K-Means and Self-Organizing Map clustering with Geographic Information System in Tasikmalaya, West Java Iryanti, Mimin; Ardi, Nanang Dwi; Nurjanah, Riska Siti
Journal of Degraded and Mining Lands Management Vol. 13 No. 1 (2026)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2026.131.9355

Abstract

Landslides are one of the most frequent natural disasters in Indonesia, primarily caused by complex topographic conditions, high rainfall intensity, and extensive land use changes. This study aimed to map landslide-susceptibility areas in Tasikmalaya Regency, West Java, using the K-Means Clustering and Self-Organizing Map (SOM) methods, visualized through a Geographic Information Systems (GIS). The data utilized include Landsat 8 satellite imagery for calculating the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) indices, elevation and slope data derived from Digital Elevation Model (DEM), and 2024 rainfall data from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). Each variable was classified into five categories based on gridcode values to facilitate spatial analysis. The clustering results revealed two main groups, with the first cluster showing higher landslide potential due to a combination of steep slopes, moderate rainfall, and a high level of urban development. This cluster recorded a Silhouette Coefficient value of 0.75, indicating a high level of landslide vulnerability. In contrast, the other cluster represented more stable terrain, with a Silhouette Coefficient of 0.72. This study is expected to serve as a reference for developing disaster risk-based spatial planning and mitigation strategies.
Empowering Physics Teachers in Indonesia through DELIVER: A Community-Based Approach to Reduce Student Misconceptions Achmad Samsudin; Nur Habib Muhammad Iqbal; Nuzulira Janeusse Fratiwi; Nurdini Nurdini; Nanang Dwi Ardi; Andi Suhandi
Smart Society Vol. 5 No. 2 (2025): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v5i2.843

Abstract

Student misconceptions in physics remain a persistent barrier to meaningful learning, often overlooked due to limited teacher training in diagnosis and conceptual remediation. This community service program aimed to enhance the pedagogical capacity of physics teachers in addressing student misconceptions through the DELIVER (Deep Learning-Integrated Verbal Refutation) approach. Implemented using a Community-Based Participatory Research (CBPR) framework, the program involved 237 teachers from various regions in Indonesia, with 69 completing both pre- and post-tests. The training covered deep learning concepts, identification of misconceptions, and practical development of refutation texts. Results indicated significant improvement with normalized gain scores exceeding 0.7 in all concept categories. In addition to cognitive gains, teachers demonstrated reflective shifts in pedagogical awareness regarding the role of misconceptions in learning. A total of 94.12% of participants reported enhanced understanding, and 82.35% expressed readiness to implement the DELIVER approach. In conclusion, the DELIVER approach effectively empowers physics teachers in Indonesia to identify and address student misconceptions, offering a scalable model for professional development in physics and science education. This program contributes to the field by offering a scalable and research-informed model for teacher professional development focused on conceptual change in science education.