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Surface deformation and its implications for land degradation after the 2021 Flores earthquake (M7.4) using differential interferometry synthetic aperture radar Purba, Joshua; Harisma, Harisma; Priadi, Ramadhan; Amelia, Rosa; Dwilyantari, Anak Agung Istri; Jaya, Laode Muhammad Golok; Restele, La Ode; Putra, I Made Wahyu Gana
Journal of Degraded and Mining Lands Management Vol. 12 No. 1 (2024)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2024.121.6819

Abstract

On December 14, 2021, an earthquake with a magnitude of 7.4 originated from the Flores Sea, impacting Kalaotoa Island in Indonesia, resulting in significant structural damage. Macroseismic observations at the site classified that there were 120 buildings slightly damaged, 108 buildings moderately damaged, and 201 buildings heavily damaged. The shakemap shows that Kalaotoa Island experienced VI-VII MMI shaking. The results of the field survey showed many indications of subsidence as many fractures were found in Kalaotoa Island. This study employed Differential Interferometry Synthetic Aperture Radar (DInSAR) to quantify land subsidence and uplift in Kalaotoa Island before and after the earthquake. Sentinel-1A satellite radar data from December 2 and December 14, 2021, were analyzed. The results revealed subsidence of up to 12 cm in Garaupa Raya Village and uplift of up to ±10 cm in Lembang Mate’ne Village. Approximately 50.50% of Kalaotoa Island experienced subsidence (39.4 km²), primarily in Garaupa Village (18.85 km²), while 49.02% of the island experienced uplift (38.2 km²), mostly in Lembang Mate’ne Village (19.03 km²). This spatial analysis underscores the efficacy of DInSAR in detecting and mapping surface deformation, offering critical insights for earthquake preparedness, mitigation efforts for impacted landscape topography, stability soils, structure of ecosystems, and infrastructure resilience.
Analisis Tingkat Kerawanan Bahaya Longsor di Hulu Daerah Aliran Sungai Wanggu, Provinsi Sulawesi Tenggara Golok Jaya, La Ode Muhammad; Restele, La Ode; Kadir, Abdul; Asmirani, Sri; Muhibudin, Muhibudin; Lawa, Asis
Jurnal Manajemen Rekayasa (Journal of Engineering Management) Vol 4, No 2 (2022): Oktober Tahun 2022
Publisher : Pascasarjana Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/jmr.v4i2.27608

Abstract

Bencana tanah longsor adalah salah satu bencana yang paling sering terjadi di wilayah Indonesia disebabkan berbagai factor termasuk adanya aktivitas manusia pada lahan yang tidak sesuai dengan kaidah pelestarian dan konservasi lingkungan. Diperlukan berbagai upaya mitigasi sehingga dampak bencana dapat dikurangi. Hulu Daerah Aliran Sungai (DAS) Wanggu yang berada di Kecamatan Konda dan Ranomeeto kabupaten Konawe Selatan Sulawesi Tenggara merupakan daerah yang sering terjadi longsor. Tujuan penelitian ini adalah untuk menganalisis tingkat kerawanan longsor pada hulu DAS Wanggu serta teknik mitigasi yang diperlukan. Metode penelitian yang diterapkan dalam penelitian ini mencakup survey lapangan serta identifikasi kuantitatif terhadap lima parameter kondisi lahan, yaitu tutupan lahan, tingkat kelerengan, kondisi geologi, besarnya curah hujan, dan jenis tanah. Teknik analisis data yang digunakan dalam penelitian ini adalah tumpang susun dari parameter yang telah ditentukan disertai pembobotan terhadap faktor-faktor yang berpengaruh terhadap bahaya longsor. Dari penelitian yang dilakukan terlihat bahwa wilayah di hulu DAS Wanggu yang memiliki tingkat kerawanan tinggi seluas, 10,541 Ha, tingkat kerawanan menengah 8,741 Ha, dan daerah dengan tingkat kerawanan rendah 15,191 Ha. Beberapa rekomendasi yang dapat diberikan untuk menurunkan risiko bahaya longsor adalah aspek teknis seperti perkuatan tebing, konservasi lahan yang memperhatikan kemiringan, penatagunaan air dan penguatan aspek manajemen, tatakelola kelembagaan dan kerjasama stakeholder.Kata Kunci: Hulu DAS Wanggu, Tanah Longsor, Mitigasi.
Expert System of Error Tracking Automated Weather Observing System Using Certainty Factor Method Based on Android Application M Djibran, Halis; Purba, Joshua; Saadia, Aprilia Ode; Restele, La Ode; Hasria, Hasria
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22536

Abstract

The limited number of technicians at several BMKG UPT (Task Implementation Units) in Indonesia is the main background of this research. Especially in the field of Aviation Meteorology, which has a significant safety risk for equipment data users. This can be made easier with an expert system. The fault tracking expert system aims to provide information about the symptoms of damage that occur in the Automated Weather Observing System (AWOS) so that it can make it easier for BMKG technicians to repair and handle the equipment. This research stage begins with collecting information data through experts and literature sources regarding AWOS equipment, then calculating the certainty value of the information using the certainty factor method, and produce information that will be displayed through the application. The system uses a Certainty Factor calculation method that presents the calculation of the certainty value of information based on the percentage of information delivery by the source, this method is used in accordance with the type of research that utilizes information from sources or experts in the AWOS field. The resulting system is an android application consisting of several knowledge bases stored in the MySQL database on the server. The results of the data analysis show that the resulting system can be used on the user's smartphone, and users can consult AWOS equipment damage properly. In addition, users can also view the consultation history and damage list. The application user satisfaction questionnaire shows the system has worked and fulfilled the function for users by showing a value of 33.3% Very Good and 66.7% Good.