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All Journal Jurnal Fisika Unand Forum Geografi
Fauzi, Adam Irwansyah
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Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Monitoring Biochemical Oxygen Demand (BOD) Changes During a Massive Fish Kill Using Multitemporal Landsat-8 Satellite Images in Maninjau Lake, Indonesia Rohman, Arif; Fauzi, Adam Irwansyah; Ardani, Nesya Hafiza; Nuha, Muhammad Ulin; Perdana, Redho Surya; Nurtyawan, Rian; Lotfata, Aynaz
Forum Geografi Vol 37, No 1 (2023): July 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v37i1.21307

Abstract

Maninjau Lake is one of Indonesia's lakes for hydroelectric power plants, tourism, and fish farming activities. Some activities around the lake cause pollution, leading to massive fish kill. Therefore, it is necessary to monitor water quality regularly. One of the critical water quality parameters is biochemical oxygen demand (BOD). This study aimed to analyze BOD changes using a remote sensing approach during massive fish kills in Maninjau Lake, Indonesia. Multi-temporal Landsat-8 satellite images are processed to estimate the BOD level based on Wang Algorithm. After that, the estimated BOD value is validated using in situ data measurement. The results of the average BOD concentration that occurred in Lake Maninjau was 1.85 mg/L and showed that R2 was 0.8334, and the standard error was 0.076 between the estimated BOD and in situ data. Furthermore, the average concentration of BOD obtained on 23rd August 2017, 13th December 2017, 30th January 2018, 19th March 2018, and 7th July 2018 are 4.96 mg/L, 4.82 mg/L, 5.31 mg/L, 6.94 mg/L, and 6.60 mg/L, respectively. Increased BOD concentration in January 2018 indicates moderate pollution in the waters. BOD concentration increases after the massive fish kill due to the decaying fish across the lake.
Klasifikasi Tanaman Menggunakan Metode Deep Learning Residual Network (ResNet) Berbasis Data Time Series Penginderaan Jauh di Desa Girimulyo, Lampung Timur Rahadianto, Muhammad Ario Eko; Sejati, Putri Wahyu; Fauzi, Adam Irwansyah; Atmojo, Aulia Try; Widayanti, Tika; Yudanegara, Rizky Ahmad
Jurnal Fisika Unand Vol 15 No 1 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.1.70-78.2026

Abstract

Most Indonesians work in agriculture, making crop-type maps essential for food security. This study evaluates time-series classification using Residual Network (ResNet) for crop mapping. Sentinel-2A imagery from May 2021 to May 2022 was used with 120 samples across five classes: Corn, Coconut, Non-crop, Banana, and Other Crops. The data were processed into a regularized Earth Observation (EO) data cube and trained using samples filtered with Self-Organizing Map (SOM) under two schemes: single clustering (SC) and double clustering (DC). The ResNet model was trained with filtered data and tested with varying epochs. The study produced a crop-type map of Girimulyo, East Lampung, smoothed with the Bayesian method. Accuracy assessment showed that SC at 100 epochs achieved 87%, exceeding the 85% threshold, while DC yielded lower accuracy due to reduced training data. These results confirm that ResNet-based time-series classification is effective for crop-type mapping in the study area.
Co-Authors Ardani, Nesya Hafiza Arif Rohman Atmojo, Aulia Try Lotfata, Aynaz MUHAMMAD ULIN NUHA Perdana, Redho Surya Rahadianto, Muhammad Ario Eko Rian Nurtyawan Sejati, Putri Wahyu Tika Widayanti Yudanegara, Rizky Ahmad