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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Journal of Oceanography Jurnal Kelautan Nasional Jurnal Chart Datum Indonesian Journal of Earth Sciences Jurnal Mandiri IT International Journal of Humanities Education and Social Sciences Formosa Journal of Science and Technology (FJST) Jurnal Hidrografi Indonesia
Gentio Harsono
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Studi Makrozoobentos sebagai Bioindikator Kualitas Perairan di Pesisir Teluk Jakarta pada Mei 2024: Study of Macrozoobentos as A Bioindicator of Water Quality on The Coast of Jakarta Bay in May 2024 Suweni, Imanuel; Widya, Kartika; Yuswantari, Truly; Harsono, Gentio
Jurnal Hidrografi Indonesia Vol 7 No 1 (2025): Jurnal hidrografi Indonesia
Publisher : Pusat Hidro-Oseanografi TNI Angkatan Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62703/jhi.v7i1.148

Abstract

Penelitian ini bertujuan untuk mengevaluasi kualitas perairan di pesisir Teluk Jakarta dengan menggunakan makrozoobentos sebagai bioindikator. Makrozoobentos adalah organisme bentik yang hidup di dasar perairan, dipilih karena sensitivitasnya terhadap perubahan kondisi lingkungan dan kemampuannya mencerminkan kualitas air suatu daerah. Studi dilakukan pada Mei 2024, dengan pengambilan sampel di tiga stasiun yang tersebar di sepanjang pesisir Teluk Jakarta. Setiap stasiun dipilih secara sistematis untuk mewakili variasi kondisi lingkungan di area penelitian. Sampel makrobentos diidentifikasi hingga tingkat taksonomi yang sesuai dan dianalisis untuk mengevaluasi kualitas perairan berdasarkan indeks biotik. Hasil studi menunjukkan variasi yang signifikan dalam komposisi makrobentos di berbagai lokasi, serta hubungan yang erat antara komposisi makrobentos dan parameter kualitas perairan seperti kandungan bahan organik. Implikasi dari temuan ini mencakup pentingnya menggunakan makrobentos sebagai indikator sensitif untuk memantau kualitas pesisir dan lingkungan perairan. Studi ini dapat menjadi dasar untuk pengembangan strategi manajemen pesisir Teluk Jakarta yang berkelanjutan di kawasan Jakarta Utara.
Bangka strait salinity prediction using landsat 9 oli image data Khoirun Nisa; Harsono, Gentio; Martha, Sukendra; Waluyo, Dangan
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.447

Abstract

Salinity is an important parameter because it affects the environment, such as water quality, growth, and development of aquatic vegetation and various animal species. Conventional water quality monitoring is still ineffective, so it is necessary to utilize technology in monitoring water quality, including water Salinity is an important parameter because it affects the environment, such as water quality, growth, and development of aquatic vegetation and various animal species. Conventional water quality monitoring is still ineffective, so it is necessary to utilize technology in monitoring water quality, including water salinity. Utilization of remote sensing is often used to study salinity both on a small scale and a global scale. Therefore, the author conducted a study to predict salinity in the Bangka Strait using the RRS (Remote Sensing Reflectance) method. The data used are Landsat 9 OLI image data downloaded from the USGS website and in situ salinity data in the Bangka Strait sea. The Landsat 9 OLI image data used is level 2 Surface Reflectance (SR), which is ready for analysis without additional processing by the user. The data obtained were processed using multiple linear regression analysis with Rrs as the independent variable and in situ salinity as the dependent variable. Salinity prediction models are divided into three groups based on the image recording date, namely Rrs 1 for the Landsat 9 OLI image recording on May 9, 2024, Rrs 2 for July 28, 2024, and Rrs 3 for the image recording on September 28, 2023. Multiple linear regression analysis produces R² values for each model of 0.81662874, 0.8170285, and 0.8136894. These R² results indicate that the three models, Rrs 1, Rrs 2, and Rrs 3, are included in the very good criteria in predicting salinity. To choose the best of the three models, by considering the results of the validity test. The NMAE validity test for Rrs 1, Rrs 2, and Rrs 3 is 10.10152, 10.37618, and 8.88680. Meanwhile, the RMSE values are 2.41327, 2.41064, and 2.43253. Therefore, it can be determined that the Rrs 2 model is the best in predicting salinity because it has the highest R² value, namely 0.8170285, and the smallest RMSE, namely 2.41064.
Sea Surface Salinity (SSS) Prediction Using Landsat 8 OLI Image Data in The Bangka Strait Waters with Five Prediction Model Combinations Nisa, Khoirun; Harsono, Gentio; Martha, Sukendra; Waluyo, Dangan
Indonesian Journal of Earth Sciences Vol. 5 No. 2 (2025): July-December
Publisher : MO.RI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52562/injoes.2025.1738

Abstract

Salinity is the most important parameter for controlling the biological components of ecosystems, seas, and estuaries, which also control the components that make up an ecosystem. Conventional water quality monitoring is considered inaccurate and inefficient in terms of energy and time. Therefore, research is needed to predict sea surface salinity as a type of water quality monitoring using remote sensing reflectance or Remote Sensing Reflectance (RRS) from Landsat imagery. The Landsat image data used is level 2 Surface Reflectance (SR), which is ready to use without additional processing by the user, whereas previous research required corrections to the image data to obtain Surface Reflectance image data. This study aims to determine the performance of the prediction model produced by using five combinations of Landsat image bands. The data used are Landsat 8 OLI image data (recording date 05 August 2024) downloaded from the USGS website and in situ salinity data in the Bangka Strait sea (09 March 2025), as many as 5 samples that can be used. The obtained data were processed using multiple linear regression analysis with Rrs as the independent variable and in situ salinity as the dependent variable. The salinity prediction model consisted of five band combinations. The analysis produced R² values for each model combination of 0.8166287408, 0.935603228, 0.820745745, 0.869209652, and 0.574027060. The RMSE validity tests for combination 1, combination 2, combination 3, combination 4, and combination 5 were 2.41327, 1.43012, 2.38602, 2.03811, and 3.67817. Then for the NMAE value, namely 10.10152205%, 5.32713015%, 9.58011308%, 8.8868031%, and 14.51012574%. The combination rankings that have the best prediction performance are combination 2, combination 4, combination 3, combination 1, and combination 5. So the best model in predicting seawater salinity is the combination of the 2 prediction models, with its constituent band components being band 1, band 2, and band 4.
Co-Authors Adi Purwandana Adrianto, Dian Agus Atmadipoera Akhmad Rafii Anatansyah Ayomi Anandari Anggriawan Maydwika, Dhedy Budi Sukoco, Nawanto Chairil Amin Saputra Fatoni, Khoirul Imam Firdaus, Randi Gultom, Feri Hascaryo, Anom Puji Henry Munandar Manik Heryoso Setiyono Heryoso Setyono Junaedi, Liva Khoirun Nisa KHOIRUN NISA Kurnia Darmawan, Lalu Kusmantoro, Yeri Putra, I Wayan Sumardana Eka Putra, Iska Raden Bima Yoga B Setyo Pranowo , Widodo Subiyanto Subiyanto Sukendra Martha Suweni, Imanuel Trismadi Trismadi, Trismadi Wadjdi, Achmad Farid Waluyo, Dangan Widodo S Pranowo Widya, Kartika Yuniarti Yuniarti Yuswantari, Truly