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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Jurnal Kelautan : Indonesian Journal of Marine Science and Technology Journal of Geoscience, Engineering, Environment, and Technology Acta Aquatica: Aquatic Sciences Journal Jurnal Riset Akuakultur Jurnal Serambi Engineering Jurnal Iktiologi Indonesia (Indonesian Journal of Ichthyology) Jurnal Ilmiah Samudra Akuatika Jurnal Pengabdian Masyarakat: Darma Bakti Teuku Umar Munggai: Jurnal Ilmu Perikanan dan Masyarakat Pesisir Depik Jurnal Ilmu-Ilmu Perairan, Pesisir dan Perikanan Buletin Pengabdian Depik Jurnal Ilmu-Ilmu Perairan, Pesisir, dan Perikanan Jurnal Solusi Masyarakat Dikara Jurnal Perikanan
Saiful Adhar
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences Other

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The Application of the willow leaf powder (Justicia gendarussa) in the fish feed to reduce the level of fertility of gift tilapia, Oreochromis sp. Munawar Khalil; Nurul Aida; Saiful Adhar; Prama Hartami
Jurnal Iktiologi Indonesia Vol 16 No 1 (2016): February 2016
Publisher : Masyarakat Iktiologi Indonesia (Indonesian Ichthyological Society)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32491/jii.v16i1.45

Abstract

The study was conducted on March, 18th -May, 2nd 2014 at Balai Benih Ikan Keumala, Pidie Regency, Nangroe Aceh Darussalam Province. The aim of this study was to test the use of willow leaf powder in the feed to reduce fertility levels of gift tilapia (Oreochromis sp. genetic improvement farmed tilapias ). The method in this study used non-factorial completely randomized design with four treatments and three replications i.e. A: without giving willow leaf powder, B: 40 mg, C: 50 mg, D: 60 mg leaf powder. The results showed that the application of willow leaf powder in the fish feed gives a very significant effect on the level of fertility and hatching rate of tilapia, where (F value > F table). The eggs were unfertilized on the treatment numbers D, C, and B, meanwhile almost of eggs were fertilized in the treatment numbers A (without giving willow leaf powder). Otherwise, the results showed that the willow leaf powder was not affect the growth weight and length of tilapia (Fvalue < Ftable). Abstrak Penelitian ini dilaksanakan pada tanggal 18 Maret-2 Mei 2014 di Balai Benih Ikan Keumala, Kabupaten Pidie, Pro-vinsi Nangroe Aceh Darussalam. Tujuan penelitian untuk menguji penggunaan tepung daun gandarusa dalam pakan un-tuk mengurangi tingkat fertilitas pada ikan nila gift (Oreochromis sp., genetic improvement farmed tilapias). Metode yang digunakan pada penelitian ini adalah rancangan acak lengkap non faktorial dengan empat perlakuan dan tiga ulangan yaitu perlakuan A: tanpa pemberian tepung daun gandarusa, perlakuan B: 40 mg, perlakuan C: 50 mg dan perlakuan D: 60 mg tepung daun gandarusa. Hasil penelitian menunjukkan bahwa pencampuran tepung daun gandarusa dalam pakan memberi pengaruh yang sangat berbeda nyata terhadap tingkat fertilitas dan tingkat penetasan telur ikan nila gift. Telur yang paling banyak tidak terbuahi adalah pada perlakuan D kemudian C, dan B, sedangkan yang paling banyak terbuahi adalah pada perlakuan A. Hasil penelitian ini juga menunjukkan bahwa tepung daun gandarusa tidak memberi pengaruh buruk terhadap pertumbuhan baik pertumbuhan bobot maupun panjang ikan nila gift (Fhitung < Ftabel).
PENGGUNAAN SUMBER KALSIUM DARI CANGKANG TIRAM, KEPITING DAN REMIS TERHADAP MOULTING DAN PERTUMBUHAN UDANG VANAME, Litopenaeus vannamei Muliani Muliani; Saiful Adhar; Rachmawati Rusydi; Erlangga Erlangga; Prama Hartami; Munawwar Khalil; Dian Laili
Jurnal Riset Akuakultur Vol 16, No 3 (2021): (September, 2021)
Publisher : Politeknik Kelautan dan Perikanan Jembrana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (97.38 KB) | DOI: 10.15578/jra.16.3.2021.185-193

Abstract

Penggunaan sumber kalsium sintetik dengan ukuran partikel yang relatif besar di tambak diduga menyebabkan ketidaksempurnaan moulting pada budidaya udang vaname, Litopenaeus vannamei. Salah satu sumber yang berkelanjutan untuk memenuhi kebutuhan kalsium selama proses moulting adalah limbah cangkang dari biota perairan budidaya lainnya. Penelitian ini bertujuan untuk mengevaluasi penggunaan sumber kalsium dari cangkang moluska yang berbeda terhadap performa moulting dan pertumbuhan udang vaname. Penelitian dilaksanakan pada bulan Agustus-September 2021 bertempat di Laboratorium Hatchery dan Teknologi Budidaya Fakultas Pertanian, Universitas Malikussaleh. Penelitian menggunakan rancangan acak lengkap (RAL) non-faktorial dengan empat perlakuan tiga ulangan, yakni: A (penambahan tepung cangkang tiram 75 mg/L), B (penambahan tepung cangkang kepiting 75 mg/L), C (penambahan tepung cangkang remis 75 mg/L), dan D (kontrol), masing-masing tiga ulangan. Tahapan-tahapan dalam membuat tepung yaitu pencucian, penjemuran, penumbukan, pengayakan, dan pembuatan nannokalsium (furnace). Parameter yang diamati selama penelitian antara lain: jumlah individu moulting, kecepatan moulting, laju pertumbuhan harian, dan kandungan kalsium cangkang. Hasil penelitian menunjukkan bahwa perlakuan terbaik terdapat pada perlakuan A (penambahan tepung cangkang tiram 75 mg/L) menghasilkan jumlah individu moulting sebesar 77,50%; kecepatan moulting 2,00 hari; laju pertumbuhan harian 3,31%; dan tingkat sintasan 93,33%. Penelitian ini menghitung bahwa 1 ha tambak udang membutuhkan 6 kg tepung cangkang untuk mencukupi kebutuhan kalsium udang budidaya. Parameter kualitas air tambak yang diukur (suhu, pH, oksigen terlarut, salinitas, dan amonia) menunjukkan nilai optimal untuk pertumbuhan udang vaname. Penelitian ini menyimpulkan bahwa kalsium dari cangkang tiram paling baik dalam meningkatkan proses moulting udang vaname dan merekomendasikan penggunaannya sebagai alternatif sumber kalsium untuk menggantikan kalsium dari batu gamping.The use of synthetic calcium sources with relatively large particle sizes in brackishwater ponds is suspected of causing moulting imperfection in cultured Pacific white shrimp, Litopenaeus vannamei. One of the sustainable sources to supply calcium needs during the moulting process is the shell waste from other farmed aquatic biota. This study aimed to evaluate the use of calcium sources from different mollusk shells on the moulting and growth performance of Pacific white shrimp. The research was conducted between August-September 2021 at the Hatchery and Cultivation Technology Laboratory, Faculty of Agriculture, Malikussaleh University. The study used a non-factorial completely randomized design (CRD) with four treatments, namely: the addition of A (75 mg/L oyster shell flour), B (75 mg/L crab shell flour), C (75 mg mussel shell flour), and D (control, 0 mg/L of shell flour) in the rearing media with three replications. The shell flour was transformed into nano-calcium via different production stages. The parameters observed during the study included: number of moulting individuals, moulting rate, daily growth rate, and shell calcium content. The results showed that the best treatment was in treatment A (addition of oyster shell flour 75 mg/L) resulted in the number of moulting individuals of 77.50%; moulting rate of 2.00 days; daily growth rate of 3.31%; and a survival rate of 93.33%. This study calculated that 1 ha of shrimp pond required 6 kg of shell flour to sufficiently supply the calcium demand of cultured shrimp. The measured ponds’ water quality parameters (temperature, pH, dissolved oxygen, salinity, and ammonia) showed optimal values for the growth of Pacific white shrimp. This study concludes that calcium from oyster shell has the best in improving the moulting process of Pacific white shrimp and recommends its use as an alternative source of calcium to replace calcium from limestone.
Estimasi Potensi Produksi Ikan Di Danau Laut Tawar Berdasarkan Morphoedaphic Index Saiful Adhar; Ternala Alexander Barus; Esther Sorta Mauli Nababan; Hesti Wahyuningsih; Erlangga Erlangga; Munawwar Khalil
Jurnal Serambi Engineering Vol 5, No 3 (2020)
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v5i3.2144

Abstract

Laut Tawar Lake is located in Aceh Tengah District is the largest lake in Aceh Province. The lake, located at an altitude of 1230 meters above sea level, produces about 13 species of freshwater fish. Fish depik (Rasbora tawarensis), eyas (Rasbora sp.), and relo (Rasbora sp.) are endemic species of Laut Tawar Lake. This study aims to estimate the potential of fish production based on the value of morphoedaphic index. The observation was conducted for one year, from October 2016 until September 2017. The measurement of electric conductivity value of lake waters was conducted on 7 (seven) stations selected purposively in the lake area about 5870 hectares. The results showed that morphoedaphic index value of Laut Tawar Lake ranged from 5.10 to 7.84 with an average of 6.14. Potential of fish production in the lake is 33.47 kg/ha/yr with total potential of fish production of 196.49 ton/yr. The value shows a decrease of 10.93 kg/ha/yr over a period of 22 years. This decrease is caused by changes in morphometry parameters and water quality of Laut Tawar Lake.
Pemodelan Status Trofik Danau Laut Tawar Aceh Tengah Saiful Adhar; Erlangga Erlangga; Rachmawati Rusydi; Mainisa Mainisa; Munawwar Khalil; Muliani Muliani; Eva Ayuzar; Muhammad Hatta
Jurnal Serambi Engineering Vol 7, No 2 (2022): April 2022
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v7i2.4022

Abstract

The trophic status of Laut Tawar Lake was increasingly leading to an alarming level of degradation. Efforts to anticipate an increase in trophic status require scientific knowledge of the trophic phenomena of the waters empirically. This research examines the trophic status parameters to produce a model of the trophic status of Laut Tawar Lake. The proposed model was a modification of Carlson's Trophic State Index Method. Analysis of the relationship examined the interaction of water clarity (SD) with chlorophyll-a (Chl), Total Suspended Solid, and Total Dissolved Solids, and the interaction of chlorophyll-a with nutrient concentration (TP, TN). Data were analyzed descriptively quantitatively, correlation, and regression. The results showed that the waters of Laut Tawar Lake contain phosphate (TP) 34 µg/L, Total Nitrogen 687 µg/L, chlorophyll-a 10 µg/L, Total Suspended Solid 47 mg/L, Total Dissolved Solid 84 mg/L, and water clarity 4.0 m. The regression test showed that the abundance of phytoplankton (Chl) was affected by nutrient phosphate with the empirical model equation Chl = 0.565 TP – 9.161. Nitrogen nutrients did not partially affect the concentration of chlorophyll-a in the waters of Laut Tawar Lake. Water clarity is influenced by chlorophyll-a and TSS, where TDS has no effect partially. The empirical model obtained is Ln SD = 1.757 – 0.013 Chl – 0.008 TSS. Modification of Carlson's TSI by substituting the two equations obtained the equation TSIdlt = 13.46 + 8.08 ln TP + 0.04 TP + 0.04 TSS. This model simulation gives an estimation rate of 91.06%.
Analisa Limbah Fosfor Kegiatan Keramba Jaring Apung di Danau Laut Tawar Aceh Tengah Saiful Adhar; Rachmawati Rusydi; Mainisa Mainisa; Erlangga Erlangga; Munawwar Khalil; Eva Ayuzar
Jurnal Serambi Engineering Vol 6, No 3 (2021): Juli 2021
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v6i3.3051

Abstract

Floating net cage activities can cause  water quality to decrease. The feed remains and the metabolism of the fish  from these activities produces nitrogen and phosphorus .  The objective of this study  is to obtain the calculation model and the amount of phosphorus released into the waters of Laut Tawar Lake  as a result of floating  cage activities. The resulting  formula  was Pw = (F x [P]p) – ((It – Io) x [P]i), where their variables were given feed (F), phosphorus concentration of feed ([P]p), the final weight of fish (It), the initial weight of fish (Io), and phosphorus concentration in fishes ([P]i). It can be used to predict the increase in phosphorus waste based on the increase in floating net cage area. The weight of tilapia increases exponentially with the  day of cultivation in the equation Y = 12.70e0.02x and the goldfish followed the formula  Y = 2.28e0.03x. Phosphorus in tilapia varies  from 1.58% to 2.23% with an average of 1.97%. Goldfish contain  1.19 - 2.02% phosphorus, with an average of 1.52%. Goldfish  growth  was not optimal due to inadequate feeding,   so  without excessive feeding and did not generate  phosphorus waste. The cultivation of  tilapia releases  0.09 Kg P/m2 of phosphorus waste from floating cages .
Identifikasi Keberadaan Mikroplastik Pada Insang dan Saluran Pencernaan Ikan Kembung (Rastrelliger sp) di TPI Belawan Erlangga Erlangga; Riri Ezraneti; Eva Ayuzar; Saiful Adhar; Salamah Salamah; Hyessica Bernardeta Lubis
Jurnal Kelautan Vol 15, No 3: Desember (2022)
Publisher : Department of Marine Sciences, Trunojoyo University of Madura, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jk.v15i3.11746

Abstract

ABSTRACTPlastic waste is one of the biggest problems that is difficult to deal with because plastic is strong, elastic and durable, making it difficult for nature to decompose. Microplastics are plastic particles 5 mm in size that have been degraded from their initial form over a long period of time. Microplastics can damage aquatic ecosystems and cause death to biota because they inhibit metabolic processes, can transfer harmful chemicals to biota, and so on. This study aims to determine the type, size, color, and abundance of microplastics in the gills and gastrointestinal tract of mackerel. The research method uses descriptive methods to describe a phenomenon that is currently happening and conduct observations and interviews with sellers/fishermen. This study used 30 mackerel fish from the Belawan shelter and were analyzed at the Water Quality and Fish Nutrition Laboratory, Malikussaleh University. The results showed that as many as 84 microplastic particles in the gills were in the form of fibers and dominated by black, and there were 74 microplastic particles found in the gastrointestinal tract in the form of fibers and films with black, blue, red microplastic fibers and transparent gray microplastic film colors. The sizes of the microplastics found varied and were not affected by the length or weight of the fish, gills and gastrointestinal tract.Keywords: Mackerel, gills, microplastics, digestive tract, and plastic waste.ABSTRAKSampah plastik merupakan salah satu masalah terbesar yang masih sulit untuk ditangani karena plastik bersifat kuat, elastis dan tahan lama sehingga sulit diurai oleh alam. Mikroplastik merupakan partikel plastik yang berukuran 5 mm dan  telah mengalami degradasi dari bentuk awalnya dengan jangka waktu yang lama. Mikroplastik dapat merusak ekosistem perairan dan menyebabkan kematian pada biota karena menghambat proses metabolisme, dapat menstrasfer zat-zat kimia berbahaya pada biota, dan lain sebagainya. Penelitian ini bertujuan untuk  mengetahui tipe/jenis, ukuran, warna, dan kelimpahan mikroplastik pada insang dan saluran pencernaan ikan kembung. Metode penelitian menggunakan metode deskriptif untuk menggambarkan atau menguraikan suatu fenomena  yang sedang terjadi dan melakukan observasi maupun wawancara dengan penjual/nelayan. Penelitian ini dilakukan dengan menggunakan ikan kembung sebanyak 30 ekor dari tempat penampungan Belawan dan diteliti di Laboratorium Kualitas Air dan Nutrisi Ikan, Universitas Malikussaleh.  Berdasarkan hasil penelitian ditemukan sebanyak 84 partikel mikroplastik di bagian insang dengan bentuk fiber dan didominasi warna hitam, serta ada 74 partikel mikroplastik yang ditemukan di bagian saluran pencernaan dengan bentuk fiber maupun film dengan warna mikroplastik fiber hitam, biru, merah serta warna mikroplastik film abu-abu transparan. Ukuran mikroplastik yang ditemukan berbeda-beda dan tidak dipengaruhi oleh panjang maupun berat ikan, insang dan saluran pencernaan.Kata Kunci: Ikan kembung, Insang, Mikroplastik, Saluran pencernaan, dan Sampah plastik. 
ANALISIS PARAMETER KUALITAS AIR DI KAWASAN TAMBAK RANCONG KOTA LHOKSEUMAWE Ayu Gustina; Riri Ezraneti; Erlangga; Muliani; Saiful Adhar
MUNGGAI : Jurnal Ilmu Perikanan dan Masyarakat Pesisir Vol 9 No 01 (2023): Jurnal Ilmu Perikanan dan Masyarakat Pesisir
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Banda Naira

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lhokseumawe merupakan kota yang dikelola sebagai sektor industri, pariwisata dan perikanan yang masih berjalan hingga saat ini. Tambak Rancong merupakan kawasan yang mudah terkena dampak dari aktivitas tersebut. Tujuan dari penelitian ini adalah untuk mengetahui beberapa nilai parameter fisik dan parameter kimia di kawasan tambak Rancong Lhokseumawe. Penelitian dilakukan pada bulan Mei 2019. Metode penentuan lokasi penelitian berdasarkan survey dengan metode sampling dan analisis parameter fisika kimia di laboratorium PT. Prima Bireun. Hasil parameter yang diamati adalah parameter fisik perairan tambak Rancong untuk parameter seperti suhu dengan nilai 34 0C pada stasiun 1 sedangkan pada stasiun 2 dengan nilai 33,5 0C telah melebihi baku mutu (28-310C) dan salinitas. pada stasiun 1 dengan nilai 34 ppt sedangkan pada stasiun 2 dengan nilai 33 ppt. Parameter Kimia Perairan Tambak Rancong untuk parameter seperti : pH dengan nilai 8,0 pada stasiun 1 sedangkan pada stasiun 2 dengan nilai 8,1, nitrat pada stasiun 1 dengan nilai 1.257 ppm sedangkan pada stasiun 2 dengan nilai 1.274 ppm sudah melebihi baku mutu yang ditentukan, nitrit di stasiun 1 dengan nilai 0,016 ppm sedangkan di stasiun 2 dengan nilai 0,012 ppm, dan fosfat dengan nilai 0,021 ppm di stasiun 1 sedangkan di stasiun 2 dengan nilai 0,019 ppm.
Phenoxycarboxlic acid Toxicity Test on Vannamei Shrimp (Litopenaeus vannamei) Mortality Lisna Lisna; Mainisa; Erlangga Erlangga; Saiful Adhar; Munawwar Khalil
Jurnal Ilmiah Samudra Akuatika Vol 7 No 1 (2023): Jurnal Ilmiah Samudra Akuatika
Publisher : Fakultas Pertanian Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/jisa.v7i1.7754

Abstract

This study aims to examine the toxicity of phenoxy acid herbicides on vannamei shrimp mortality, by conducting preliminary tests, persistence tests, mortality tests, and water quality. The research design used the regression method and probit analysis with 6 treatments and 3 replications, namely A herbicide concentration of 0 ml/L, B herbicide concentration DMA-6, 0.025 ml/L water, C herbicide concentration DMA-6, 0.005 ml/L water, D DMA-6 herbicide concentration, 0.075 ml/L water, E DMA-6 herbicide concentration, 0.1 ml/L water and F DMA-6 herbicide concentration, 0.125 ml/L water. Pesticides do not affect temperature changes, dissolved oxygen (DO), Salinity, and pH. Clinical symptoms due to Pesticide exposure to vanamei shrimp are irregular movements, shells peeling off, and swimming close to aeration until they die. The LC50 value in the herbicide toxicity test was 0.124 mg/l at 24 hours, 0.099 mg/l at 48 hours, 0.073 mg/l at 72 hours, and 0.026 mg/l at 96 hours.
Strategi Pemasaran Pakan Ikan Buatan Berbahan Baku Lokal Daun Kelor Di Gampong Reuleut Timur Kecamatan Muara Batu Riani, Riani; Rusydi, Rachmawati; Mainisa, Mainisa; Salamah, Salamah; Adhar, Saiful
Jurnal Pengabdian Masyarakat: Darma Bakti Teuku Umar Vol 4, No 2 (2022): Juli-Desember
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/baktiku.v4i2.5642

Abstract

Moringa leaf-based fish feed is feed that is processed by pond farmers in Gampong Reuleut Timur, Muara Batu District, North Aceh, using Moringa leaf raw materials that have good nutritional content and are in accordance with fish needs. This feed is relatively cheap with protein content that meets the needs of fish. However, the feed has not been able to reach a wide market. This is due to the lack of knowledge of farmers about marketing strategies. The service team, through community service activities, aims to provide counseling about marketing strategies for artificial fish feed. Service activities were carried out in Gampong Reuleut Timur, Muara Batu District, North Aceh Regency. Extension activities were carried out by presenting material on the 4P marketing strategy, namely product, price, place, and promotion, and distributing questionnaires to participants to evaluate their understanding of the material. The result of this activity is the increased understanding and knowledge of partners about the 4P marketing strategy, with the hope that later it will be applied to the business that has been occupied in order to obtain maximum profit.<img 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
A Response of Water Temperature to Wind Speed and Air Temperature in Lake Laut Tawar, Aceh Province, Indonesia Adhar, Saiful; Mainisa; Andika, Yudho
Journal of Geoscience, Engineering, Environment, and Technology Vol. 9 No. 3 (2024): JGEET Vol 09 No 03 : September (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2024.9.3.14469

Abstract

Changes in water temperature impact the dynamics of lake ecosystems. Changing climate factors, including wind speed and air temperature, influence the water temperature of lakes. This research aims to analyze the response of water temperature to wind speed and air temperature in Lake Laut Tawar. Observations were conducted from August to September 2023, with a sampling frequency of every two weeks. The results revealed that water temperature, wind speed, and air temperature in Lake Laut Tawar fluctuated according to the presence of light, namely day and night factors. Variations in sunlight intensity lead to hourly fluctuations in air temperatures, while wind speeds vary hourly due to changes in air pressure, consequently resulting in hourly variations in water temperature as well. During daylight hours, air temperature surpasses water temperature, whereas during nighttime hours, water temperature exceeds air temperature. Heat transfer from the air to the water contributes to an increase in water temperature, while the release of heat energy from the surface water into the air leads to a decrease in water temperature. Changes in the water temperature of Lake Laut Tawar are primarily influenced by changes in wind speed and air temperature by 80 percent simultaneously. However, while air temperature showed a partial response, wind speed did not exhibit a significant response. The relationship between these variables can be expressed through a mathematical model Tw = 0.356 Ta + 0.025 W + 15.674, where Tw is water temperature (°C), Ta is air temperature (°C), and W is wind speed (km/minute). Another factor that influences the water temperature of Lake Laut Tawar is the inlet water temperature, which was not observed in this research.
Co-Authors Alura, Aula Maretta Andika, Yudho Arief Rahman Arief Rahman Aula Maretta Alura Ayu Gustina Ayub Sugara Bulan, Rifqi Cut Meurah Nurul ‘Akla Dian Laili Erlangga Erlangga Erlangga Erlangga Erlangga Erlangga Erlangga Erniati Erniati Erniati Eva Ayuzar Eva Ayuzar Eva Ayuzar Eva Ayuzar Eva Ayuzar, Eva Fridayanti, Nelly Hefni Effendi Hesti Wahyuningsih Hyessica Bernardeta Lubis Ilhami, Bq Tri Khairani Imamshadiqin, Imamshadiqin Imanullah Imanullah, Imanullah Julianda Sahputra Julianda Sahputra, Julianda Lisna Lisna LUKMAN, LUKMAN Magfirah Magfirah Magfirah, Magfirah Mainisa Mainisa Mainisa Mainisa, Mainisa Muhammad Fazil, Muhammad Muhammad Hatta Muhammad Hatta Muhammad Hatta MUJIZAT KAWAROE Muliani Muliani Muliani Muliani Muliani Muliani, Muliani Muliani, M Munawar Khalil Munawwar Khalil, Munawwar Nababan, Esther Sorta Mauli Natasya, Siti NEVIATY PUTRI ZAMANI Nurul Aida Prama Hartami Prasetya, Yukis Angga Rachmawati Rusydi Rachmawati Rusydi Rachmawati Rusydi, Rachmawati Rahmatul Husna Rahmatul Husna, Rahmatul Riani Riani, Riani Rifqi Bulan Riri Ezraneti Riri Ezraneti Riri Ezraneti salamah salamah Salamah Salamah Siti Natasya Ternala Alexander Barus Teuku Die Aulya Rizky Teuku Die Aulya Rizky, Teuku Die Aulya Usnawiyah, Usnawiyah Yudho Andika Zaitun, Urmila Zulpikar Zulpikar Zulpikar, Zulpikar Zuriani, Z Zuriani, Zuriani