Judul: Analysis of Small Pelagic Fishing Ground Using GAM Model and MaxEnt Model in Java Sea
Abstrak: Small pelagic fish are a group of potential fish resource types in the Java Sea. The high fishing effort over the last few years has led to an increase in utilization rate until it reaches an optimum level and tends to be over fishing. Purse seine and bauke-ami vessels are the dominant fishing vessels in these waters. Both types of vessels use light aids in the fishing process. The main obstacle to the success of fishing operations, apart from the dynamic movement of fish, is also caused by oceanographic factors in the waters. Fish respond differently to oceanographic factors, making it difficult for fishermen to carry out fishing operations. To help make it easier for fishermen to catch fish in the Java Sea, it is necessary to develop a better fishing area information system, so that fishing efforts can be more effective and efficient. The aim of this study was to develop a small pelagic fishing area prediction model based on oceanographic parameters, namely sea surface temperature and salinity. This study uses Generalized Additive Models (GAM) and Maximum Entropy (MaxEnt) for prediction modeling of fishing grounds in the Java Sea. The data used are Visible Infrared Imaging Radiometer Suite Boat Detection (VBD) images to determine the position and distribution of fishing vessels, sea surface temperature (SST) images, and salinity data as parameters for DPI modeling. VBD data assumed to be the location of fishing vessels to catch fish is stated as a response variable and oceanographic data is stated as a predictor variable in the development of the GAM model and the MaxEnt model. Data for 2018 is used as training data and data for 2019 is used as data validation. The analysis of the GAM model and the MaxEnt model was carried out using the same software, namely RStudio. The predictive performance of the model is assessed using the area under cover (AUC) matrix. The DPI prediction map generated from the GAM and MaxEnt modelers is expressed by the habitat suitability index (HSI) value. The GAM model for DPI prediction based on SST and salinity parameters shows that the presence of small pelagic fish is in the SST range 28 – 28.7 oC and salinity 32 – 33 psu. The MaxEnt model shows that the highest probability of fish distribution is in the range of SST 27.6 – 31.0 oC, and salinity 32 – 34 psu. Salinity is the most influential parameter in the distribution of small pelagic fish. Potential fishing areas, either based on the GAM model or the MaxEnt model, are found more in the central and eastern parts of the Java Sea closer to the southern waters of Kalimantan Island. The GAM model and MaxEnt model show accurate results in predicting small pelagic DPI in the Java Sea.
Keyword: google earth engine, salinitas, suhu permukaan laut, Laut Jawa

Judul: Potential Fishing Ground Prediction for Bigeye Tuna (Thunnus obesus) in the Southern Off Java Based on Satellite Remote Sensing Data
Abstrak: Prediksi daerah potensial penangkapan ikan dalam bentuk peta diperlukan untuk meningkatkan produktivitas usaha penangkapan ikan. Daerah potensial penangkapan ikan dipengaruhi oleh parameter oseanografi seperti suhu, kelimpahan fitoplankton, tinggi muka air laut, salinitas dan lain-lain. Penelitian ini bertujuan untuk menganalisis hubungan antara hasil tangkapan tuna mata besar dan parameter oseanografi sebagai dasar untuk memprediksi daerah potensial penangkapan ikan baik secara spasial maupun temporal. Dalam penelitian ini dilakukan pengumpulan data tangkapan ikan tuna mata besar dari PT. Perikanan Nusantara dan data parameter oseanografi baik dari citra satelit, in situ dan pemodelan. Data tangkapan ikan diolah dalam bentuk laju pancing (hook rate) tangkapan. Data parameter oseanografi diekstraksi dalam rata-rata bulanan. Hubungan antara parameter oseanografi dengan hasil tangkapan ikan dianalisis menggunakan Generalized Additive Model (GAM). “Best-fit” model dinilai keseesuaiannya berdasarkan nilai Pelagic Hotspot Index (PHI) dengan rentang 0 hingga 1. Nilai PHI diinterpolasi untuk dipetakan secara spasial dimana nilai ≥0,75 dinilai sebagai daerah yang paling potensial untuk keberadaan tuna mata besar. Hasil penelitian menunjukkan distribusi parameter oseanografi bervariasi setiap bulannya. Suhu pada kedalaman 200 m mengalami peningkatan pada Mei hingga Agustus dan menurun hingga November. Konsentrasi klorofil-a di permukaan meningkat dari bulan April hingga puncaknya pada September. Tinggi paras laut mengalami penurunan pada Juni hingga November, sedangkan salinitas paling tinggi di September. Tangkapan tuna mata besar terdistribusi pada 110° – 118° BT dan 10° – 15° LS. Pada tahun 2009 diperoleh tangkapan terbanyak pada bulan Juli sedangkan pada tahun 2010 ada pada bulan Agustus. Puncak musim tangkapan terjadi pada Musim Timur yang terjadi sepanjang Juni hingga Agustus. Pemodelan GAM menunjukkan tangkapan tuna mata besar memiliki hubungan yang signifikan terhadap tiap parameter yang digunakan, dengan konsentrasi klorofil-a sebagai kontributor tertinggi. Performa model terbaik ditunjukkan oleh kombinasi keempat parameter (TEMP + SSC + SAL + SSH) dengan nilai p-value yang signifikan dan nilai CDE terbesar yaitu 9,36%. Hasil analisis memprediksi daerah potensial berdasarkan nilai HSI, habitat tuna mata besar berada pada posisi geografis 11°– 15° LS and 108°– 122° BT dengan nilai indeks rata-rata >0,75. Daerah potensial penangkapan ikan terbentuk berdasarkan rentang nilai optimum parameter oseanografi dari tuna mata besar. Daerah ini juga dipengaruhi oleh arus eddy yang memicu terbentuknya upwelling dan front konsentrasi klorofil-a serta front suhu. Peta PHI dapat menjadi salah satu alternatif untuk memprediksi daerah potensial penangkapan ikan., The prediction map of potential fishing zone is necessary to enhance the productivity of fishing efforts. These potential zones are influenced by oceanographic parameters such as temperature, phytoplankton abundance, sea surface height, salinity, and others. This study aims to analyze the relationship between bigeye tuna catch and oceanographic parameters as a basis for predicting potential fishing zones both spatially and temporally. The data of bigeye tuna catches were collected from PT. Perikanan Nusantara, along with oceanography parameters data obtained from satellite imagery, in-situ measurements, and modeling data. The bigeye tuna catch data were processed in the form of hook rate. Oceanography parameters data were extracted as monthly averages. The relationship between oceanographic parameters and fish catch results was analyzed using the Generalized Additive Model (GAM). The 'best-fit' model's suitability was evaluated based on the Pelagic Hotspot Index
Keyword: Generalized Additive Model, Pelagic Hotspot Index, Lepas Pantai Selatan Jawa, tuna mata besar

Judul: Prenilasi Senyawa β-Diketon sebagai Prekursor 3-Prenilflavon
Abstrak: One group of flavonoids that has not been commonly synthesized is 3- prenylflavone. The main objective of this experiment was to synthesize precursor of 3-prenylflavone. Firstly, β-diketone was synthesized via 4 steps from phenol as starting compound with total yield of 24%. Prenylation of β-diketone with prenyl bromide and K2CO3 base produced 4 fractions which were separated by using preparative thin layer chromatography (PTLC) with n-hexane:EtOAc (8:2) as the eluent. Fraction with Rf = 0.80 was further separated by using preparative PTLC with n-hexane:EtOAc (99:1) as the eluent. Two fractions were obtained with Rf = 0.38 and 0.42. The first fraction has been characterized as diprenylated β-diketone with 49% yield. Application of protecting group on the phenolic OH group in β- diketone are needed to obtain monoprenylated β-diketone which can be further cyclized into 3-prenylflavone
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