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: Aplikasi Model Maximum Entropy untuk Pendugaan Daerah Penangkapan Ikan di Laut Jawa
Abstrak: Laut Jawa adalah salah satu wilayah pengelolaan perikanan (WPP) yang memiliki potensi sumber daya ikan yang melimpah di Indonesia. Penangkapan ikan dapat dioptimalkan dengan pengembangan sistem informasi daerah penangkapan ikan. Penelitian ini bertujuan untuk memprediksi daerah penangkapan ikan dengan menggunakan model Maximum Entropy (MaxEnt). Data yang digunakan dalam penelitian ini adalah data lingkungan yaitu konsentrasi klorofil-a dan suhu permukaan laut (SPL) tahun 2020 di Laut Jawa dan data posisi kapal penangkap ikan. MaxEnt menunjukkan hasil yang cukup baik dengan nilai AUC 0.742. Kurva respons menunjukkan probabilitas tertinggi distribusi kapal berada pada konsentrasi klorofil-a pada kisaran 0.2 – 1.25 mg/m3 dan SPL pada kisaran 27.25 – 30.7°C. Peta kesesuaian habitat sebaran kapal menunjukkan bahwa sebagian besar distribusi kapal ikan berada pada nilai Habitat Suitability Index (HSI) 0.5 – 0.8. Daerah penangkapan ikan potensial berada pada daerah tengah dan utara Laut Jawa mendekati perairan selatan Pulau Kalimantan.
Keyword: DPI, Laut Jawa, MaxEnt, Distribusi Kapal

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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