Judul: Imposeks dan Logam Berat pada Keong Laut Strombidae Khususnya di Pulau Tinjil, Provinsi  Banten
Abstrak: Pencemaran logam berat yang disebabkan oleh tumpahan batu bara di Pulau Tinjil dapat memberikan dampak kronis terhadap biota laut. Salah satu efek logam EDC pada biota laut adalah munculnya gejala imposeks pada gastropoda, yaitu tumbuhnya alat kelamin jantan pada betina beberapa jenis gastropoda. Penelitian ini bertujuan menganalisis fenomena imposeks pada keong laut Strombidae, serta mengidentifikasi logam berat yang berpotensi menjadi pemicunya. Penelitian dilaksanakan di Pulau Tinjil, Kabupaten Pandeglang, Banten pada bulan Juni 2020. Pengambilan contoh dilakukan secara purposive sampling berdasarkan daerah yang diduga tercemar oleh pelayaran dan tumpahan batu bara dari kapal karam, namun karena sedikitnya sampel yang didapat, penelitian ini lebih banyak menggunakan data sekunder. Hasil studi menunjukkan tidak ditemukannya gejala imposeks maupun kandungan logam berat EDC yang tinggi pada populasi Harpago chiragra di Pulau Tinjil. Berdasarkan data sekunder dari penelitian di daerah lain, keong Strombidae yang menunjukkan gejala imposeks terdapat pada spesies Strombus gigas, Strombus canarium, dan Canarium urceus yang mana senyawa pemicunya organometal-Sn Tributiltin., Heavy metal pollution caused by coal spills on Tinjil Island can have a chronic impact on marine life. One of the effects of EDC metals on marine biota is imposex symptoms on gastropods, namely the growth of male genitalia in females of several types of gastropods. This study aims to analyze the imposex phenomenon on Strombidae sea conch, as well as identify heavy metals that have the potential to be the trigger. The study was conducted on Tinjil Island, Pandeglang Regency, Banten in June 2020. Sampling was carried out by purposive sampling based on areas suspected of being contaminated by shipping and coal spills from shipwrecks, but due to the small number of samples obtained, this study used mostly secondary data. The results of the study showed that neither imposex symptoms nor high EDC heavy metal content were found in the population of Harpago chiragra on Tinjil Island. Based on secondary data from studies in other areas, Strombidae snails that show symptoms of imposex are found in the species Strombus gigas, Strombus canarium, and Canarium urceus which trigger compound organometal-Sn Tributyltin.
Keyword: Coal, heavy metal, imposex, sea conch, Tinjil Island

Judul: Heavy Metal Content (Pb, Cd, Cu) in Sediments in the Tinjil Island Waters, Pandeglang, Banten
Abstrak: Pulau Tinjil yang terletak di Kecamatan Cikeusik, Kabupaten Pandeglang, Provinsi Banten mengalami peristiwa penting pada tahun 2018, yakni kapal tongkang pengangkut batubara mengalami kecelakaan, sehingga batubara tumpah ke perairan. Tumpahan batubara ini akan terakumulasi di dasar perairan dan berpotensi mencemari Perairan Pulau Tinjil. Penelitian ini bertujuan untuk menduga tingkat pencemaran logam berat (Pb, Cd, Cu) pada sedimen di Perairan Pulau Tinjil, Pandeglang, Banten. Penelitian dilakukan pada bulan Juni 2020 dengan pengambilan sampel di Perairan Pulau Tinjil, Pandeglang, Banten. Data yang didapat berupa kandungan logam berat (Pb, Cd, Cu) pada sedimen yang dianalisis menggunakan alat ICP-OES. Hasil pengukuran logam Pb memiliki konsentrasi berkisar 0,005 mg/kg sampai 3,26 mg/kg. Hal tersebut menunjukkan bahwa logam Pb pada ketiga stasiun di bawah baku mutu. Logam Cd memiliki konsentrasi 4,12 mg/kg sampai 5,94 mg/kg. Hasil tersebut menunjukkan logam Cd memiliki konsentrasi melebihi baku mutu. Logam Cu memiliki nilai konsentrasi 2,37 mg/kg sampai 5,34 mg/kg. Hasil tersebut menunjukkan bahwa logam Cu pada ketiga stasiun di bawah baku mutu. Indeks geoakumulasi pada logam Pb dan Cu memiliki nilai negatif. Hal tersebut menunjukkan logam Pb dan Cu ada dalam kategori tidak tercemar. Indeks geoakumulasi logam Cd memiliki nilai 0,966 sampai 1,121 yang menunjukkan bahwa sedimen tercemar oleh logam Cd dalam rentang ringan dan sedang., Tinjil Island, located in Cikeusik District, Pandeglang Regency, Banten Province, experienced an important event in 2018, where a barge carrying coal had an accident, resulting in coal spilling into the waters. This coal spill will accumulate at the bottom of the waters and have the potential to pollute the waters of Tinjil Island. This research aims to estimate the level of heavy metal pollution (Pb, Cd, Cu) in sediments in the waters of Tinjil Island, Pandeglang, Banten. The research was conducted in June 2020 by taking samples in the waters of Tinjil Island, Pandeglang, Banten. The data obtained was in the form of heavy metal content (Pb, Cd, Cu) in the sediment which was analyzed using the ICP-OES tool. The results of measurements of Pb have concentrations ranging from 0.005 mg/kg to 3.26 mg/kg. This shows that the Pb at the three stations is below the quality standard. Cd has a concentration of 4.12 mg/kg to 5.94 mg/kg. These results show that Cd has a concentration that exceeds the quality standard. Cu has a concentration value of 2.37 mg/kg to 5.34 mg/kg. These results show that the Cu at the three stations is below the quality standard. The geoaccumulation index for Pb and Cu has negative values. This shows that Pb and Cu are in the uncontaminated category. The geoaccumulation index for Cd has a value of 0.966 to 1.121, which shows that the sediment is polluted by Cd in the light and moderate range.
Keyword: Logam Berat, Sedimen, Batubara, Pulau Tinjil

Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation