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Abstract-The aim of this paper is to analyze the determination of the potential fishing zones based on data mining approach. The algorithm utilized in this study is AGRID+, a grid density based clustering for high dimensional data. The case study area is in eastern Indian Ocean located at 16.56 -2 S and 100.49 -140 E. The algorithm is implemented in 7 phases, partitioning, computing distance threshold, calculating densities, compensating densities, calculating density threshold, clustering and removing noise. The clustering result is evaluated by the Silhouette index. The results of the study show that the best cluster formed at daily aggregate temporal with number of cell (m) = 14 and the number of cluster formed was 50 clusters. The constant execution time is in line with the increasing the value of m. From three different temporal aggregate, the daily aggregate is running relatively constant for various m value. To determine the potential fishing zones for different temporal aggregate can be achieved by applying the thresholding technique to the cluster result. Utilizing the data mining approach yielded a prominent 22 daily clusters identified as potential fishing zone.

Digital image forgery or forgery is easy to do nowadays. Verification of the authenticity of images is important to protect the integrity of the images from being misused. The use of a deep learning approach is state-of-the-art in solving cases of pattern recognition, the one is image data classification. In this study, image forgery detection was carried out using a deep learning-based method, the Convolutional Neural Network (CNN). The analysis of the different architecture of CNN has been done to show the effectiveness of each architecture. Two architectures were tested to know which one is more effective, architecture 1 has three convolution and pooling layers with 256 × 256 × 3 image input. While the other architecture has two convolution layers and pooling with 128 × 128 × 3 image input. The results show that the accuracy rate of the image forgery detection model in each architecture is around 80%. However, the validation accuracy is not more than 70%.

Pembentukan pos pemberdayaan keluarga (Posdaya) di Dusun Robyong, Poncokusumo, Kabupaten Malang bertujuan sebagai sarana meningkatkan pendapatan tambahan keluarga melalui pengolahan potensi alam yang ada dengan penerapan teknologi. Posdaya yang dibentuk berbasis masjid sehingga dapat memfungsikan masjid sebagai pusat sosio-ekonomi masyarakat, selain sebagai pusat keagamaan. Pada pengabdian masyarakat ini, Masjid Baitussalam menjadi proyek percontohan dalam pemberdayaan masyarakat berbasis masjid. Pelaksanaan kegiatan diawali dengan pendataan keluarga, sosialisasi, dan perumusan program kerja Posdaya sesuai potensi setempat. Setelah dilakukan diskusi dengan warga setempat, kegiatan pengolahan minuman kemasan sari buah markisa (Passiflora edulis) menjadi program kerja utama Posdaya, mengingat potensi buah markisa yang belum termanfaatkan secara optimal selama ini. Peserta program adalah warga sekitar Masjid Baitussalam, terutama ibu rumah tangga. Hasil pengamatan menunjukkan bahwa peserta berperan aktif pada program kerja Posdaya, serta didukung oleh pengurus Posdaya dan tokoh masyarakat. Produk yang dihasilkan pun direspons positif oleh pasar. Produk berhasil dipasarkan hingga ke luar Jawa sebagai buah tangan dari Desa Wonomulyo. Kegiatan wirausaha Posdaya ini dalam jangka panjang, diharapkan mampu berkembang menjadi kelompok usaha mikro dari Dusun Robyong dengan diversifikasi produk, penerapan strategi pemasaran, dan manajemen produksi yang semakin baik.