Judul: Optimasi Pemanfaatan Pulau-Pulau Kecil Berbasis Daya Dukung Bagi Peruntukan Wisata (Kasus Gugus Pulau Sapeken Kecamatan Sapeken Kabupaten Sumenep)
Abstrak: Sustainable tourism development in Sapeken archiplego designed with characteristic area and carrying capacity. Based on this fact, the objectives of this research are 1) determine socio ecological system in Sapeken archipelago; 2) arrange suitability area for tourism in Sapeken archipelago; 3) estimate tourism sustainability by calculate area potential to optimize resource utilization; 4) Optimizing Sapeken archipelago used for tourism. Analyze status of socio-ecological systems are using SES methods; suitability of area for tourism were analyzed using matrix tourism suitability; tourism carrying capacity estimated using the carrying capacity utilization (DDP) and touristic ecological footprint (TEF) approaches; and tourism optimizing utilization using emergy analysis. Results showed: 1) the status of socio-ecological systems in Sapeken archipelago can provide ecosystem services for ecotourism development; 2) Sapeken archipelago according suitability level of tourism has suitable categorie (S) which are to dive tourism (164.42 ha), snorkeling tourism (361.56 ha), mangrove tourism (3927.09 ha) and beach tourism (3.89 ha), 3) The development of ecotourism in Sapeken archipelago are sustainable (DDP> EF), and 4) Long term sustainability tourism in Sapeken archiplego (ESI = 10) will be achieved by input addition as 4,35 x 1022 - 6.4 x 1024 sej / year.
Keyword: 

Judul: Small Island Resources Use for Sustainability Tourism (Case Study Sepanjang Island, Sumenep Regency, East Java Province)
Abstrak: The needs of coastal and marine tourism, especially in small island continues to rise. But tourism have risk unsustainable if not planned properly and not based on carrying capacity (CC). The aims of this study are to identify degree of use and coastal resources management, estimated suitability and opportunity for sustainable tourism, and measure ecological CC and sustainable tourism management on Sepanjang Island. This study uses a hybrid analysis method that include Participatory Rural Appraisal (PRA), tourism suitability index, Recreation Opportunities Spectrum (ROS) and Touristic Ecological Footprint (TEF). The results of this study indicate that the degree of resources use of Sepanjang Island is still low and management on resources not yet optimal, although Sepanjang Island has been set as Marine Protected Area (MPA) since 2010. Sepanjang Island has suitability resources for tourism of beach 26.35 km, mangrove 3,319.75 ha, seagrass 85.06 ha, snorkeling 134.95 ha and diving 107.36 ha. Sepanjang Island is divided into 6 classes of ROS are urban 557.1260 ha (0.92%), Rural 3,086.6240 ha (19%), Frontcountry 4,104.0390 ha (6.75%), Backcountry 26,018.7110 ha (42.80%), Remote 26,997.9960 ha (44.41%) and Wilderness 33.7920 ha (0.06%). Whereas TEF values Sepanjang Island is 0.162504 ha with CC is 159,743 capita.
Keyword: 

Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak