Judul: The fern diversity of South East Sulawesi
Abstrak: Floristic study has played an important role in expressing the diversity of plant species in a region. However such fern explorations are very rare, thus the data and information about ferns in Sulawesi Island particularly in South East Sulawesi is very limited. The objectives of this study is to know the diversity of fern species in South East Sulawesi region. The methods used in this study were exploration and description based on morphological characters. The study had been carried out in three locations, namely Mekongga Mountains, Uluiwoi and Wawonii Island. Materials used for this study comprised of herbarium specimens in the Herbarium Bogoriense and new specimens collected from exploration during this research in South East Sulawesi. This study documented 189 species belonging to 70 genera and 29 families. There are 173 species new collections of Pteridophyte from South East Sulawesi, 28 species of it are new from Sulawesi for Herbarium Bogoriense. Two species presumed as new species and 4 species are new records from Sulawesi. Fern in South East Sulawesi varied in their habit, rhizome, hair/scale, fronds and soral characters. Based on the habitat, there are nine types of ferns. Among three locations the greatest species diversity found in Mekongga Mountains (126 species), followed by Wawonii Island (87 species) and Uluiwoi (76 species). The degree of similarity of fern species at three different locations is low. Fern species are also used by local people for foods, medicine, ornamental plants, handycraft and construction. The identification key to the families, genera and species are provided.
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Judul: The Fern of West Timor  East Nusa Tenggara
Abstrak: Based on this study there are 58 species belonging to 36 genera. They are Achrosticum (1 species), Adiantum (5 species), Amphineuron (3 species), Asplenium (4 species), Athyrium (1 species), Belvisia (1 species), Botrycium (1 species), Ceratopteris (1 species), Christella (4 species), Ctenitis (1 species), Ctenopteris (1 species), Cyathea (1 species), Davallia (2 species), Dennstaedtia (1 species), Diplazium (2 species), Doryopteris (1 species), Drynaria (2 species), Dryopteris (1 species), Gleichenia (1 species), Heterogoniurn (1 species), Humata (1 species), Lycopodium (1 species), Microlepia (1 species), Microsorium (1 species), Nehprolepis (2 species), Oleandra (1 species), Platycerium (1 species), Pleocnemia (1 species), Pteris (5 species), Pyrrosia (1 species), Sellaginela (1 species), Selliguea (1 species), Sphaerostephanos (2 species), Tectaria (1 species), Trichomanes (1 species), and Vittaria (1 species).
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Judul: Model Interaksi Gravitasi Banyak Massa Untuk Prediksi Dinamika Harga Bitcoin.
Abstrak: Penelitian ini ditujukan untuk memprediksi harga Bitcoin dengan menggunakan metode pengoptimuman berbasis interaksi gravitasi banyak massa (Gravitational Search Algorithm atau GSA) dengan parameter yang akurat berdasarkan nilai error yang kecil. Nilai parameter yang diperoleh dari metode GSA disubstitusi pada persamaan Gaussian untuk meramalkan harga Bitcoin selanjutnya. Hasil peramalan divalidasi dengan uji R2 dan uji MAPE. Hasil menunjukkan bahwa Model Gaussian Termodifikasi Orde 5 memberikan hasil terbaik dengan nilai R2 sebesar 0.8824 dan nilai MAPE minimal sebesar 8.4672%. Hasil simulasi dengan variasi rentang nilai parameter [0,8000], [0,800] dan [0,80] yaitu model 3 memberikan nilai R2 terbesar yaitu 0.89865, namun untuk nilai MAPE terkecil didapatkan dari simulasi dengan variasi rentang parameter [0,10000], [0,1000], dan [0,100] yaitu model 1. Hasil interpolasi, prediksi dan validasi harga Bitcoin menggunakan data harian selama 1 tahun dengan model 1 dan model 3 menunjukkan hasil yang kurang baik, ditunjukkan dengan nilai R2 yang kurang dari 0.50. Model 3 dapat dinyatakan sebagai model yang lebih baik dari model 1, dengan nilai koefesien deterministik (R2) sebesar 0.38944 dan nilai MAPE sebesar 21.21732% dibandingkan dengan model 1, dengan nilai R2 sebesar -0.43704 dan nilai MAPE sebesar 33.75404%.
Keyword: bitcoin, prediksi, GSA, gaussian