Judul: Diversity and Distribution of Amblypygi (Arachnida) in Siparat and Sipahang Caves, Bogor
Abstrak: Amblypygi merupakan ordo dari kelas Arachnida yang banyak ditemukan di dalam gua di kawasan karst. Sepertiga spesies Amblypygi yang telah diketahui merupakan hypogean dari gua. Penelitian ini bertujuan untuk mempelajari keragaman dan sebaran Amblypygi di dalam Gua Siparat dan Sipahang, Bogor. Amblypygi dikoleksi dengan metode hand collecting di sepanjang lorong gua. Pemetaan gua dilakukan dengan teknik forward method dari bottom to top. Amblypygi yang ditemukan di kedua gua tersebut ialah Stygophrynus dammermani dan Stygophrynus sunda. Kedua spesies tersebut terdistribusi secara acak di dalam gua. Amblypygi banyak ditemukan di dinding gua., Whip spiders (Arachnida, Amblypygi) are commonly found in caves in karst region. One-thirds of whip spider species are known to live on hypogean habitat. The research is aimed to study the diversity and distribution of Amblypygi in Sipahang and Siparat Cave, Bogor. The whip spider were collected by hand collecting method along the cave passage. Cave Mapping was done by forward technique method from bottom to top. The whip spiders found in both caves were Stygophrynus dammermani and Stygophrynus sunda. The whip spiders species distributed randomly inside the caves on the caves walls. Keywords: Amblypygi, new record, diversity, Stygophrynus dammermani, Stygophrynus sunda
Keyword: Amblypygi, catatan baru, diversitas, Stygophrynus dammermani, Stygophrynus sunda

Judul: Keanekaragaman Laba-laba (Ordo Araneae) di Daerah Mangrove
Abstrak: Spider (Order Araneae, Phylum Arthropod) has high adaptation to different types of environments. Therefore, spider is a cosmopolitan animal due to their ability to live anywhere. The spider can be found in terrestrial, arboreal, and some aquatic habitat. One of the aquatic habitat is mangrove. In every ecosystem, spider is an important component in the food chain i.e. as a predator. Thus, this research was aimed to study the diversity of spider in the mangrove vegetation in Wildlife Reserve and Protected Forest of Muara Angke in Jakarta and the mangrove vegetation in Kolaka, Southeast Sulawesi. Spider was collected using sweeping and beating techniques on the transect lines in each species of mangroves. There were 21 families and 32 genus of spider in two different locations which were Wildlife Reserve and Protected Forest Muara Angke, Jakarta. The dominant families of Araneae in Wildlife Reserve and Protected Forest were Mimetidae, Tetragnathidae, Salticidae, and Oecobidae. Mimetidae has highest abundance from all of the spider families found. Among the four families above, two most dominant genera were existed, i.e. Mimetus and Tetragnatha. Two families of spider which were found in Kolaka were in five genera and the dominant family was Tetragnathidae. The dominant genera spiders in Sonneratia Rhizopora, and Avicennia were Mimetus, Tetragnatha, and Ebo, respectively.
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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