Judul: Studi Perilaku Simakobu (Simias concolor Miller, 1903) dan Pola Penggunaan Habitat di Area Siberut Conservation Programme, Sumatera Barat
Abstrak: Simakobu was one of endemic primates in Siberut island that become attention of Siberut Conservation Programme. This species was threatened mainly by heavy hunting and commercial logging by local people. This study aims to determine the behavior of simakobu and habitat use. This research was conducted from July 2011 until January 2012 in SCP. Pattern of behavior data collected via Scan Sampling. The results found that simakobu in exploiting their food used more sitting posture than hanging posture. Actively grooming often existed between female and juvenile and passively grooming existed on male. The highest percentage calls of simakobu was occurred at 07.00-08.00 am with loud call duration in 20-30 second. Simakobu often used space between the first branch of the tree to the canopy at 21-25 meter in height.
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Judul: Ketersediaan Pakan antar Kelompok Simakobu (Simias concolor) di Hutan Peleonan, Siberut Utara, Kepulauan Mentawai
Abstrak: The endemic primate Simakobu (Simias concolor) in Mentawai is endangered due to habitat decreases. The purpose of this study was to discover the availability of food trees in the home ranges of two groups of Simakobu, Aldran group and Aldo group. Each of them consists of a different number of group members. Although Aldran group has more group members, Aldo group’s home ranges is 0,4 Ha wider than Aldran’s. However, the variety of food trees in Aldran’s home range is 11.8 % higher than Aldo’s. There are 113 species of trees inventoried in Peleonan forest and 53 types of which are food sources of Simakobu. Coccoseras borneensis is the most dominant food tree available in the both regions. Simakobu’s preferences are the young leaves and fruits. In general, the food abundance and variety in both home ranges are still adequate. The less varied food trees available in Aldo’s area and the group composition are expected to be the driving factors that made Aldo group explores more and eventually has a wider home range.
Keyword: the availability of food tree, Simias, simakobu, Siberut, Mentawai Islands

Judul: COVID-19 Pneumonia Identification Based  Computed Tomography Imaging Using Deep Transfer Learning
Abstrak: Dunia sedang dilanda pandemi COVID-19 yang menyebabkan pneumonia hingga kematian pada penderitanya. Karena tingkat penyebaran virus SARS-CoV 2 yang tinggi, maka pemberian tes harus dilakukan secara cepat dan masif agar penderita dapat diisolasi sesegera mungkin. Pendekatan standar yang digunakan untuk mendeteksi COVID-19 saat ini adalah RT-PCR. Namun, terdapat beberapa kekurangan seperti sensitivitasnya hanya sebesar 60-70% dan biayanya relatif mahal. Penelitian ini bertujuan untuk membangun metode pendeteksian alternatif COVID-19 berbasis citra Computed Tomography menggunakan teknik deep transfer learning. Transfer learning dilakukan menggunakan pre-trained model DenseNet-201. Pembuatan model terdiri dari beberapa tahapan yaitu praproses data, pembagian data, augmentasi data, hyperparameter tuning, pembangunan model klasifikasi deep transfer learning, dan evaluasi. Model yang telah dibangun memiliki performa yang cukup baik dalam mengidentifikasi paru-paru dengan pneumonia COVID-19 dan paru-paru sehat dengan akurasi sebesar 93,41%, presisi sebesar 94,19%, sensitivitas (recall) sebesar 93,10%, dan spesifisitas sebesar 93,75%., The world is being stricken by the COVID-19 pandemic, which can cause pneumonia, even the worst case is death. Due to the high level of spread of the SARS-CoV-2 virus, rapid and massive tests must be done. The infected people need to be isolated as soon as possible. The gold standard used to detect COVID-19 is RT-PCR. However, there are some drawbacks, this method only has 60-70% sensitivity, and has a relatively high cost. This study aims to develop an alternative detection method for COVID-19 based on Computed Tomography images using deep transfer learning techniques. Transfer learning is carried out using the pre-trained DenseNet-201 model. The process consists of several stages; data preprocessing, data splitting, data augmentation, hyperparameter tuning, developing a deep transfer learning classification model, and evaluation. The model that has been built has a fairly good performance in identifying lungs with pneumonia COVID-19 and healthy lungs with accuracy 93,41%, precision 94,19%, sensitivity (recall) 93,10%, and specificity of 93,75%.
Keyword: computed tomography, covid-19, densenet-201, transfer learning