Judul: Pengembangan hidden semi markov model dengan distribusi durasi state empiris untuk prediksi struktur sekunder protein
Abstrak: This research aimed to develop Hidden Semi Markov Model (HSMM) with longduration distribution of state empirically and theoretically to predict protein secondary structure. Data used in this study are subset data taken from database of secondary protein structure in DSSP program with three secondary protein structures of alpha-helix (H), betha-sheet (B), and coil (C). Accuracy of the predicted protein structure with HSMM is compared with Hidden Markov Model (HMM) standard. The results showed that the HSMM generally provides a prediction accuracy 72. 1% , 35.3% and 63,1% for H, B and C respectively. The accuracy of standard HMM is 98.4% for (B), 1% for (B) and 6.4% for (C). The use of empirical state duration distribution gives better accuracy on average compared a theoretical state duration distribution. Use of the empirical state duration distribution provides accuracy 71.2% for (H), 37.1% for (B) and 64.0% for (C). Use of theoretical state duration distribution provides accuracy 75% for (H), 30.2 % for (B) and 60.6% for (C). Less accuracy in betha-sheet prediction with HSMM was caused by high of the dispute between distribution in training data and testing data.
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Judul: Prediksi Struktur Sekunder Protein dengan Metode Support Vector Machine
Abstrak: Protein merupakan salah satu dari biomolekul raksasa yang berperan sebagai elemen penyusun utama makhluk hidup yang dibentuk dari asam amino. Secara hierarki, protein terbagi menjadi empat tingkat: struktur primer, struktur sekunder, struktur tersier, dan struktur kuartener. Struktur sekunder protein adalah struktur yang dibentuk oleh rangkaian asam amino yang akan membentuk struktur tiga dimensi serta memiliki informasi mengenai struktur tersier dan fungsi protein. Penelitian ini menggunakan data yang berjumlah 277 389 residu protein dari data kategori enzim. Ekstraksi ciri yang digunakan yaitu position specific scoring matrix (PSSM) profile dan fitur kimiafisik. Penelitian ini melakukan prediksi struktur sekunder protein menggunakan metode support vector machine melalui pengenalan pola sekuens asam amino. Hasil penelitian menunjukkan bahwa Q3 score terbaik diperoleh sebesar 93.16% oleh dataset yang memiliki 260 fitur dengan kernel radial. Penambahan fitur kimiafisik tidak memberikan pengaruh yang signifikan terhadap akurasi prediksi model.
Keyword: protein, prediksi, struktur sekunder, support vector machine

Judul: Etno-ornitologi Burung Kancilan Flores (Pachycephala nudigula nudigula Hartert 1897) pada Masyarakat Lio di Taman Nasional Kelimutu
Abstrak: Bare-throathed whistler is one of several wild birds in Kelimutu National Park (TNKL) that is believed to have correlation with Lio Community‟s belief. The estimation of Bare-throated whistler‟s population in Kelimutu National Park was about 186.41-1668.07 individual by the density about 0.0389-0.3481 ind/ha with the sex ratio 6.2:1 that spreaded on area with height of 1100 – 1600 masl. The characteristic of Bare-throated whistler‟s habitat is a natural forest with various canopy level, high plant species diversity and high density. The habitat characteristics related to the availability of food in the form of insects, cover, safety and a nesting site. Generally the most of Lio community knew the existence of Bare-throated whistler directly or from their parents‟ stories. The perceptions of Lio Community to the Bare-throated whistler‟s existence is generally support the conservation efforts. The perceptions construct the Lio Community‟s atitude that tend to be conservative to the existence of Bare-throated whistler. The attitude shown by their behavior that not cathing the Bare-throated whistler from the TNKL‟s area or the outside. The attitude have an important role to the existence of Bare-throated whistler in Kelimutu National Park‟s area.
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