Judul: Daya dan kestabilan buih putih telur itik tegal pada umur telur dan level penambahan cream of tartar yang berbeda
Abstrak: The application of duck eggs in making of cake is very limited. It's caused by the egg white foaming and its stability of duck egg lower than chicken egg. This research was designed to study whether egg white foaming and its stability of Tegal ducks egg in different age could be increased with addiig cream of tartar in a given concentration before mixing. This research was held in Poultry Science Division, Department Animal Production and Technology, Faculty of Animal Science, Bogor Agriculture University, from Mei-September 2005. As first treatment were egg age which contained 4 factor levels; frest, 7, 14, 21 days of egg. Second treatment were cream of tzear addition, that also contained 4 facor levels; 0; 0,s; 1,6; 2,4%. Data which is obtained were analyzed by descriptive method. The result showed that storage and adding cream of tartar treatment's affected foaming ability in egg white. In fresh egg, cream of tartar didn't increase more foaming ability. Average of pH egg white is 8,05*0,25. Adding cream of tartar 0,8% could decrease of pH and made highest foaming stability. White egg seven days had highest foaming ability and its stability with addiig cream of tartar 0,8%. Egg white foaming and its stability 14 days got highest with adding cream of tartar 1,6%, but in 21 days cream of tartar didn't make increase foaming ability and its stability. Storage caused increase of pH and must adding more cream of tartar to increase foaming ability and its stability. Adding cream of tartar in 21 days didn't make increase foaming ability and its stability because were decrease of egg quality. Keywords : Egg White, Egg Age, Cream of Tartar, Foaming, Stability
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Judul: Daya dan kestabilan buih putih telur itik tegal pada umur telur dan taraf penambahan asam sitrat yang berbeda
Abstrak: The usage of ducks egg for cake mixes is still rare since its foaming is lower than chicken egg. Albumen foaming and its stability are affected by egg age and chemical addition or stabilization. Chemical substance which can be used is citric acid. This research was designed to study whether albumen foaming and its stability of Tegal ducks egg in different age could be increased by adding citric acid in a given concentration before mixing. The result was hoped to supply information about the best albumen foaming and stability formed by Tegal duck egg by adding citric acid in different egg age. The research used Block Randomized Design factorial pattern. As first treatment were egg age which contained 4 factor levels; fresh, 7, 14 and 21 days of egg. Second treatment were citric acid addition, that also contained 4 factor levels; 0, 0.8, 1.6 and 2.4%. Egg which is collected in different days was used as block. The longer egg shelf life, the more block formed. Data which is obtained were analyzed by descriptive approach. The highest albumen foaming and its stability of Tegal ducks egg without citric acid addition was in fresh egg, which was 451.83±122.18% and 3.43±0.66% in leak percentage. Albumen foaming and its stability was decrease as egg age longer. Citric acid addition until 2.4% could increase albumen foaming and its stability only in fresh egg, with addition level best in 0.8%. This addition established foaming as much as 683.33±14.43% and 2.66±1.41% leak percentage.
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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