Judul: Foam Ability and Stability of Hen`s Egg White in The Different Egg`s Age and Level of Cream of Tartar Additon
Abstrak: Hen`s egg, one of food substance, has high nutrition. Today many food industries such as cake and bread use hen`s egg white as raw material, because its foam ability is higher than Tegal duck`s egg white. unfortunately its foam ability and stability decrease along with the increasing of egg`s age. This research was designed to find out the effect of percentage level cream of tartar addition on foam ability and stability to different egg`s age. The result was expected to give information about the best hen’s egg white foaming and its stability. This research comprises two treatments. First, 4 factor levels; fresh, 7, 14 dan 21 days of age. The second treatment was cream of tartar addition, that also consisted of 4 factor levels; 0, 0.8, 1.6, and 2.4%. Foam ability and stability of hen’s egg white were analyzed by descriptive method. The result was hoped to supply information about the best albumen foaming and stability formed by hen`s egg by adding cream of tartar in the different egg age. At first treatment were egg age which contained 4 factor levels; fresh, 7, 14 dan 21 days of age. Second treatment were cream of tartar addition, that also contained 4 factor levels; 0, 0.8, 1.6, and 2.4%. Foam ability and stability hen’s egg white were analyzed by descriptive method. The highest foam ability reached by fresh egg without cream of tartar. The highest foam stability reached by hen’s egg with 14 days time in storage and added by 0,8% cream of tartar.
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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