Judul: Sifat Fisis-Mekanis Papan Partikel dari Serbuk Bulu Domba, Serbuk Gergaji, dan Serutan Kayu Sengon (Paraserianthes falcataria)
Abstrak: Indonesian or local sheep is classified as a meat type. It is not classified as a wool type because the wool yielded are coarse, so it is not optimally used. Wool can be added in the making of particle board because of its strength, pressure resistance, good insulator, and sound absorption properties. Paraserianthes falcataria (Sengon) saw dust and wood shaving are wood processing waste which can be used as particle board because it contains lignocellulose fiber. This research was to find the best formulation of wool dust (chopped wool at the length of 2-3 mm), saw dust, wood shaving and adhesive concentration used (urea formaldehida). The experiment will be conducted as a factorial experiment with two factors. The factors are wool dust, sawdust, and wood shaving combination (0% :60% : 40% ; 10% : 50% : 40% ; 20% : 40% : 40% ; 30% : 30% : 40%) and adhesive concentration (16, 18, and 20%). The parameter scored are the density, moisture content, thickness swelling, water absorbing capacity, modulus of rupture, modulus of elasticity, internal bond and strength of screw holding power. Any significant differences data will be further analyzed with Polynomial Orthogonal. The result showed that wool dust significantly improved the water absorbing capacity, modulus of rupture, and modulus of elasticity. The urea formaldehida adhesive significantly improved the density and water absorbing capacity. The interaction of two factors significantly improved the thickness swelling, internal bond, and strength of screw holding power. The data from this research showed that the number of density between 0,69-0,78 g/cm3, moisture containt with mean 6,49%, thickness swelling between 32,36-83,9%, water absorbing capacity beetween 89,04-169,15%, modulus of rupture between 68,4- 121,8 kgf/cm2, modulus of elasticity between 671,84-1381,51 kgf/cm2, internal bond between 0,44-1,3 kgf/cm2, and strength of screw holding power between 6,0-22,3 kg. To find out the proportion of the product that suite the JIS A 5908 and SNI 03-2105, the distribution of the data was estabilished and the area under the normal curve (represent the proportion) was examined. From the stand point of density, moisture containt and modulus of rupture more than 95% of the product suited to the standards. However, from the stand point of thickness swelling, modulus of elasticity, internal bond, and strength of screw holding power none of the product suited to the standards.
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Judul: Sifat fisis mekanis papan partikel dari bulu domba, serbuk gergaji kayu sengon (Paraserianthes falcataria) dan plastik polyprophylene
Abstrak: Vool of Indonesian or local sheep had been used as particle board material, however not all of its physical and mechanical traits, especially its texture match the SNI (Standar Nasional Indonesia) and JIS (Japanese Industrial Standard). Paraserianthes falcataria (sengon) can be used as practice board materials, because it contain lignocellulose fiber.
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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