Judul: Sifat Mekanis Polipaduan Polistirena-Pati Menggunakan Zat Pemlastis Epoksida Minyak Jarak Pagar
Abstrak: Polyblend is a physical mix between two or more polymers resulting in different properties to get degradable polymer and better mechanical properties. Starch is degradable polymer, but polystyrene is synthetic polymer which is difficult to degrade. Polyblend polystyrene-starch will make the polymer easily to degrade but low mechanical properties. To increase the mechanical properties of polyblend, the plasticizers is needed. The plasticizers in this experiment was epoxide of jathropha oil. The polyblend of polystyrene-starch was prepared with various compositions, i.e. 0; 2.5; 5.0; 7.5; and 10.0%. The density of the polyblend decreased with the addition of the plasticizers. The reduction of density shown that jathropha oil epoxides can be used as plasticizers. The mechanical properties showed that the additions of plasticizer could increase the elongation and reduce the tensile strength. Thermal analysis using differential scanning calorymetry showed that glass transition temperature decreased from 95.37 ºC to 91.79 ºC.
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Judul: Pembuatan dan Pencirian Polipaduan Poliasamlaktat-Lilin Lebah
Abstrak: Nondegradable plastic waste could cause an environment problem due to its difficulty in degradation. The development of biodegradable polylacticacid (PLA)-beeswax polyblend is necessary to give alternatives of nondegradable plastic. PLA-beeswax polyblend were produced by mixing PLA and beeswax solution with various compositions namely 40:60, 50:50, 60:40, 70:30, and 80:20 by weight. The polyblends were tested for their tensile strength, density, fourier transform infrared spectroscopy (FTIR), and scanning electron microscope (SEM). PLA-beeswax polyblends were white in color. Tensile strength, elongation, and density increases as increasing of PLA constituents. FTIR spectrum of polyblend PLA-beeswax 80:20 did not show any new functional groups, meaning that the film was merely a physical mixture. Structure of the polyblend film revealed a homogenous film.
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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