Judul: Pengaruh Kondisi Ekstrusi terhadap Perubahan Kandungan Serat Pangan: Studi Meta-Analisis
Abstrak: Dietary fiber is part of a plant or carbohydrate analog resistant to digestion and absorption in the human small intestine but can be fermented in the large intestine. Based on its solubility, dietary fiber consists of soluble dietary fiber and insoluble dietary fiber. Soluble dietary fiber includes pectin, mucilages, and gums; insoluble dietary fiber contains cellulose, hemicellulose, and lignin. Quality food fiber has a minimum presentation of 10% soluble dietary fiber, but insoluble dietary fiber generally has a more incredible amount than soluble dietary fiber. Several methods, including extrusion, can do increasing soluble dietary fiber. This study aims to determine the effect of extrusion conditions such as temperature, screw speed, and moisture content on dietary fiber content through a meta-analysis study. This study consisted of searched research questions, purchasing inclusion and exclusion extraction, literature search, data extraction, and statistical analysis using the OpenMEE application. Data analysis used the value of the Hedges'd effect size (standardized mean difference/SMD) with a 95% confidence interval (CI) collected in the random effects model. The results showed that the overall effect size of soluble dietary fiber was 3,845 with a p-value <0,001, meaning a significant increase. Insoluble dietary fiber shows an overall effect size value of -1,537 with a p-value <0,001, meaning a significant decrease. In contrast to total dietary fiber, the overall effect size was 0,244 and the p-value was 0,436, which did not show significant changes. This is because in the extrusion process, the β-1.4 glycoside bond is broken in insoluble fiber, so it undergoes a transition to soluble dietary fiber, so that the total change in dietary fiber does not occur significantly. Subgroup analysis showed that temperature, screw speed, and moisture content could improve the quality of dietary fiber. With an interval of temperature 100 < x ≤ 130◦C, screw speed of 100 < x ≤ 200 rpm, and moisture content of 5 ≤ x ≤ 20% showed an increase in soluble dietary fiber and a decrease in maximum insoluble dietary fiber.
Keyword: Extrusion, meta-analysis, polysaccharides, soluble dietary fiber

Judul: Mempelajari Pengaruh Pre-conditioner, Kecepatan Ulir, dan Substitusi Gandum Utuh Terhadap Ekstrusi
Abstrak: Extrusion cooking is the process of forcing a material to flow under a variety of conditions through a shaped hole (die) at a predetermined rate to achieve various resulting products. This research was done to learn the influences of pre-conditioner, screw speed, and whole wheat substitusion in extrusion process.Several extrusion treatments were studied, as follows: pre-conditioner treatment, wheat proportion (0 %, 5 %, and 10 %), and screw speed (350 rpm, 360 rpm, and 370 rpm). Pre-conditioner, wheat proportion, and screw speed found to affect the product in different ways. Pre-conditioning treatment increases the product’s moisture, gelatinisation degree, WSI (Water Solubility Index), WAI (Water Absorbtion Index), hardness and product’s length but it decreases the product’s bulk density. The higher wheat proportion between 0 % to 10 % increases gelatinisation degree, WSI, hardness, and bulk density. On the other hand, increasing of screw speed around 350 rpm to 370 rpm will increases gelatinisation degree, WSI, product’s length, hardness, but decreases product’s expansion degree.
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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