Judul: Analisis Pola Konsumsi Mahasiswa Terhadap Pangan Asal Ternak (Studi Kasus: Mahasiswa Program Sarjana Fakultas Ekonomi dan Manajemen Institut Pertanian Bogor Tahun Masuk 2010)
Abstrak: FEM Students have a diverse characteristic, which rise to differences in consumption decisions, one of which is the consumption of the food from livestock. This study aims to analyze the consumption pattern of the food from livestock, identify the factors that influence the demand for the food from livestock, and analyze the level of price and income elasticity of the food from livestock with the application of the Almost Ideal Demand System (AIDS) model. The results of this studies showed that the higher the students income, the lower the total expenditure of FEM students allocated to consumption of food from livestock. The largest proportion of total expenditure of FEM students is used for the consumption of chicken meat, followed by cow’s milk, eggs of chicken, and beef. The own price variable, cross-price, total expenditure, gender, residence status tends to be dominant significantly affect the proportion of food from livestock expenditure on the real level (α) of 0,10. The own price elasticity of beef, chicken meat, and eggs is inelastic while the own price elasticity of cow’s milk is elastic. Most of cross-price elasticity have negative sign, its mean that the livestock has the complementary relationship with other food from livestock. The income elasticity for chicken meat, and eggs of chicken shown the value less than one, its mean that commodities are a staple goods. The income elasticity for beef and cow’s milk shown the value more than one, its mean that commodity is considered luxury good
Keyword: Bogor Agricultural University (IPB), food from livestock, elasticity, demand, consumption patterns, Almost Ideal Demand System (AIDS) Model

Judul: Dampak Kenaikan Harga Daging Sapi terhadap Pola Konsumsi Pangan Sumber Protein Hewani di Kabupaten Bogor.
Abstrak: One kind of food which has important role in developing the food security and create human resources who has healthy quality is food animal proteins. The government is targeting food consumption of animal protein as much as 7.2 g/capita/ day in 2014. Food sources of animal protein can be derived from livestock or fishery products like ruminant meat, poultry, fish, eggs, and milk. This study aims to analyze the pattern of household consumption in Bogor Regency, analyze the level of demand elasticity, and perform simulations to analyze the impact of higher prices for meat. The data used comes from SUSENAS 2012 with 1125 households in Bogor Regency. The analytical method used was descriptive analysis and model of Almost Ideal Demand System (AIDS). The analysis showed that the type of ruminant meat consumption group was very inelastic and have a lower level of consumption than other animal protein foods. People in Bogor Regency consume more fish than ruminant meat. Increasing the prices of beef could make other public consumption for food source of animal proteins is down. Food protein consumption for the people of Bogor regency have met the target set by the government
Keyword: SUSENAS, inelastic, consumption patterns, animal protein, AIDS

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