Judul: Permintaan energi rumah tangga di pulau Jawa
Abstrak: Reducing of subsidies would increase energy prices that affect the level of energy consumption and consumer welfare. Analyze the behavior of households in Java in consuming energy was the aim of this study, this include their price elasticity, income elasticity, and cross elasticity of energy commodities. The methode of this study is the linear approximate version of the almost ideal demand system (LA-AIDS) model using data from the National Socio-Economic Survey (SUSENAS) covering the period from 2007 to 2010 for household in Java along with the kerosene’s conversion to gas program undertaken by the government. The own price elasticities of energy (except for the electricity) showed that they are elastic so the increase of their price will effectively reducing its consumption. While the cross elasticities showed that the energy comodities are substitute each other, but in very low level. The kerosene’s conversion to gas has been shifting household kerosene consumption in Java into commodities LPG, city gas, and coal.
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Judul: Analisis konsumsi dan penyediaan energi dalam perekonomian indonesia
Abstrak: The general objective of this study develop a model of energy consumption by users and the energy supply by energy type in the Indonesian economy. The model used is a simultaneous equation model with two stage least square method. The design of this model uses the assumption that along with economic progress the need for energy has increased so that the necessary supply of energy is greater, and vice versa. The main findings of this study are: First, world oil prices, domestic energy prices, GDP, and exchange rates are the main factors that influence the consumption and supply of energy in Indonesia. Second, the increase in world oil prices and declining goverment expenditure of oil subsidies decrease consumption and supply of energy in Indonesia. The apresiation of the IDR to US Dollar increase consumption and supply of energy in Indonesia. And third, the results of forecasting analysis show overall energy consumption by all sectors tend to increase, except for biomass consumption by the industrial and total of biomass consumption. In addtion, supply of energy tends to increase with smaller rate the increasing of energy consumption. In the short term, it should be an effort to increase energy utilization efficiency and productivity through conversion using fuel oil by gas and the reduction of subsidies oil in energy prices gradually, accompanied by efforts to increase economic growth, among others, by applying a low interest rate policy and maintain stable exchange rates. In the medium term, some efforts are required to increase investment from the aspect of production, processing, and distribution of fossil energy, and effort-based energy conversion using fuel oil by industry sector into other energy types. Along with that, efforts to increase the number and capacity of oil refineries, gas and electricity generation are required to be done to reduce the level of dependence on the final energy that comes from imports. In the long term, the implemention of green energy development strategy, which shifts the energy use sourced from unrenewable resources to energy use that are renewable resources, such as utilization of water energy, wind, biomass, biodiesel, biogas and other sources of sustainable energy.
Keyword: Energy consumption, Economic growth, Water energy, Biomass, Biodiesel, Biogas

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