Judul: Poverty and Its Reduction Policy in the Western and Eastern Indonesia.
Abstrak: Reducing poverty is one of goals on the national development. In the effort of poverty reduction, government has run several programs. But, poverty is not reduced. It means that these programs have not effectively reduce poverty because it is homogenous for all regions. The purpose of this study: (1). Identify the factors that affect poverty in the Western and Eastern Indonesia, (2) Formulate poverty prevention policies in the Western and Eastern Indonesia. The methods which used are: descriptive and panel data analysis. Result of this study: (1). The are similarities causes of poverty in the Western and Eastern Indonesia, it is unemployment. (2). This study also shows that causes of poverty in the Western and Eastern Indonesia is different. Poverty in the Western Indonesia caused high population density especially in Java and Bali with education remains low and is predominately dependent on primary agricultural sector as the main source of livelihood. Poverty in the Eastern Indonesia caused lack of populations and inadequate availability of infrastructure. The difference may be the cause of poverty as proof that poverty reduction programs that are not effectively tackling poverty homogeneous. (3). Poverty reduction programs in the Western Indonesia can be done with transmigration and agro-industry business development, while in the Eastern Indonesia by increasing the number of population and infrastructure.
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Judul: Regional Poverty: An Analysis of Inclusive Growth in Indonesia
Abstrak: Various studies have shown a negative correlation between poverty and economic growth. However, poverty reduction in Indonesia has begun to slow down, accompanied by income inequality. This study aims to determine how economic growth can benefit poor people and focus on the relationship between inclusive growth and women. To determine the extent of inclusive growth in Indonesia, a calculation is made using the Poverty Equivalent Growth Rate (PEGR) and social mobility curve from the 2015-2019 national socioeconomic survey data (Susenas) sourced from Statistics Indonesia. Next, the panel data regression determines what factors drive inclusive growth. Analysis of the data demonstrated that, in general, Indonesia had not shown inclusive growth in 2015-2019. Only a few provinces in Indonesia experienced inclusive growth during that timeframe. The results indicate that education, contributions of women in economics, and household size can drive economic growth so that inclusive growth can be achieved. On this basis, it is recommended that the Indonesian government pay more attention to handling poverty, primarily through the education and empowerment of women. Further research is required to identify other factors that could influence Inclusive growth in Indonesia.
Keyword: gender, inclusive growth, poverty

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