Judul: Hubungan Strategi Koping dan Kesejahteraan Keluarga Nelayan
Abstrak: Fishermen have income that is uncertain, depending on the season. Coping strategy is needed, especially at a time when revenues are down. This study aims to analyze the relationship between coping strategies and subjective well-being of fishermen families. Location of the research conducted at the village of Pantai Mekar, Muara Gembong, Bekasi district, West Java Province. Example of this research are 100 fishing families selected by purposif sampling. Research found that the majority of fishing families do as much as 7-13 coping and coping in middle category. Coping strategies are most often committed by fishermen families including: cutting strategies to reduce or replace a side dish that is eaten, attempt to stall and debt owed to the family. Subjective well-being of fishing families in middle category. The average family of fishermen have a high subjective well-being in a relationship or interaction with her husband, children, family and neighbors. Correlation test results showed that there was a significant positive correlation between age and subjective well-being. Means increasing age, the higher subjective well-being. Subjective well being significantly negatively associated with coping strategies. Means that the smaller the family attempt to do coping strategies tend to increase the subjective well-being.
Keyword: Bogor Agricultural University (IPB), fishermen., family wellbeing, coping strategies

Judul: Kajian Kesejahteraan Keluarga:  Keragaan Pemenuhan Kebutuhan Pangan Dan Perumahan Pada Keluarga Nelayan Di Daerah Rawan Bencana
Abstrak: Act of god that knocks over coast area will impacted on fisherman family wellbeing. The aim of this research is subject to be analyze food and housing fulfillment description as part of family wellbeing indicators on fisherman at disaster gristle region. Research is done at Pangandaran's Village Pangandaran's District, Ciamis's Regency by design cross sectional study. Example in observational it is 80 fisherman family that chosen by ala simple random is sampling . As big as 58.8 percent sample to comprise little families( 4 person). Patriarch age (57. 5%) and mother (70%) lie on early mature group. Father and mother education is not extremely different which is majority doesn't accomplish mandatory bounds studies 9 years. Family characteristic is engaged food fullfilmentt. The greater family head count therefore food expenditure and availibility of food per capita will get little. Progressively old aged mother therefore excelsior alertness zoom. Per capita income that excelsior that decrease caused by food aid and zoom food coping strategy. Food access increases and coping strategy decrease along with at the height asset ownership. Largely fisherman family samples (83. 7%) rank is not prosperous objectively, but a large part (67.5%) rank is prosperous subjectively. Factor that regard objective wellbeing is family size, while factors that regard subjective wellbeing are density (far ranging floor per capita) and home ownership state. Subjective wellbeing on fisherman family was characterized by housing fulfillment that give biggest satisfaction.
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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