Judul: Household Food Security in Village Producing Resin In Lampung Barat District
Abstrak: Repong is a forest management system which inherently conducted by community in Lampung Barat Coast. The objective of this research were to: 1) analyze household socio economic characteristics, 2) analyze household income and food expenditure level, 3) analyze household food security 4) analyze socio economic factors that influence household food security. This research was conducted by using retrospective design. The location was in Penengahan Village, Karya Penggawa Subdistrict, Lampung Barat District that chosen purposively with consideration that the village is the main village producing resin. The samples were 30 households who own repong and 30 households who do not own repong that chosen snowbally. The result of the research shows that: 1) almost all (93.3%) household who own repong work as fanner, while half of household who do not own repong work in area of fanning (46.67%), service (40%), and trading (13.33%); 2) there is 30% of household who own repong and 36.7% of household who do not own repong that have low food access; 3) only 33.33% of household who own repong and 23,33% household who do not own repong are food secure; 4) factors that significantly influence energy consumption level are number of household member, size of repong, and food expenditure; factors that significantly influence protein consumption are number of household member and food expenditure; factors that significantly influence consumption diversity are number of household member and size of repong.
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Judul: Analysis of Food Supplying Pattern and Strategy of Social Forestry's Fanner Household in Lampung Barat
Abstrak: The government of Lampung Barat District need information about food supplying pattern and strategy of Social Forestry's farmer household. This inf~tion is essential for formulating forestry policy and program that support foo~ecurity of Social Forestry's fanner who utilize the forestry by planting coffel: The objectives of this research were to: 1) know the socio economic cha~teristics of Social Forestry's fanner household in Lampung Barat District, 2) a~lyze food supply level of Social Forestry's fanner household in Lampung BarafllDistrict, 3) know the source and cost of food supplying of Social Forestry's fann~ household in Lampung Barat District, 4) know food supplying strategy of Sociaf Forestry's farmer household in Lampung Barat District. The research was cond@ted in October-November 2008 by using cross sectional study design. The sam~ was 90 Social Forestry's fanner households. The data obtained by the reseaWh were household characteristics; food supplying pattern and strategy whidi analyzed descriptively and inferentially. More than half of the samples (78~1;are small households with all fanners and housewives are in productive age and illl elementary school. The income per capita per month of the sample is Rp 509.626, obtained from both Social Forestry and Non Social Forestry works. The food &xpenditure per capita per month is Rp 213.136, while non food expenditure per c~ita per month is Rp 197.851. The larger shares of food expenditure are utiliz&i in food supplying of cereals (31,7%), vegetables and fruits (20,3%), and animi food (18,5%). Energy supplying of the sample for cereals, vegetables and fruits?'oils and fats, sugar, and others are sufficient, while energy supplying for tubers, animal protein, legumes, and oily seed are not sufficient. The food supply is obtained by purchasing (70,8%), self production (28,2%), and other sources (0,4%). Strategies for raising income conducted by the sample are searching supplementary income other than the main working field; conducting husbandry; and planting crops in garden. Saving is a strategy rarely conducted by the sample.
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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