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.
Keyword: 

Judul: Nutritional Garrying Gapacity of Social Forestry In Lampung Barat District
Abstrak: The Government of Lampung Barat District issued Social Forestry Program to address forest degradation problem that caused by illegal logging. Therefore, nutritional carrying capacity of Social Forestry was important to be analyzed to understand the advantage of Social Forestry for farmer. The objectives of this research were to 1) analyze the characteristics of farmer household, 2) analyze the commodity diversity of Social Forestry, 3) analyze nutritional carrying capacity of Social Forestry, 4) analyze household food access based on income and food expenditure, and 5) analyze factors related to nutritional carrying capacity and food access of farmer household. The research was conducted by using cross sectional study design. The sample was 90 Social Forestry's farmer households chosen by using purposive sampling method. The result of the research shows that almost all samples are small family which has elementary school-education-Ievel and are Sundanese and Javanese. The average size of land organizing of Social Forestry each household is 1.3 ha with range 0.25-4 ha. The commodity diversity organized by the farmer household (52.2%) is included in low category with:;; 12 commodities. The average nutritional carrying capacity of Social Forestry is 2.754 Gal/cap/day, while the highest is in others (coffee and spices) which is 1.875 Gal/cap/day (68.1%) and the lowest is in legumes which is 2 Gal/cap/day (0.1%). More than half (67.8%) of the sample has reach nutritional carrying capacity"' 90% of ideal nutritional requirement. Social Forestry is the main source of income for fanner household with share of income is 63.9% of total household income. More than half (71,1%) of the sample has sufficient food access level (share of food expenditure is < 55% of income). The factors related to nutritional carrying capacity are: number of household member, housewife education, land size, income per capita, and commodity diversity, while the factors related to food access level are number of household member, land size, income per capita, and food caring capacity
Keyword: 

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