Judul: Analisis Pendapatan Usaha Garam Rakyat Berdasarkan Status Lahan dan Penggunaan Zat Aditif (Studi Kasus: Desa Santing, Kecamatan Losarang, Kabupaten Indramayu)
Abstrak: Indonesia is a maritime country with a variety of marine and fishery resources of wealth. One that can be utilized is sea water as a raw material for producing salt. Although it has abundant marine wealth, Indonesia still remains a substantial importer of salt annually. Since 2006 – 2010, the Government of Indonesia imports amounted to 1.731.309 tons, 4, on average per year. Santing village is one of the largest salt manufacturer in Indramayu Regency, while Indramayu Regency is the largest salt producer in West Java. Therefore base on the potential of the area, Indramayu Regency is expected to optimize more the production of salt. Production propotionally compares to income, so the farmers have to increase the production of salt by optimizing the input that is used as a factor of production in order to improve their income. The additives substance named ramsol is used as additional inputs in producing salt by some farmers in the village of Santing and it is good to improve the quality and output of salt, from the results of interviews with 100 respondent, 62 respondents use additives as additional inputs to their salt production, and the rest, 38 respondents do not use additives. The factors that affect the production of salt are the number of windmills, land area, labor and additives. But in a season of 2011 salt farmers who use additives caan improve their salt production on average 85.053,76 kg per ha, while the farmers who do not use additives is only 71.903 .51 kg per ha. The analysis of people’s salt revenues is differed based on the status of the land and the use of additives. Status of land is divided into two categories, first the farmers’s production sharing land (penyakap) and the second is the farmers on the leased land. The highest income will be obtained by the farmers who use additives on their own land. The lowest income will be gained by the farmers whoo do not use additives and they produce on the production sharing land status.
Keyword: income, production, ramsol, additives, salt

Judul: Efisiensi Produksi Usaha Garam Rakyat di Kabupaten Indramayu
Abstrak: Indramayu is one of salt production center in West Java with character of having higher rainfall. Some problems faced by Indramayu, such as low farm gate price and fluctuating production level. These problems have caused farmers do not get any profit or having business inefficiency and higher business cost. The aims of the research carried out are (1) to identify the production factors that affecting the salt production, (2) to analyse the resources of efficient production techniques, (3) to analyse the economic and allocation efficency level of of salt production in Indramayu. Data was collected from 100 farmers, divided into 3 groups based on tenurial ownership (selected by multistage sampling technique and analysed using statistic inferencial) with production function Cobb-Douglas stochastic-frontier and cost function. The probability of OLS resulted RTS which salt production in increasing return to scale area where RTS of cash-rent farmers 2.670, share-rent farmers 1.350 and farmers owner-tenant 1.153. Farm size is the most responsive production factor to increase the production level. Technical and allocation inefficiency affected the salt production as described by estimation of gamma which significant in each of farmer’s type. The parameter of gamma shows 80.1%, 99% of output variances of salt business among the cash-rent farmers, share-rent and owner-tenant farmers is the difference in technical efficiency. The result of research also showed that increasing of farm size, income and access of capital loan in labour-farmer, significantly causing the efficiency increasement. The use of additives could also increase the efficiency of self-own farmers. The average of technical efficinecy is 68% in scale of 0.7 and the rest is under this scale. The allocation efficiency level is only 5% out of total samples which is dominated by farmers owner-tenant. With optimalisation test of production input through ratio of product marginal value of cost (in output price Rp. 450/kg), showed that the land and human resource factor is more than 1 which means it is needed to add farm size and human resources quality. The phenomena of high technical efficiency and low allocation and economic efficiency is the characters of agribusiness which still has problem in high price production input and low output price. By reducing the level of fuel for providing the sea water, it is able to increase the allocation efficiency of salt production. The farmers could do substitution by using the low cost technology of sea water supplier. The policy of extensification of salt-farm is one of strategies to increase the salt production and the intensification policy is improvement of infrastructures and technologies that could increase the production quantities with the available land
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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