Judul: Economic Analysis of Smallholder Rubber Agroforestry System Efficiency in Jambi Indonesia
Abstrak: The objectives of this research were; (1) to analyze production efficiency and profitability of smallholder rubber monoculture and smallholder rubber agroforestry systems, and (2) to find out the effects of policy distortions towards rubber production under smallholder monoculture and smallholder agroforestry. Smallholders are not only judged by yield per hectare; economic efficiency is not only a matter of returns to land and returns to labor but smallholders can adopt low input strategy, continue making profits at prices that would be economically viable. Such flexibility offers the possibility of efficient resource allocation in response to diversification of economic opportunities. The Policy Analysis Matrix (PAM) with the Domestic Resource Cost (DRC) results under the baseline scenario indicated that the use of domestic resources in production of rubber was efficient and socially profitable under the two systems but more desirable under monoculture system given the current prices for physical inputs, outputs, technologies and policy transfer. However, even with sensitivity analysis of 10% increase in the price of rubber holding other factors constant does not make rubber agroforestry system more efficient than its counterpart and a 20% fall in price of rubber made rubber production under agroforestry system less efficient and undesirable. All measures are compared to the alternative policy indicators currently used. Therefore, recommendations made from this study relate to the need for diversification into better practices that can sustain efficient rubber production under the agroforestry system, encouraging private sector participation and reducing disincentives to rubber production.
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Judul: Financial analysis of rubber plantation conversion into oil palm plantation and its impact on income distribution in Muaro Jambi Regency.
Abstrak: Rubber and oil palm plantations give bigger contribution to region economics development in Jambi Province. These commodities export rank first in that of farm estate commodity. Land conversion activities happened from rubber plantation into oil palm plantation because of many reasons. The objectives of this research are to estimate profit of rubber and oil palm farming, to identify influencing factors of land conversion and to know its impact on income distribution. Methods to determine the structure of income and production cost are performed by farming analysis and feasibility of rubber and oil farming for 25 years are used by financial analysis. Factors influencing of farmer decision are analyzed using logit regression analysis and the impact on farmers income are analyzed using income distribution analysis. The research was conducted from February to March 2013 in Jambi Luar Kota Subdistrict, Muaro Jambi Regency. It applied a survey method using structured questionnaires. Primary data collected from rubber farmers and oil palm farmers who converted their rubber plantation. Secondary data also collected from relevant agencies. The results showed that a decrease of rubber plantation area in Muaro Jambi Regency during 2005 to 2010 about 3 429 hectares while in the same period the area of palm oil plantation increasing with average of 2 242.33 hectares per year. Profit and financial feasibility analysis of rubber farming is higher than oil palm farming. Influencing factors of land conversion are farmers education, frequency of rubber tapping and dummy other income sources at ten percent probability level. The job creation of rubber farming is less than oil palm farming for daily worker there.
Keyword: rubber farming, oil palm farming, land conversion

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