Judul: A Study on Sustainability of Small Holder Dairy Farming on Agroforestry System: Case Study in Lembang West Java Province, Indonesia.
Abstrak: Dairy farming practices is one of the empowerment agriculture sectors in Asia, due to Labor and nutrition fact on milk. The fastest dairy farming development found in Java island, noted as the most populated island in Indonesia. Todays, farmers conduct endeavor varied ways in filling forage demand for the dairy cattle, one of them by Agroforestry system. An appropriate management on Agroforestry system is highly required in enhancing forage production. However, the information of potency that might be occuring not clearly understood, therefore the broad objection of this research is to explore the potency of Agroforestry system due to sustainability of small scale dairy farming. The observation of Agroforestry system was conducted in Lembang (West Java). Several plots have been designed West Java and As 78 of farmers have been involved. We observed, more than 36% of dairy farmers attempted the forage by utilizing forest area. We calculate the highest forage yield was gained in low-density forage (21.01 Mg/ha, 12% shading level). The lower forage yield (7.09 Mg/ha) was found in middle density plot, since it higher levels of shade (78%). In general discussion, it could be seen that in higher density plot showed higher nutrients compound specially in DM, fiber, protein and fat. Experimental field has been design to understand the effect of shading treatment to the forage. In field experiment research there were two kinds of variety forage used; P. purpureum (King Grass) and S. Splendida. Pointed on the influence of shading levels, we were stressing on several treatments; un-shaded (0%) and shade treatments. The artificial shading treatment (60% and 80%) was applied for this research. Organic fertilizer was used as the secondary factor. As 30 Mg/ha, 20 Mg/ha and 10 Mg/ha of organic fertilizer were conducted. We were simulating for 40d, 50d and 60d of the harvest time. Therefore, totally 81 plots have been designed for this research. As 31.1% of forage yield was slowly decreasing as increasing shading intensities.Underneath shading condition such as 60% and 80%, the forage yield required higher organic fertilizer. In 40 and 50 days of plantation, the additional 30 Mg/ha organic fertilizer presences the highest forage yield underneath limitation of sun availability. The plant height was gradually increasing with the higher number of levels of shade. Chlorophylls content were the other respond showed by plant as the effect of shading treatment. It could be obtained that as 29.03% and 23.41% of chlorophyll content was increasing rapidly on P. purpureum, and S. splendida respectively. The average of DM production was depleting as 28.69% for S. splendida, this amount was lower compared with P. purpureum (36.50%). Ash compound was significantly influenced ash content (p<0.05). We also observed that levels of shade has a significantly effect due to fat content on P. purpureum (P<0.05). The average of data gained showed that fat content was increase for 16.35% as the raising of levels of shade in S. splendida. The less number of irradiance accepted by plants, showed the increment of protein content (%) (p<0.05). The average of crude protein content was increasing for 26.95% in P. purpureum and
Keyword: Dairy Farming, Agroforestry, Sustainability

Judul: Simulation Model of Sustainable Forage Supply for Dairy Farming in Lembang Sub District West Java
Abstrak: Forage supply is the core issue in dairy farming productivity in Indonesia. The restricted forage supply is mainly due to the limited farming land owened by the farmers. This research was carried out to formulate the simulation model of forage supply sustainability for daory farming in Lembang Sub district, West Java. Method of this research was collecting data from (1) desk study, (2) survey,(3) interview with the stakeholder, (4) FGD (Focuse Group Discussion). The model was built by using the system approach; need assessment, problem formulation, system identification (causal loop and inputoutput diagram) and model formulation. The model simulation software program used in this study was Powersim Studio 2005. There were four submodels built; population submodel, dairy cattle population submodel, economic submodel, and forage availability submodel. Based on the dynamic simulation, it was obtained the amount of population as 207.025 ppl in the end of year simulation (20 years), 15.392 AU for dairy cattle population, 48.722.025 litter for milk production, Rp. 157.372.140.750,00 for the revenue from milk yield, 40.484,52 ton for forage production and 165.492,73 ton for forage requirement. Policy analysis was exceeding from the model built. Sensitivity’s test was the real form of intervention from model structures. It showed how sensitive the model as the changes of output and it showed what the effect resulted. Model validation was done by statistical method; MAPE (Mean Percentage Error). The spatial analysis was done to determine the ecological suitability for forage management.
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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