The goal of this research was to design a Decision Support System (DSS) to monitor and forecast the price of rice. This system was designed to help the policy makers in decision making process to stabilize the rice price. The most fitted model base of the DSS forecasting method was selected by analyzing the architecture of Artificial Neural Network (ANN). The best fitted ANN architecture was selected based on the smallest value of Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) in training, testing, and validation. The research was done using the monthly price of rice IR64 in District Deli Serdang, North Sumatera from January 2000 to December 2015. Decision support system developing phases was used to create the best match of ANN architecture for the model base of the DSS along with the database, the knowledge base, as well as the user interface. DSS was programmed using the PHP programming and was designed in a web base to facilitate the interaction between the DSS, the system's users, and the flow of data exchange. From 73 trials unit of the ANN architecture analysis, it has been obtained that an ANN 12-1-1, purelin activation function inside the hidden layer, purelin activation function inside the output layer, traingda training algorithm (gradient descent with adaptive learning rate) and the value of learning rate was 0,1 were the best match for developing the DSS forecasting method. Furthermore, the MSE and MAPE of the training, testing and validation in a row were 0.00128 and 3.57%; 0.0319 and 5.47%; 0.0052 and 2.51%. The validation results showed that the forecasting results that has been produced by the DSS has a 90 % accuracy.ABSTRAKSistem pendukung keputusan monitoring dan peramalan harga beras dirancang untuk memberikan prediksi harga masa depan dan dukungan keputusan bagi para pembuat kebijakan dalam melakukan stabilisasi harga beras. Tujuan penelitian ini adalah merancang prototipe Sistem Pendukung Keputusan (SPK) dengan terlebih dahulu menganalisis arsitektur Jaringan Saraf Tiruan (JST) yang paling sesuai untuk digunakan sebagai metode peramalan/subsistem model SPK. Kajian dilakukan dengan menggunakan data harga bulanan komoditas beras IR64 di Kabupaten Deli Serdang, Sumatera Utara bulan Januari 2000–Desember 2015. Arsitektur model JST terbaik dipilih berdasarkan pada nilai Mean Square Error (MSE) dan Mean Absolute Percentage Error (MAPE) terkecil dari hasil pelatihan, pengujian dan validasi. Arsitektur model JST terbaik kemudian dirancang menjadi subsistem model SPK bersamaan dengan basis data, komponen pengetahuan dan tampilan antarmuka menggunakan fase-fase perancangan sistem pendukung keputusan. SPK dirancang untuk digunakan berbasis web (web base) agar memudahkan interaksi dengan pengguna (user) dan arus pertukaran data. SPK diprogram menggunakan bahasa pemrograman PHP. Dari 73 percobaan analisis arsitektur model JST yang telah dilakukan, diperoleh satu arsitektur JST dengan performa peramalan terbaik yang digunakan sebagai metode peramalan dengan arsitektur 12-1-1, fungsi aktivasi purelin pada lapisan tersembunyi, fungsi aktivasi purelin pada lapisan output, algoritma pelatihan traingda (gradient descent with adaptive learning rate) dan nilai laju pembelajaran 0,1. Nilai MSE dan MAPE dari hasil pelatihan, pengujian dan validasi berturut-turut adalah 0,00128 dan 3,57%; 0,0319 dan 5,47%; 0,0052 dan 2,51%. Hasil validasi menunjukkan bahwa hasil peramalan yang dihasilkan oleh SPK memiliki tingkat akurasi 90%.
The objectives of this study was to observe the effect of dolomite and NPK fertilizer application on growth, yield and leaf phosphorus levels of soybeans (Glycine max (L.) Merril) due to the application of dolomite and NPK fertilizers. This research was conducted from June 2019 to December 2019, at the Experimental Station of Agriculture Faculty of University of HKBP Nommensen Medan in Simalingkar B Village, Medan Tuntungan District at an altitude of ±33m above sea level. Soil type Ultisol with Tex-sand 43.75%, Tex-dust 42.18%, Tex-clay 14.07; pH 4.63; cation exchange capacity (CEC) 14.64%, Ptotal 0.10%, N kjehldahl 1.9% K-exch 0.20%, Ca-exch 1.32%, Mg-exch 0.92%. This research was arranged in a factorial randomized block design (RAK) with 2 treatment factors, namely, the first factor was dolomite (D) which consisted of 3 levels, namely: D0 = 0 g/polybag, D1 = 11.2 g/polybag, D2 = 22.4 g/polybag. The second factor was NPK fertilizer (N), which consisted of 4 levels, namely: N0 = 0 g/polybag, N1 = 1.5g/polybag, N2 = 3 g/polybag and N3 = 4.5g/polybag. Parameters observed were plant height, number of leaves, number of pods, number of filled pods, weight of filled pods, dry seed production per plant, dry weight of 100 seeds and leaf phosphorus levels. The results showed that dolomite application had a very significant effect on the number of pods, number of filled pods, weight of filled pods, dry seed production per plant, dry weight of 100 seeds, but had no significant effect on plant height, number of leaves and leaf phosphorus levels. The application of NPK fertilizer had a very significant effect on the number of pods, number of filled pods, dry seed production per plant, dry weight of 100 seeds but had no significant effect on plant height, number of leaves, weight of filled pods and leaf phosphorus content. The interaction of dolomite and NPK fertilizer had no significant effect on all observed parameters.
Soyghurt is a fermented soy milk product using Streptococcus thermophilus and Lactobacillus bulgaricus bacteria. Efforts to enrich drinks high in antioxidants are carried out by combining food ingredients that can function as antioxidants such as soybeans and jicama. The combination of these two food ingredients and the assistance of the fermentation process by S. thermophilus and L. bulgaricans was expected to increase antioxidant activity in soyghurt production.This study used a completely randomized design (CRD) with 2 factors, namely: the first factor was the ratio of soybean juice and yam juice (S) which consisted of 4 treatment levels, namely: S0 = 100: 0, S1 = 75: 25, S2 = 50: 50, S3 = 25: 75, S4 0: 100. Meanwhile for the second factor was the duration of fermentation (F) which consisted of 3 levels of treatment, namely: F0 = 6 hours, F1 = 8 hours, F2 = 10 hours. The parameters observed were total solids, total acid, organoleptic value, antioxidant activity, and total lactic acid bacteria in jicama soyghurt on the best samples.The results showed that both treatment factors had a very significant effect (P <0.01) on total solids, total acid, and organoleptic tests of soyghurt. The study showed that the comparison of soybean juice and jicama juice and fermentation duration resulted a high protein content value of 9.18% and was in accordance with the SNI yoghurt protein content, namely minimum of 3.5%. Antioxidant activity of yam bean soyghurt obtained an IC 50 value of 18.110 mg / L and it was a very strong antioxidant. Total lactic acid bacteria on yam soyghurt was 108 CFU / ml and it was high.Based on this research results, it was suggested that soyghurt processing may use a combination with other types of fruits and use natural food coloring to give different flavors and increase the nutritional values of soyghurt.
It is not known how to extract carotenoid- which has a high value -optimally from palm oil mesocarp fiber (POMF). The research objective was to determine the optimization of oil extraction from POMF waste and to determine the optimization of carotenoid extraction from POMF oil. The research was carried out in 2 stages: The first stage was oil extraction from the POMF with the treatment factor ratio of hexane to the weight of the POMF and the extraction time. Stage 2 Optimization of carotenoid extraction using the solvolitic method with treatment: Minor solvent types Methyl ester (Me) caprylate-caprate (C8 –C10) and Me laurate-myristate (C12-C14) and minor solvent concentrations of 0.1% and 0, 25%. Parameters analyzed were: oil content, Deterioration bleaching of index (DOBI), and carotenoid concentration. Optimization ratio between hexane and POMF weight is 1:40 (vol / g) with an oil content of 2.938%. Optimization of extraction time for 100 minutes with 4.104% oil content. Optimization of carotenoid extraction is by using minor solvent Me C8-C10 with a solvent amount of 0.1% which results in a carotenoid concentration of 302.442 ppm and DOBI of 5.74. The increase in caroten concentration resulted from saponification reached 114.2 times from the carotenoid concentration in POMF oil.
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