ABSTRAKWarna mempunyai korelasi dengan penampilan fisik, kandungan gizi, kimiawi serta sifat-sifat sensoris yang sangat menentukan kualitas produk-produk pertanian dan bahan pangan. Oleh sebab itu, pengukuran warna memiliki peran yang sangat penting di dalam industri pangan dan pengolahan produk-produk hasil pertanian. Pengukuran warna secara konvensional dilakukan secara destruktif dengan menggunakan peralatan laboratorium. Metode pengukuran warna produk-produk hasil pertanian yang lebih cepat, akurat dan tidak merusak (non-destruktif) menjadi sebuah kebutuhan. Penelitian ini bertujuan mengembangkan Computer Vision System (CVS) yang dapat digunakan sebagai alat untuk mengukur warna buah-buahan. CVS yang dirancang terdiri dari mini photo studio berwarna hitam berukuran 60x60x60 cm; pencahayaan dari 2 unit lampu LED 15 watt, kamera digital sony α6000, seperangkat laptop dan software aplikasi pengolahan citra. Software pengolahan citra dikembangkan dengan menggunakan bahasa pemrograman VB.Net 2008. CVS yang dikembangkan dikalibrasi dengan menggunakan 24 grafik warna Macbeth Colorchecker (Gretag-Macbeth, USA). Hasil validasi dari 24 grafik warna Macbeth Colorchecker yang diukur dengan menggunakan aplikasi pengolah citra menghasilkan nilai MAPE (Mean Absolute Percentage Error) komponen R/Red = 0%; G/Green = 0% dan B/Blue = 0,5%; dengan akurasi 99%. Dalam pengukuran warna, CVS yang dikembangkan memiliki tingkat akurasi 95%. ABSTRACTColor had a correlation with physical appearance, nutritional and chemical content as well as sensory properties which determine the quality of agricultural products and foods. Conventional color measurements were performed destructively using laboratory equipment. Therefore, color measurement methods of agricultural products were needed more quickly, accurately and non-destructively. This study aimed to develop a Computer Vision System (CVS) that can be used as a tool to measure the color of fruits. The designed CVS consists of a 60x60x60 cm black mini photo studio; a pair 15 watt LED lighting, sony α6000 digital camera, a set of laptop and an image processing software applications. Image processing software was programmed using VB.Net 2008 programming language. The developed CVS was calibrated using 24 color charts Macbeth Colorchecker (Gretag-Macbeth, USA). The calibration results of 24 color chart of Macbeth Colorchecker was resulted in a MAPE (Mean Absolute Percentage Error) value of component R / Red = 0%; G / Green = 0% and B / Blue = 0,5%; with 99% accuracy rate. In color measurement, the developed CVS had a 95% accuracy rate.
The fruits lemon pepper (Zanthoxylum acanthopodium DC.) are commonly used as flavouring in fresh form. Meanwhile, the lemon pepper fruits are perishable and easily attacked by fungi and loss its colour and fragrance. In this study, during a 4-week storage, the effects of drying temperature (40, 50, 60 and 70 oC) in a hot oven on water content, aroma and taste intensity of lemon pepper powder were evaluated. The initial average moisture content of fresh lemon pepper is 68,5 %. Among the four drying temperature that were used, 40 oC and 70 oC showed no significant different effect on water content, while 50 oC and 60 oC produced a lower water content. The intensity of the aroma and taste of lemon pepper decreases significantly with the increase of drying temperature. The moisture content, aroma and taste intensity were also decreased significantly during the experimental storage period (4 weeks). Our experiment has shown that lemon pepper powder dried at 40 oC has a lower water content and can maintain aroma and taste better than lemon pepper dried at 50–70 oC in a hot air oven. Therefore, the drying temperature of 40 oC is a better option for drying lemon pepper.
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 aims of this research are to determine the effect of the concentration addition level of karamunting (Rhodomyrtus tomentosa) fruit extract on the quality of yogurt during 9 d storage period to increase the functionality of yogurt as a probiotic food, a source of antioxidants and the use of karamunting as a natural colorant for food. This research used a completely randomized design consisting of two factors, that is the concentration of karamunting fruit extract which consisted of 4 addition levels (0%, 12%, 15% and 18% w/w) and the storage period of yogurt which also consisted of 4 levels (0 d, 3 ds, 6 d and 9 ds). The parameters that have been observed in this research were pH, total acid, water content, fat content, protein content, color analysis, antioxidant activity and organoleptic tests (aroma, taste, viscosity and color) of the yogurt. The results showed that karamunting fruit extract had a significant effect on pH, total acid, color L*, a* and b*, and organoleptic (aroma, viscosity and color) of the yogurt, but had no significant effect on water content, fat content, protein content and flavor of the yogurt. Storage period had a significant effect on pH, total acid, protein content, color L*, a* and b*, and organoleptic (aroma, taste, viscosity and color) of the yogurt, but had no significant effect on water content and fat content of the yogurt. The addition of 18% (w/w) karamunting fruit extract and 9 d of storage period resulted in yogurt with the characteristics as follows pH 4.2; total acid 1.57%; water content 87.05%; fat content 3.66%; protein content 2.7%; color L* 72,24; a* 8.28; and b* 9.38 and organoleptic (aroma, taste, thickness and color) which were quite favorable, as well as a strong antioxidant activity value of 85.35 ppm IC50.
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