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All Journal Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)
Sandra Sandra
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Engineering Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering

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The effect of adding rice straw charcoal to the processing of bio-pellet from cacao pod husk Retno Damayanti; Sandra Sandra; Novita Riski Nanda
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 3, No 2 (2020)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2020.003.02.6

Abstract

Cacao pod husk and rice straw charcoal are potentially transformed into bio-pellet because of their high calorific value. Cocoa pod husk and rice straw charcoal has a calorific value of 4974.837 cal/g and 3569.837 cal/g, respectively. This research aimed to identify the effect of variations in particle size and in the addition ratio of rice straw charcoal on the calorific value of bio-pellet. Randomized block design factorial were employed in this study with factor of the addition ratio of rice straw charcoal and cacao pod husk (i.e.  0%:100%, 20% : 80%, 40% : 60%) and the particle size (i.e. 20, 40, 60 and 80 mesh). The results showed that rice straw charcoal addition resulted bio-pellet with the calorific value of 4111.93 – 4706.57 cal/g, and fulfill the SNI of bio-pellet (SNI 8021-2014). The treatment with addition of 100% cocoa pod husk and 80 mesh particle size generated the superior quality of bio-pellet. The findings confirmed that addition of rice straw charcoal did not enhance the energy potential (i.e. calorific value) of the bio-pellets, hence it is unfavourable option.
Moringa leaf chlorophyll content measurement system based on optimized artificial neural network Yusuf Hendrawan; Titon Elang Perkasa; Joko Prasetyo; Dimas Firmanda Al-Riza; Retno Damayanti; Mochamad Bagus Hermanto; Sandra Sandra
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) 6th International Conference on Green Agro-industry and Bioeconomy (ICGAB) July 2022 - Special Issue
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aimed to measure the chlorophyll content of Moringa leaves using machine vision and an optimized artificial neural network (ANN). A total of 480 images were used, 70% as training data and 30% as validation data. Features extraction was used to extract color and textural features. ANN was used as a modeling method, and the filter method was used as a feature selection method to optimize the best ANN input. Sensitivity analysis was done by varying the attribute evaluator in the filter method, as well as the learning function, the activation function, the learning rate, the momentum, the number of hidden layers, and the number of hidden nodes in the ANN. The best ANN structure was 10 input nodes, 30 nodes in the hidden layer 1, 40 nodes in the hidden layer 2, and 1 output node when using a learning rate of 0.1, a momentum of 0.5, the traincgf learning function, a logsig activation function in the hidden layer, and a tansig activation function in the output layer. The correlation coefficient between predicted and real data in the training process was 0.9792 with the training mean square error (MSE) of 0.0100, and the correlation coefficient of the validation process was 0.9794 with the validation MSE of 0.0099.
Co-Authors Dimas Firmanda Al Riza Joko Prasetyo Mochamad Bagus Hermanto Novita Riski Nanda Retno Damayanti Titon Elang Perkasa Yusuf Hendrawan