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All Journal Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Peternakan (Jurnal of Animal Science) Jurnal Agricultural Biosystem Engineering Education, Language, and Arts: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian dan Pemberdayaan Masyarakat Inovatif Journal of Engineering and Technological Sciences
Febryan Kusuma Wisnu
Jurusan Teknik Pertanian, Fakultas Pertanian, Universitas Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences Veterinary Other

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Journal : Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)

 1 
Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) Rizza Wijaya; Silvia Oktavianur Yudiastuti; Anna Mardhiana Handayani; Elok Kurnia Novita Sari; Tri Wahyu Saputra; Febryan Kusuma Wisnu; Aulia Brilliantina
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 11, No 4 (2022): Desember 2022
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v11i4.589-600

Abstract

Edamame is included in perishable products or products that have a fairly short shelf life if post-harvest processing is not carried out. One of the post-harvest processing methods commonly used by the community is drying. The purpose of this study was to analyze the drying process of edamame related to the MLRL and ANN models. This study used a completely randomized design (CRD) with three variations of air velocity, namely 1 m/s, 3 m/s, and 5 m/s. Data collection was repeated three times every 30 minutes until 330 minutes.  Multiple linear regression (MLR) model training and validation produce accuracy values of 88.03 and 82.23, and the value of R2 of 0.93 and 0.90. While the training and validation of the artificial neural network (ANN) model resulted in accuracy values of 88.34 and 82.15, and R2 values of 0.93 and 0.90. Keywords:    ANN, Drying, Edamame, Food  dehydrator
MATHEMATICAL MODEL OF PHYSICAL PROPERTIES CHANGE OF COCONUT SAP IN THE VACUUM EVAPORATOR Febryan Kusuma Wisnu; Sri Rahayoe; Rizza Wijaya; Mareli Telaumbanua; Agus Haryanto
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 10, No 2 (2021): Juni
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v10i2.252-262

Abstract

The potential of brown sugar as a substitute for granulated sugar is enormous considering the abundant coconut sap production. However, the quantity of brown sugar production through the traditional method is one of the main obstacles. This study used a vacuum evaporator that emphasizes the hygienic and effective mass production of brown sugar. For this reason, it is necessary to approach changes in the physical properties of sap juice during the cooking process. This knowledge is indispensable in the cooking process, which involves the proper evaporation and crystallization of brown sugar. This research is devoted to determining the viscosity, density, and dissolved solids expressed in Brix and proposes a mathematical model to predict the physical properties during the evaporation process of brown sugar as a function of the initial concentration the solution before proceeding to the crystallization process. Results confirm that the prediction model for Brix is Cθ=(Co–Ce)·exp(0.0067·t)+Ce, the model for viscosity µθ=µo·exp(0.011·t), and ρө=(0.44996·t)+ρ0 for the density prediction model. The resulted mathematical model can accurately predict the rate of change in coconut sap's physical properties, indicated by the high coefficient of determination (R2). Keywords : brix, brown sugar, density, vacuum evaporator, viscosity
Empirical Model for Estimation of Soil Permeability Based on Soil Texture and Porosity Siti Suharyatun; Mareli Telaumbanua; Agus Haryanto; Febryan Kusuma Wisnu; Mayrani Tri Pratiwi
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 12, No 3 (2023): September 2023
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v12i3.533-544

Abstract

Soil permeability is the ability of the soil to pass water or air. Soil permeability is affected by texture, structure, and soil porosity. This study aims to develop a mathematical model to predict the value of soil permeability as a function of the percentage of the constituent fraction of the soil and soil permeability as a function of porosity. The study used soil taken from 7 different locations, with 6 samples for each location, 4 samples for model building and 2 samples for model validation. Parameters observed consisted of the percentage of sand (x1), the percentage of silt (x2), the percentage of clay, (x3), soil porosity (x4) and soil permeability (y). From the analysis, the empirical model obtained is soil permeability as a function of the percentage of constituent fractions of the soil which is expressed by the equation y1=36.796-16.022x2-23.938x3 and soil permeability as a function of porosity is expressed by the equation y2=12+0.65(x4-0.06)-2.92 . The permeability equation as a function of soil constituent fraction (y1) can predict soil permeability with a value of R2 = 0.925 and an RRMSE value of 5.461%, better than the permeability equation as a function of porosity. Keywords:   Empirical model, Multiple linear regression, RRMSE, Soil physical properties, Model validation
The Kinetics of Ethanol Volume Change With Variation of Input Volume and Heating Temperature in The Re-Distillation Process of Glucomannan Extraction Residue Using Batch Distillator Saut Edo Riko Manurung; Rizky Qhasim Pratama; Sri Rahayoe; Joko Nugroho W. K; Febryan Kusuma Wisnu
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 11, No 1 (2022): March
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v11i1.161-173

Abstract

Ethanol is commonly used as a solvent in extracting glucomannan from Porang. However, the extraction process often leaves ethanol. The remaining ethanol can be re-distilled to save the use of it. The remaining ethanol is used in the re-distillation process with input volumes of 50L and 100L with variations in heating temperatures of 80°C, 85°C, and 90°C. This study aimed to analyze the effect of the ethanol input volume and temperature on the output volume of re-distilled ethanol and determine the constant change in volume of re-distilled ethanol using kinetics and Arrhenius equations. The results showed that the input volume and heating temperature variation differed significantly from the ethanol output volume. The k value changes in the ethanol output volume from re-distillation with an input volume of 50L and a temperature variation of 80°C, 85°C, and 90°C respectively were 0.0016, 0.0023, and 0.0027 L/min, while the input volume of 100L was 0.0009, 0.001, and 0.0014 L/min. The results of the k value as a function of temperature using the Arrhenius equation showed that the re-distillation process with an input volume of 50L and 100L produces activation energy (Ea) of 55.83 kJ/mol and 46.94 kJ/mol, while the collision frequency value (A) of 3.03x105/min and 7.7x103/min.Keywords: Distillation, ethanol, glucomannan, arrhenius model, re-distillation
Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) Wijaya, Rizza; Yudiastuti, Silvia Oktavianur; Handayani, Anna Mardhiana; Sari, Elok Kurnia Novita; Saputra, Tri Wahyu; Wisnu, Febryan Kusuma; Brilliantina, Aulia
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 11 No. 4 (2022): Desember 2022
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v11i4.589-600

Abstract

Edamame is included in perishable products or products that have a fairly short shelf life if post-harvest processing is not carried out. One of the post-harvest processing methods commonly used by the community is drying. The purpose of this study was to analyze the drying process of edamame related to the MLRL and ANN models. This study used a completely randomized design (CRD) with three variations of air velocity, namely 1 m/s, 3 m/s, and 5 m/s. Data collection was repeated three times every 30 minutes until 330 minutes.  Multiple linear regression (MLR) model training and validation produce accuracy values of 88.03 and 82.23, and the value of R2 of 0.93 and 0.90. While the training and validation of the artificial neural network (ANN) model resulted in accuracy values of 88.34 and 82.15, and R2 values of 0.93 and 0.90. Keywords:    ANN, Drying, Edamame, Food  dehydrator
Co-Authors Agus Haryanto Agus Haryanto Agus Haryanto Anna Mardiana Handayani Ayu Anggiana Brilliantina, Aulia Budianto Lanya Budianto Lanya Cicih Sugianti Dwi Dian Novita Eka Yana Elhamida Rezkia Amien Elok Kurnia Novita Sari Elok Kurnia Novita Sari Fil'aini, Raizummi Fil’aini, Raizummi Firmansyah Ully Pratama Handayani, Anna Mardhiana Intan Nuraini Joko Nugroho W. K Kade Wahyu Saputri khusnul khotimah Kurniawan, Ahmad Ridho Marcus, Patrice Kevin Mareli Telaumbanua Mareli Telaumbanua Mayrani Tri Pratiwi Nur'aini, Nur'aini Nuraini Purnomo, Cahyo Eko Ramadhanti, Annisa Suci Ridwan, Ridwan Rizky Qhasim Pratama Rizza Wijaya Rizza Wijaya Sandi Asmara Saut Edo Riko Manurung Setiawan, Wahyu Hendi Silvia Oktavia Nur Yudiastuti Sinuraya, Eden Ericson Siti Suharyatun Siti Suharyatun Sri Rahayoe Sri Waluyo Tenaumbanua, Mareli Tri Wahyu Saputra Warji Warji Winda Rahmawati Winda Rahmawati Winda Rahmawati Winda Rahmawati Yudiastuti, Silvia Oktavianur