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KARAKTERISTIK PROSES GASIFIKASI TEMPURUNG KELAPA MENGGUNAKAN UPDRAFT GASIFIER TIPE HISAP Icha Syahrotul Anam; Bambang Purwantana; Radi Radi
Jurnal Teknologi Terapan Vol 8, No 1 (2022): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v8i1.380

Energy demand continues to increase in line with the increasing population which results in high energy consumption. Utilization of biomass waste such as coconut shells as an alternative energy source is very beneficial, because it can reduce dependence on fuel oil. One of the technologies that can be used to produce alternative energy is biomass gasification. The purpose of this study was to determine the character of the biomass gasification process using a suction type updraft gasifier with varying the size of the material and air flow rate. The biomass used was coconut shell with a moisture content of 8-10%. The research method used by varying the size of the material and the air flow rate. The weight of the material tested was 5 kg with additional 200 grams of charcoal. The results showed that coconut shell gasification using updraft gasifier with suction type was able to produce combustible gas with the characteristics of the gasification process including the effective gasification time of 15-21 minutes, gasification process temperature > 700° C, flame temperature 400-800° C, mass of charcoal and ash 138-223 grams, tar mass 38-95gram and effectiveness 25-94%. The flames characteristic at an air flow rate of 0.17m/s tend to be solid, at an air flow rate of 0.23 m/s the flames are high, and 0.3 m/s the flames are mostly filled with smoke. The highest effectiveness is achieved at a variation of 0.17 m/s, which indicates that with the increasing rate of incoming air, the effectiveness of gasification will be lower.

Redesign and Performance Test of Liquid Fertilizer Based on Variable Rate Application on Chili Cultivation Andi Muh. Saldan; Radi Radi; Bambang Purwantana; Lilik Sutiarso
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 13, No 2 (2024): June 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i2.339-349

Liquid fertilizer applicator based on Variable Rate Application (VRA) is a technology that is used to fertilize in a controlled and precise manner. This study aims to improve the effectiveness and efficiency of the fertilization process of chili plants. The development carried out is esp32 cam which serves to detect chili plants that lack elements. The method used is input of the nutritional needs of chili plants, spray doses of each plant, and a microcontroller to control applicator components such as sprayer pumps, selenoid valve, and esp32 cam. In this liquid fertilizer applicator there are 2 pipes, each pipe has 4 nozzles. The results of laboratory tests show that the discharge of liquid fertilizer sprayed follows the input results of the nutritional needs of chili plants. The discharge released on the PWM sprayer motor varies from 40 to 100% resulting in very different discharge variations in each PWM spray. The efficiency of this VRA-based liquid fertilizer applicator reaches 87% or an increase of about 14.7% from the applicator before development. Regression analysis of dimmer level to spraying discharge showed a function y = 6.3016x + 18.937 with an R 2 of 0.9921. While the regression analysis of the dimmer level of the applicator speed obtained the function y = 94.075x + 20.203 with an R2 of 0.9936. Keywords: Chili plants, Liquid, Fertilizer, Applicator, VRA.

Classification of Roasting Level of Coffee Beans Using Convolutional Neural Network with MobileNet Architecture for Android Implementation Isran Mohamad Pakaya; Radi Radi; Bambang Purwantana
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 13, No 3 (2024): September 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i3.924-932

The roasting process has a significant impact on the aroma profile and taste of coffee making it an essential stage in the coffee processing. Currently, the classification of coffee bean roasting levels still relies on subjective human visual assessment, which can lead to errors due to fatigue or negligence. To overcome this problem, a classification system was developed using computer vision technology with a deep learning approach. The present study designed a coffee bean roasting level classification system based on image analysis integrated within an Android application. The Convolutional Neural Network (CNN) model with the MobileNet architecture was used to identify and classify coffee beans based on their roasting level. Two CNN models, namely CNN Alpha and CNN Beta were used in this study. The dataset included 1.600 coffee bean images, with 1.200 images used to train the model and 400 images used to test the accuracy. In this experiment, the input image had an optimal size of 70x70 pixels, a learning rate of 0.0001, and 100 epochs for both models. The model training and testing results in the highest accuracy of 98-88% in 6.40-0.0012 minutes.The application test results obtained 93.55% accuracy, 97.06% precision, and 96.67% recall. These results indicate that this model and application function optimally in classifying coffee bean roasting levels accurately. Overall, this study reveals the potential of integrating CNN with the MobileNet architecture into an Android-based application to change the way of roasting level classification, as well as to improve efficiency and accuracy. Keywords: Coffee, Roasting, Convolutional Neural Network, MobileNet, Android.

Potential Analysis of Biomass Briquettes from Sugarcane Milling Waste for Boiler and Generator Turbines Stations Makbul Hajad; Muhammad Hafidz Syahputra; Raditya Yulianta; Radi Radi; Sri Markumningsih; Bambang Purwantana
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 13, No 4 (2024): December 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i4.1226-1236

The decrease in sugar productivity was due to insufficient process of the sugar production process such as the low efficiency of boiler machine input energy. This study aims to analyze the potential use of Bagasse Briquetting Fuel (BBF) made from sugarcane milling waste at PT Madubaru as an attempt to obtain the optimal efficiency of boiler machine. Analysis of the effect of the adhesive concentration on the BBF quality was carried out to determine the optimal composition of the use of adhesive materials. Economic analysis was also conducted to determine the economic potential of BBF development. The analysis revealed that the BBF from Sugarcane milling waste has Calorific Value of 17,367-19,497 KJ/kg and density of 0.740-0.915 g/cm3. BBF with an adhesive variation of 1.25% is the BBF with the highest efficiency because it meets the needs of boiler fuel with the least amount of 100.8 tons/day for the operation of 1 boiler machine. The development of BBF from sugarcane milling waste has a selling value of Rp1.390.5,-/kg much lower than the existing biomass fuels found in the market. Keywords: Bagasse briquetting fuel; Boiler machine; Energy efficiency; Renewable energy; Sugarcane milling waste.

Redesign and Performance Test of Liquid Fertilizer Based on Variable Rate Application on Chili Cultivation Saldan, Andi Muh.; Radi, Radi; Purwantana, Bambang; Sutiarso, Lilik
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 2 (2024): June 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i2.339-349

Liquid fertilizer applicator based on Variable Rate Application (VRA) is a technology that is used to fertilize in a controlled and precise manner. This study aims to improve the effectiveness and efficiency of the fertilization process of chili plants. The development carried out is esp32 cam which serves to detect chili plants that lack elements. The method used is input of the nutritional needs of chili plants, spray doses of each plant, and a microcontroller to control applicator components such as sprayer pumps, selenoid valve, and esp32 cam. In this liquid fertilizer applicator there are 2 pipes, each pipe has 4 nozzles. The results of laboratory tests show that the discharge of liquid fertilizer sprayed follows the input results of the nutritional needs of chili plants. The discharge released on the PWM sprayer motor varies from 40 to 100% resulting in very different discharge variations in each PWM spray. The efficiency of this VRA-based liquid fertilizer applicator reaches 87% or an increase of about 14.7% from the applicator before development. Regression analysis of dimmer level to spraying discharge showed a function y = 6.3016x + 18.937 with an R 2 of 0.9921. While the regression analysis of the dimmer level of the applicator speed obtained the function y = 94.075x + 20.203 with an R2 of 0.9936. Keywords: Chili plants, Liquid, Fertilizer, Applicator, VRA.

Classification of Roasting Level of Coffee Beans Using Convolutional Neural Network with MobileNet Architecture for Android Implementation Pakaya, Isran Mohamad; Radi, Radi; Purwantana, Bambang
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 3 (2024): September 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i3.924-932

The roasting process has a significant impact on the aroma profile and taste of coffee making it an essential stage in the coffee processing. Currently, the classification of coffee bean roasting levels still relies on subjective human visual assessment, which can lead to errors due to fatigue or negligence. To overcome this problem, a classification system was developed using computer vision technology with a deep learning approach. The present study designed a coffee bean roasting level classification system based on image analysis integrated within an Android application. The Convolutional Neural Network (CNN) model with the MobileNet architecture was used to identify and classify coffee beans based on their roasting level. Two CNN models, namely CNN Alpha and CNN Beta were used in this study. The dataset included 1.600 coffee bean images, with 1.200 images used to train the model and 400 images used to test the accuracy. In this experiment, the input image had an optimal size of 70x70 pixels, a learning rate of 0.0001, and 100 epochs for both models. The model training and testing results in the highest accuracy of 98-88% in 6.40-0.0012 minutes.The application test results obtained 93.55% accuracy, 97.06% precision, and 96.67% recall. These results indicate that this model and application function optimally in classifying coffee bean roasting levels accurately. Overall, this study reveals the potential of integrating CNN with the MobileNet architecture into an Android-based application to change the way of roasting level classification, as well as to improve efficiency and accuracy. Keywords: Coffee, Roasting, Convolutional Neural Network, MobileNet, Android.

Potential Analysis of Biomass Briquettes from Sugarcane Milling Waste for Boiler and Generator Turbines Stations Hajad, Makbul; Syahputra, Muhammad Hafidz; Yulianta, Raditya; Radi, Radi; Markumningsih, Sri; Purwantana, Bambang
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 4 (2024): December 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i4.1226-1236

The decrease in sugar productivity was due to insufficient process of the sugar production process such as the low efficiency of boiler machine input energy. This study aims to analyze the potential use of Bagasse Briquetting Fuel (BBF) made from sugarcane milling waste at PT Madubaru as an attempt to obtain the optimal efficiency of boiler machine. Analysis of the effect of the adhesive concentration on the BBF quality was carried out to determine the optimal composition of the use of adhesive materials. Economic analysis was also conducted to determine the economic potential of BBF development. The analysis revealed that the BBF from Sugarcane milling waste has Calorific Value of 17,367-19,497 KJ/kg and density of 0.740-0.915 g/cm3. BBF with an adhesive variation of 1.25% is the BBF with the highest efficiency because it meets the needs of boiler fuel with the least amount of 100.8 tons/day for the operation of 1 boiler machine. The development of BBF from sugarcane milling waste has a selling value of Rp1.390.5,-/kg much lower than the existing biomass fuels found in the market. Keywords: Bagasse briquetting fuel; Boiler machine; Energy efficiency; Renewable energy; Sugarcane milling waste.

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