Jurnal Keteknikan Pertanian
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Challenges in the Implementation of Internet of Things (IoT) in Irrigation and Fertilizer Management System in Indonesia
Full text linkAgriculture is critical to many countries\u27 economies, especially related to gross domestic products (GDP) and employment. However, as a result of industrialization, leading to a problem in fulfilling the expanding global food supply demand. The Internet of Things (IoT) can enhance automatic data transfer in agricultural, improve production, increase quality, improve cost-effectiveness, and reduce environmental impact. However, the obstacles related to IoT application in agriculture have received little discussion especially in the development countries such as Indonesia. This research seeks to fill that gap by investigating the specific issues of adopting the Internet of Things (IoT) in the context of an irrigation and fertilizer management system in Indonesia. To fully study this, a stratified multistage random sampling was conducted to acquire significant insights and data. According to the interview results, respondents voiced worries regarding IoT deployment in agriculture, including, costs implementation (CI), their own knowledge (perceived knowledge (PK)), user experiences with the technology (perceived ease of use (PEU)) and intention of use (IU). The study finds weak CI-IU and PK-IU links but a strong PEU-IU correlation, underscoring the multifaceted factors influencing IoT adoption in agriculture. It is found that the easiness of the use of IoT is the main factors that influence Indonesian farmers to implement the IoT in their farmers. Although the cost of the implementation is an essential factor, easiness to use IoT is the most significant factor. Lastly, researchers, policymakers, and agricultural stakeholders can leverage these insights to advance IoT integration and sustainability in farming practices
Adaptive-Historical Energy-Efficient Temperature Control for Tropical Greenhouses
No full textMaintaining an optimal microclimate is essential for efficient operation of tropical greenhouses, particularly under fluctuating weather conditions. This study proposes an adaptive energy-efficient model for regulating air temperature in tropical greenhouses using historical climate data. The model optimizes the fan rotation speeds via an inverter to meet the temperature targets while minimizing energy consumption. Key methodologies include climate data analysis, development of a predictive model for indoor air temperature using Artificial Neural Networks, and optimization of fan speed control. The model achieved high predictive accuracy, with an RMSE of 0,02 and an R² of 0,96. The practical implementation demonstrated effective temperature control, with fan speeds ranging between 30 and 40 Hz during cloudy periods and 50 Hz in sunny conditions. Notably, the system reduced electricity consumption by 33,93% during cloudy weather and 18,54% in sunny weather, showing its potential for significant energy savings. This data-driven adaptive model approach is highly suited for tropical greenhouses experiencing dynamic climatic variations and offers a sustainable and efficient solution for greenhouse microclimate management
Low-Temperature Carbonization on Biochar from Agricultural Waste for Heavy Metal Removal
Full text linkBiochar from agricultural waste has many applications in the field of agricultural and wastewater treatment. In this study, biochar derived from exhausted kahwa coffee (EKC) was produced at low carbonization temperatures (200–400°C) for the removal of copper (II) ions (Cu2+) from aqueous solutions. The EKC biochar exhibited a removal efficiency of 92.5% under optimal conditions. The biochar was also subjected to surface characterization for further investigation of the varied capacity removal of the EKC biochar at low temperatures. BET analysis was performed on the EKC biochar to gather information on the surface area and pore size, and the structure of the formed pores was imaged using SEM. Furthermore, the elemental content and functional groups on the surface of the EKC biochar were determined by EDX and FT-IR analyses. The results showed that the surface and pore sizes of the EKC biochar had an interplay with the capacity removal of the EKC biochar during low-temperature carbonization. Meanwhile, it was also confirmed that the elemental ion content and the surface functional groups showed a stronger relation to the removal capacity of the EKC biochar at each low temperature applied
Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization
Full text linkShallots (Allium cepa L.) are a major horticultural commodity in Indonesia, with a production of 1.98 million tons in 2022, representing 13.59% of the total national vegetable production. Accurate forecasting of agricultural commodity prices is fundamental to sustainable development in the agricultural sector and contributes to broader economic stability. This study uses the random forest regression algorithm, a supervised machine learning technique that utilizes ensemble learning to combine multiple decision trees. This approach offers advantages in modeling non-linear relationships for agricultural price prediction while also reducing the risk of overfitting, resulting in more accurate and stable forecasts compared to individual decision trees. The purpose of this research is to develop and optimize a shallot price forecasting model using random forest regression. The optimized model, using 50 decision tree estimators, successfully predicted up to 15 months ahead of monthly prices and achieved an RMSE of 2363.15 and a MAPE of 8.71% in validation, then a MAPE of 10.31% in test evaluation
Performance Comparison of Microclimate Control ANFIS vs Fuzzy Logic in Plant Factory
Full text linkPopulation growth and the reduction of agricultural land necessitate the application of technology to enhance agricultural productivity. A plant factory is an advanced agricultural technology that enables indoor plant production by precisely regulating the microclimate for optimal growth. While fuzzy logic algorithms have been applied for microclimate control, the use of an adaptive neuro-fuzzy inference system (ANFIS) has not been explored. This research aims to develop a microclimate monitoring and control system based on ANFIS and fuzzy logic in a plant factory and compare their performance. The study involves five stages: designing control system schemes, developing hardware and software, testing, analyzing data, and comparing system performance. Microclimate data from both systems were analyzed using the Mean Absolute Error (MAE) metric and visualized through performance graphs. The results indicate that the plant factory with ANFIS control achieved MAE temperature values of 1.18°C and 1.48°C and MAE humidity values of 14.68% and 12.48%, while the fuzzy logic control system yielded MAE temperature values of 1.68°C and 1.60°C and MAE humidity values of 13.02% and 12.31%. Based on the MAE values, the ANFIS control system demonstrated better temperature regulation than fuzzy logic; however, neither system provided optimal microclimate control. These findings highlight the potential of ANFIS for improving temperature regulation in plant factories, suggesting the need for further refinement and optimization of control strategies to enhance overall system performance
The research consists of five stages, namely designing ANFIS and fuzzy logic control system schemes, designing hardware, designing software, testing and analyzing data, and comparing the performance of the two control systems. Microclimate data from both control systems were then analyzed to see their performance by looking at the MAE (Mean Absolute Error) value. Analysis is also done by looking at the graph of running results. The results showed that the plant factory with ANFIS control system showed MAE temperature values of 1.18oC and 1.48oC and MAE humidity of 14.68% and 12.48% while the plant factory with fuzzy logic control system showed MAE temperature values of 1.68oC and 1.60oC and MAE humidity of 13.02% and 12.31%. The plant factory with ANFIS control system provides better performance in temperature regulation based on the MAE value obtained but has not provided good performance, either using ANFIS control system or using fuzzy logic control system
Optimizing Coffee Flavor Through Roasting and Manual Brewing Using Chemical and Sensory Approach: English
Full text linkThe global popularity of coffee has led to growing attention on how processing and brewing techniques influence its sensory attributes. This study analyzed the chemical content of coffee and assessed the combination of roasting and manual brewing methods on coffee flavor. The coffee types used were Arabica coffee and Robusta coffee. The roasts used were light, medium, and dark roast with AeroPress, Siphon, and V60 manual brewing methods. The experiment was arranged in a factorial arrangement within a Randomized Complete Block Design (RCBD), where coffee varieties served as blocks, and the treatment combinations of roasting and brewing methods were randomly assigned within each block. Data analysis includes two-way analysis of variance, biplot analysis, and the compromise programming method. The results showed that the selection of roast level and brewing method had a significant influence on the coffee\u27s chemical analysis and sensory profile. Light roasting and complex flavors were more acceptable than dark roasting, which tends to be heavy. Based on the panelists\u27 preference analysis using the compromise programming method, RLS (Robusta-Light roast-Siphon) emerged as the optimal choice, indicating that this combination balances all coffee taste criteria. The combinations ALV (Arabica-Light roast-V60), ALA (Arabica-Light roast-Aeropress), and AMA (Arabica-Medium roast-Aeropress) which tends to similar and provide a balanced, complex flavor profile, including aroma, acidity, and high overall quality. Arabica coffee combination ADS (Arabica-Dark roast-Siphon), ADA (Arabica-Dark roast-Aeropress), and ADV (Arabica-Dark roast-V60) which have less optimal visual and balance because dark roasting reduces the sensory criteria of coffee
The Effectiveness of Cocoa Pod Husk Activated Carbon as an Ethylene Adsorbent for Extending the Shelf Life of Cavendish Bananas
Full text linkOne of the abundant agricultural wastes in Indonesia that has not been optimally utilized is cocoa pod husk (Theobroma cacao L.). Cocoa pod husk is the main by-product of the cocoa bean processing. Cocoa pod husk has a high cellulose content, making it a suitable precursor for activated carbon production. Activated carbon can adsorb ethylene from climacteric fruits, extending fruit shelf life. This research aims to test the effectiveness of activated carbon derived from cocoa pod husk as an ethylene adsorbent to extend the shelf life of fruits, specifically Cavendish bananas. The research procedure consists of preliminary research and primary research. The initial research involved measuring ethylene production, synthesizing activated carbon from cocoa pod husk, testing the characteristics of the cocoa pod husk activated carbon, calculating the activated carbon\u27s capacity for ethylene adsorption, and determining the optimal amount of cocoa pod husk activated carbon. The primary research involved testing Cavendish bananas\u27 storage and display life by applying an ethylene adsorber bag (EAB) using perforated LDPE packaging until spoilage. The Cavendish banana samples originated from Klaten, Central Java, with a maturity level of 1. The test parameters included moisture content, weight loss, firmness, color, total soluble solids (TSS), and Total Titratable Acidity (TTA). The treatments in this study consisted of samples treated with EAB from cocoa pod husk and a control group without EAB. All treatments were performed in triplicate. The experimental design used was a Completely Randomized Design (CRD). The results showed that a well-defined porous structure, a rough surface, and numerous cavities characterize the cocoa pod husk activated carbon. It has an ethylene absorption capacity of 363 ppm/g. The ethylene production rate of Cavendish bananas observed during storage was 1,280 ± 227.5 ppm. The results showed that bananas treated with cocoa pod husk-activated carbon were still green on the 10th day compared to the control treatment, which had already spoiled. During the display period, Cavendish bananas could last up to 5 days before spoilage. Therefore, cocoa pod husk-activated carbon can delay ripening and spoilage, thus extending the shelf life of Cavendish bananas
Design of Microclimate Monitoring and Graphical Interface System for Indoor Vertical Hydroponic Based on User-Centered Design Technique
Full text linkMonitoring microclimate conditions, including temperature, humidity, and light intensity, is crucial for maintaining plant health and productivity in vertical indoor hydroponic systems. These conditions directly influence essential physiological processes such as photosynthesis and respiration, affecting growth and yield quality. Manual monitoring methods often suffer from inefficiencies such as slow data collection, operator dependency, and human error. This can delay responses to sudden microclimate changes, leading to plant stress and reduced productivity. This study aims to design a real-time microclimate monitoring and graphical interface system for indoor vertical hydroponics using a User-Centered Design (UCD) approach. The system integrates DHT11 and BH1750 sensors to measure temperature, humidity, and light intensity, respectively, with data processing performed using a Raspberry Pi 3 Model B+. The system performance was evaluated over 24 h using the root mean square error (RMSE) and accuracy metrics. Based on this analysis, the RMSE values for temperature, humidity, and light intensity were 2.398, 1.483, and 392.225, respectively, with an overall accuracy of 97.33%, demonstrating high reliability. Two interface prototypes, Design A and Design B, were developed using distinct visual approaches and evaluated by ten respondents across six criteria: appearance, color, layout, information, icon, and font. Design A outperformed Design B, achieving a higher average score (49 versus 43.4), reflecting its superior clarity and intuitive design. These findings highlight the potential of the proposed system to enhance microclimate management and optimize plant growth in indoor vertical hydroponics. 
Estimating the Shelf Life of Instant Tempe in Various Packaging and Storage Temperature using the Arrhenius Model
Full text linkTempe is a nutritious food that is popular with many people. The problem is that tempe has a short shelf life and must be consumed immediately. Instant tempe is one of the innovative tempe products with a longer shelf life. This research aims to analyze the shelf life of instant tempe in various packages and at different storage temperatures using the Arrhenius model. The method used in this research was laboratory experimental, using 378 samples of instant tempe which were packaged in vacuum packaging, non-vacuum packaging, and cup packaging, and stored at cold temperature (12°C), room temperature (27°C), and hot temperature. (35°C). There are nine treatment combinations regarding the relationship between packaging and temperature, each treatment is carried out 3 times. Measurements for each treatment are carried out until the instant tempe cannot ferment to become tempe ready for consumption. This shows that the fungus Rhizopus oligosporus has died. The research results show that the CIE L* value of instant tempe decreases as the storage period for instant tempe increases. The shelf life of estimation results using the Arrhenius model for vacuum packaging and storage temperatures of 12°C, 27°C, and 35°C respectively is 36.98 days, 6.88 days, and 3.00 days. The use of non-vacuum packaging and storage temperatures of 12°C, 27°C, and 35°C were respectively 15.84 days, 5.63 days, and 2.38 days. In comparison, the use of cup packaging and storage temperatures of 12°C, 27 °C, and 35°C respectively are 17.62 days, 6.31 days, and 2.80 days
Kaji Terap Fertigator Otomatis Nirdaya (FONi) pada Budidaya Aneka Terong (Solanum melongena)
No full textWater scarcity, intensified by climate change and pollution, necessitates innovative irrigation approaches to sustain agricultural productivity. The Automatic Unpowered Fertigator (FONi) represents a solution that integrates automation without electricity, using evapotranspiration-driven subsurface irrigation to deliver water and nutrients directly based on plant demand. Unlike conventional systems, FONi operates entirely without external energy input, offering a low-cost and sustainable alternative for smallholder farmers. Previous applications in various crops have demonstrated significant water savings and increased productivity, indicating its strong potential as a scalable technology for resource-limited agriculture.This study evaluated the performance of FONi in cultivating four eggplant varieties under greenhouse conditions in Bekasi City, an area facing increasing competition for water resources. Over a 118-day growing period, plant growth, water use, crop coefficients (Kc), and productivity were monitored. Results showed Kc values ranging from 0.1 to 1.8, reflecting dynamic water demand throughout plant development. The long purple variety attained the greatest height (99.8 cm), while pondoh and white varieties achieved higher water productivity (up to 4.0 g/L) and land productivity approaching 1,120 g/m². Total irrigation water use was 1,329.3 liters, with an overall application efficiency of 98.9%. These findings demonstrate that integrating FONi with appropriate crop selection provides an efficient and sustainable strategy to optimize water use and enhance yield, supporting precision agriculture and climate-resilient food systems in drought-prone regions