JTEP (Jurnal Keteknikan Pertanian) P-ISSN: 2407-0475 E-ISSN: 2338-8439, previously named Agricultural Engineering Bulletin, is an official publication of the Indonesian Society of Agricultural Engineers (ISAE) in collaboration with the Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Technology and Engineering, Bogor Agricultural University (The MoU can be downloaded here). JTEP starting in 2025 will be published four times a year in March, June, September, and December. JTEP is a peer reviewed journal that has been accredited SINTA 2 by the Ministry of Research, Technology and Higher Education Number 177/E/KPT/2024 which is valid for 5 (five) years since volume 11 issue 2, 2023 to volume 16 issue 1, 2028. JTEP has been registered in Crossref, Indonesian Publication Index (IPI), Google Scholar, and other scientific databases. JTEP receives manuscripts of research results or scientific review in agricultural engineering related to farm structures and environment, agricultural and biosystem engineering, renewable energy, postharvest technology, food engineering and agricultural information system. The articles sent by the author - must be an original script and is not being considered for publication by other journal or publishers - should be written in accordance with the writing guidelines and submitted online via https://journal.ipb.ac.id/index.php/jtep. Editors can revise the paper without changing the substance and content after a blind review process. For further information and correspondence, please contact the secretariate of Jurnal Keteknikan Pertanian, Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Technology and Engineering, Bogor Agricultural University, Kampus IPB Darmaga Kotak Pos 220, Bogor 16002; Phone: +62 251 8624503 Fax: +62 251 8623026; E-mail: jtep@apps.ipb.ac.id
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Prototype of AI-Integrated Chatbot for Shallot Price Forecasting and Advisory Support to Assist Farmer Decision Making
Forecasting agricultural commodity prices is a fundamental tool for sustainable development in the agricultural economy and broader economic stability. With rapid and simple access to information on future prices, farmers can plan their planting schedules to optimize profits. This study presents a prototype AI chatbot that integrates price forecasting and advisory functions to assist farmers in decision-making and interact as an extension agent. Price forecasting employed Random Forest regression, achieving MAPE of 8.34% (training), 13.98% (validation), and 15.62% (testing). The chatbot was developed to access price forecasting information for the next four months. This system also integrates an LLM-AI model for consultations on planting schedules and other topics using a trusted knowledge base. During the testing phase, the chatbot successfully made predictions, provided recommendations, and interacted as an extension agent. Although demonstrating promising results, this study is limited to shallot price forecasting in Yogyakarta, highlighting the need for broader commodity and regional coverage in future studies. Unlike previous studies that focused only on forecasting or advisory, this study integrates predictive analytics with conversational AI in a farmer-friendly chatbot.
Copyright (c) 2026 Jurnal Keteknikan Pertanian https://creativecommons.org/licenses/by-sa/4.0
Effect of Soy Protein Isolate (SPI) Concentration on Anthocyanin-Based Smart Indicator Labels from Purple Sweet Potato Waste
Indonesia has a high level of food waste, with up to 30% of the country’s total fruit production contributing to it. Smart packaging, such as that with anthocyanin-based freshness indicator labels and Soy Protein Isolate (SPI), can help reduce waste by providing visual information about the product’s condition within the packaging. This research aims to determine the effect of SPI concentrations on the characteristics of anthocyanin-based smart packaging indicator labels extracted from purple sweet potato waste (Ipomoea batatas (L.) Lam). The indicator label was made using cornstarch (2.5%) as the matrix, glycerol as the plasticizer, and SPI as the crosslinking agent. The researchers employed a simple Completely Randomized Design with SPI concentration variations of 0%, 1%, 2%, 3%, and 4%. The researchers tested several parameters on the indicator labels, including pH sensitivity, color stability against storage temperature (4°C and 25°C), water absorption capacity, color reversibility, and biodegradability. The results indicate that adding SPI affects label characteristics, with 1% SPI concentration yielding the best performance. The 1% SPI concentration obtained the lowest color stability value at cold temperatures (∆E*ab = 5.15), the highest biodegradation rate (97.33% on day 4), the most varied color response to pH, and good physical durability of the label. Therefore, a 1% SPI concentration is recommended as the most effective formulation for an anthocyanin-based freshness indicator label.
Design and Fabrication of a Microcontroller-Based Automatic LED Grow Light Array System for Leafy Vegetables in Indoor Vertical Hydroponic
Indoor vertical hydroponic systems require precise and energy-efficient lighting to support optimal growth of leafy vegetables. This study presents the design and fabrication of a microcontroller-based automatic LED grow light system tailored for indoor hydroponics. A tailored LED grow light is necessary because each crop responds uniquely to light quality and intensity, and a customized spectrum ensures optimal growth while minimizing energy use. The system integrates a red–green–blue LED configuration (70:10:20%) with an ESP32 microcontroller, real-time clock (RTC), BH1750 light sensor, keypad interface, and LCD. The lighting cycle was programmed for 12 hours per day and tested continuously over two days. The system demonstrated accurate scheduling, with LED activation at 06:00 and deactivation at 18:00 and a timing deviation of only 1–2 seconds. During operation, the system maintained a stable photosynthetic photon flux density (PPFD) of 260–275 µmol/m²/s, producing an estimated daily light integral (DLI) of about 12 mol/m²/day, which is suitable for leafy vegetable production. Light distribution analysis using cubic interpolation showed that increasing the lamp height from 20 cm to 30 cm improved spatial uniformity, with the most uniform distribution achieved at 30 cm despite a slight reduction in intensity. The system consumed 2.65 kWh per day, covering four LED arrays and the control module. Overall, the proposed system offers a reliable, programmable, and energy-efficient lighting solution for indoor hydroponic environments, supporting sustainable crop production through precise scheduling and an optimized spectral configuration.
Copyright (c) 2025 Jurnal Keteknikan Pertanian https://creativecommons.org/licenses/by-sa/4.0/
Wood Waste Charcoal Briquettes: Physical and Thermal Characteristics Based on Particle Size and Wood Type
The depletion of fossil fuel reserves and rising fuel costs have highlighted the need for alternative household energy sources. Biomass waste, particularly sawmill residues, has potential for conversion into solid fuel in the form of charcoal briquettes. This study evaluated the physical and thermal characteristics of charcoal briquettes produced from Bus wood (Melaleuca sp.) and Rahai wood (Acacia sp.) sawmill waste at particle sizes of 10, 30, and 60 mesh. Briquettes were produced through carbonization, size reduction, binder mixing, molding, and drying, followed by evaluation of physical and thermal properties. The results showed that Bus wood briquettes had a higher density (0.56–0.60 g/cm³) than Rahai wood briquettes (0.46–0.58 g/cm³). Moisture content ranged from 10% to 13%, with the highest value observed in Rahai briquettes at 60 mesh, which affected combustion stability. Thermal analysis indicated that Bus briquettes at 60 mesh achieved a high peak temperature (608 ᵒC) and a high combustion time efficiency (92.31%), while Rahai briquettes exhibited a higher peak temperature at a coarser particle size (10 mesh, 604 ᵒC). Overall, Bus briquettes with finer particle size showed favorable physical properties and combustion behavior. These findings indicate that Bus wood waste briquettes have potential for further development as household-scale solid fuel. Keywords: charcoal briquettes, briquette properties, particle size, wood waste biomass
Copyright (c) 2026 Jurnal Keteknikan Pertanian https://creativecommons.org/licenses/by-sa/4.0/
Land Carrying Capacity Assessment for Harmonizing Environmental and Anthropogenic Pressures in the Tiworo Watershed
The Tiworo Watershed on Muna Island, Southeast Sulawesi, is experiencing increasing ecological and anthropogenic pressure threatening ecosystem sustainability. Land cover change has intensified surface runoff and affected downstream and coastal ecosystems. This study assesses land carrying capacity in the Tiworo Watershed based on biophysical characteristics to support management. The assessment applies three key indicators—Percentage of Critical Land (PCL), Percentage of Vegetation Cover (PVC), and Erosion Index (EI) in accordance with Minister of Forestry Regulation No. P.61/Menhut-II/2014. A spatial analysis approach using Geographic Information Systems (GIS) was employed. Secondary data was obtained from satellite imagery and relevant agencies, including watershed boundaries, land use and land cover, critical land maps, rainfall, soil type, and slope. Soil erosion was estimated to be using the Universal Soil Loss Equation (USLE), with tolerable erosion (ETOL) values from previous studies. Land carrying capacity was calculated using a regulation-based weighted scoring method, in which PCL, PVC, and EI were weighted at 50%, 25%, and 25%, respectively, to generate a composite score. The results show that 65.4% of the watershed is classified as potentially critical land, with 8.14% categorized as critical to very critical. Vegetation cover remains high at 89.57%, while the erosion index reached 13.65, exceeding tolerable limits across more than half the watershed. The composite land carrying capacity score of 87.5 places the watershed in a good sustainability category despite localized degradation. These findings highlight the need to control land conversion, strengthen soil and water conservation, rehabilitate degraded land, and integrate land carrying capacity into spatial planning. Keywords: Carrying Capacity, Land Sustainability, Tiworo Watershed, Watershed Management.
Carrying Capacity, Land Sustainability, Tiworo Watershed, Watershed Management.
Sustainable Urban Farming Rack Design Using Eco Friendly Materials with Variety Load
Vertical farming requires a structurally sound and environmentally sustainable racking system; however, existing research largely neglects the evaluation of the structural safety of eco-friendly racking materials under various operational loads. This gap necessitates further rigorous mechanical performance assessments to ensure the long-term reliability of the system. This study aims to develop a vertical farming racking design with an adequate safety factor by examining its structural response to various load scenarios. A design-construction methodology supported by SolidWorks simulations was used to evaluate stress distribution, strain behavior, and deformation patterns. Simulation results using SolidWorks software indicate that the maximum compressive stress at the loaded joint surface is 13.734 MPa, while the minimum stress is 1.149 MPa. The highest load occurs at the fourth (bottom) joint level due to pump-induced water pressure. The maximum strain recorded is 0.00491, with a minimum of 0.00041. The top shelf level exhibits a maximum displacement of 48.165 mm, while the minimum displacement is 0 mm. This shows that the material and shape of the frame that has been made are in the safe category with a safety factor (sf) of 2.4. These findings indicate that the proposed rack design maintains structural integrity within safe limits and is suitable for application in vertical farming systems.
Physicochemical Properties of Ambarella Fruit Leather with Variations in Gum Arab Concentration
Fruit leather is a thin sheet product made from dried fruit pulp until it has a flexible texture and can be rolled. Ambarella was chosen as the raw material because it has a sour taste and is naturally rich in vitamin C. The elasticity of fruit leather is influenced by the type of binding agent used, one of which is gum arab. Gum arab is a polysaccharide from the sap of Acacia sp. that is stable at pH 2–7 and resistant to acidic conditions and high temperatures. This study aimed to determine the changes in physicochemical characteristics of ambarella fruit leather with the addition of gum arab at different concentrations. The formulation used five gum arab concentrations (0%, 0.6%, 0.8%, 1%, and 1.2%) and three replications. The process included preparing puree, mixing with sugar and gum arab, and drying into sheets. Parameters tested were moisture content, pH, total sugar, total soluble solids, vitamin C, crude fiber, thickness, and texture. The results showed chemical properties which are moisture content 14.10–17.08%; pH 3.09–3.47; sugar 20.00–20.02%; soluble solids 72.43–74.77%; vitamin C 5.57–12.61%; and crude fiber 1.58–2.23%. Physical properties such as thickness 1.27–1.31 mm; tensile strength 0.03–0.16 MPa; elongation 7.01–10.53%; hardness 13.66–78.85 N; gumminess 5.28–57.88 N; and chewiness 3.75–49.20 N. It can be said that the variations in gum arab significantly affected both chemical and physical properties of ambarella fruit leather is a healthy snack. Keywords: ambarella, fruit leather, gum arab, vitamin C.
Microclimate Fluctuations and Temperature Modelling of Naturally Ventilated Greenhouse Nurseries in Tropical Region
Wood production from industrial plantations in Indonesia has increased from 18.8 to 49.6 million m³ (2015–2022). This requires an increase in the supply of seedlings in terms of both quantity and quality. Generally, seedling production involves the use of seeds and cuttings in greenhouses, followed by their relocation outdoors once they are nearly ready for planting. The success of greenhouse seedling production is often hampered by challenges related to temperature increases beyond the tolerance limits of the seedlings, which disrupts their growth. This study aims to identify temperature fluctuations inside greenhouses where Eucalyptus seeds are grown as a source for the pulp and paper industry and to develop a heat transfer model suitable for the conditions of these greenhouses. Climate conditions inside and outside the greenhouses were measured using automatic weather stations at 30-min intervals over five months. The developed heat transfer model explains how external climate changes affect the temperature inside the greenhouse. Based on the greenhouse conditions, differences in temperature between the floor and roof can be disregarded, resulting in a simpler heat transfer model. This model yields a coefficient of determination (R²) of 0.9051 and a root mean square error (RMSE) of 1.31°C. Temperature fluctuations inside the greenhouse are relatively more stable, with an average temperature 0.1°C lower and humidity 2.9% higher than those outside. A limitation of the model is its inability to accurately predict extreme outdoor temperatures. Therefore, this model requires further development, including considering the possibility of changes in heat transfer parameter values at more extreme temperatures.
Application NIR Spectroscopy for Prediction Soluble Solids Content and Classification of Tomatoes During Storage
Tomatoes are a horticultural commodity that is highly susceptible to quality degradation after harvest; therefore, appropriate postharvest handling is required to maintain quality. This study aims to evaluate the potential of near-infrared (NIR) spectroscopy for assessing tomato quality by applying partial least squares (PLS) to predict soluble solids content (SSC) and linear discriminant analysis (LDA) for classification based on storage temperature and ripeness level, with SNV pretreatment. Tomato samples were stored at 10 °C and 28 °C and observed at the breaker and pink ripeness stages. The best PLS model was obtained with SNV pretreatment and 10 latent variables, yielding R² calibration = 0.89, RMSEC = 0.19°Brix, R² prediction = 0.80, and RMSEP = 0.26 °Brix. The RPD value of 2.04 and the RER of 8.08 indicate that the model has a good predictive ability for evaluating tomato SSC. Meanwhile, LDA distinguished storage temperature better (accuracy 89.13%) than ripeness level (accuracy 65.21%). These results demonstrate that NIR spectroscopy can be used as an effective nondestructive method for analyzing the SSC of tomatoes during storage, reflecting the levels of sugars, organic acids, and other soluble compounds that contribute to the taste and overall fruit quality. Keywords: NIR Spectroscopy, Soluble Solids Content, Storage Temperature, Ripeness Level, Tomato.
Copyright (c) 2026 Andasuryani Andasuryani, Raisal Maulana, Dinah Cherie https://creativecommons.org/licenses/by-sa/4.0
Study the Application of Automatic Unpowered Fertigator (FONi) in Eggplant (Solanum melongena) Cultivation
Water 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.
Dynamic Thermofluid Study of Petrodiesel Droplet Combustion with Variations of Kesambi Biodiesel Composition Accompanied with TiO₂ Catalyst
The increasing global energy demand encourages the use of sustainable alternative fuels with combustion performance equivalent to petrodiesel. This study focuses on the droplet combustion characteristics of petrodiesel and biodiesel kesambi blends (B0, B10, B20, B30 B40 and B100), both without and with the addition of 100 ppm TiO₂ nanoparticle catalyst. Analysis was carried out on ignition delay, combustion duration, flame height, peak temperature, and flame visualization patterns. The results show that increasing the biodiesel fraction tends to prolong the combustion time and decrease the peak temperature, while the addition of TiO₂ provides significant improvements in the form of shorter ignition delay, higher peak temperature, and more stable flame. The novelty of this study lies in the use of a droplet approach to explore the role of TiO₂ catalyst in kesambi biodiesel, which is still limited in the literature. Thus, nanoparticle catalysts are proven to have great potential in increasing the efficiency and sustainability of the use of biodiesel–petrodiesel blends.
Biodiesel; Droplet; TiO2 Catalyst; Combustion Characteristics.
Portable NIR Spectroscopy for Cocoa Bean Re-fermentation Analysis: A Rapid and Reliable Technique
Portable near-infrared (NIR) spectrometers may examine samples directly on-site, speeding up data gathering. However, the NIR spectrometer has a limited wavelength, ranging from 740 to 1,070 nm, whereas previous studies used a longer wavelength. The research aims to determine the fermentation index, pH, and moisture content of fermented dry cocoa beans using a portable Near-Infrared (NIR) spectrometer. The NIR spectra were preprocessed using several methods, including the Savitzky-Golay first and second derivatives (SG 1 and SG2), Linear Baseline Correction (LBC), Multiplicative Scatter Correction (MSC), comparative Partial Least Square Regression (PLSR) modeling, and Artificial Neural Network (ANN). To simulate different fermentation levels, unfermented cacao beans underwent pretreatment fermentation for durations of 0, 24, 48, and 72 hours. The prediction outcomes of ANN models, when applied to dried fermented cocoa beans with data preprocessing, offered better results in comparison to PLSR models, with strong correlation, lowest RMSEC, and highest residual predictive value. The most effective method for predicting fermentation index was ANN combined with LBC preprocessing, while optimal pH models were applied using the SG2 method. The effective moisture content models were developed using MSC preprocessing. The analytical approach of portable NIR spectroscopy produced rapid and accurate results to determine the quality of ground-dried cacao bean fermentation.
The Effectiveness of Cocoa Pod Husk Activated Carbon as an Ethylene Adsorbent for Extending the Shelf Life of Cavendish Bananas
One 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's capacity for ethylene adsorption, and determining the optimal amount of cocoa pod husk activated carbon. The primary research involved testing Cavendish bananas' 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.
Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization
Shallots (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.
GIS-Based Spatial Analysis for Optimizing Spare Parts Distribution of Combine Harvesters in Lampung, Indonesia
Rice harvesting machine in Lampung Province has been commonly used for both personal use and contracting system. This opens up business opportunities for the provision of spare parts and machine repair services, especially during the main harvest season. Determining office locations or business policies in a region requires an analysis of the internal and external factors of the business itself. The analytical method used in this study was Spatial Data Analysis (SDA) to determine the types of strategies and policies that must be carried out by dealers of Kubota brand harvesting machines in Lampung Province. This decision support system is based on the results of spatial data analysis at the sub-district level. The results of spatial data analysis that combines data on paddy field area, slope level, and machine acceptance level show that there are six groups of potential priority areas included the sub-district recommendation for placing part shop and comparing with the existing active dealer part shop. There are six areas group, and dealer has cover 4 of them. Dealer is suggested to add two more-part shop that located in Suoh and Sungkai Utara to cover all areas group that can cover all area within 2 hours by motorbike. Keywords: Combine harvester, spatial analysis, location determination analysis, decision support system
Combine harvester, spatial analysis, location determination analysis, decision support system
FONi's Performances in the Hydroponic Cultivation of Various Vegetables
Land conversion, a phenomenon being encountered today, has led to a decline in the availability of agricultural land. On the other hand, population growth requires more food, which means more farmland is needed. Hydroponics is a method to grow crops intensively in any form of space without using soil, but only nutritious water as the growing medium. Hydroponics can produce high-quality crops and is easy to implement, making it popular in urban areas. However, it requires a continuous flow of water using electric pumps, which ultimately increases investment and operating costs. In this study, FONi (An Unpowered Automatic Fertigator) was tested to maintain water levels and flow rates as required by the plants. FONi is designed to replace the water lost by plant evapotranspiration, utilizing an automatic water valve. Here, FONi demonstrated good performance in growing three types of vegetables, namely water spinach (kangkung), choy sum (caisim), and spinach (bayam). The water productivity of each was 47.51 kg/m³, 43.80 kg/m³, and 21.25 kg/m³, respectively, and the land productivity was 1.35 kg/m², 0.97 kg/m², and 0.57 kg/m², respectively. The overall water efficiency was above 80%. By applying FONi, hydroponic cultivation activities at the research site, which had been suspended, can resume without incurring electricity costs.
Hydroponic, FONi, Land productivity, Water productivity, Water efficiency
Correlation Analysis Between Water Discharge and Suspended Load During The Rainy Season in The Downstream Ciliwung River, Jakarta
The Ciliwung River Basin originates at the foot of Mount Pangrango in West Java and flows downstream to Jakarta, with a catchment area of approximately 387 km². The downstream segment in Jakarta is highly vulnerable to sedimentation, which reduces flow capacity, damages infrastructure, and increases flood risk. These conditions highlight the urgency of analyzing the relationship between water discharge and total suspended solid (TSS) load in the downstream Ciliwung River as a scientific basis for river management in Jakarta. This study analyzed the correlation between water discharge and total suspended solids (TSS) in the downstream segment of the basin. Water samples were collected from three observation points, resulting in 11 observations and 33 samples in January 2025. The relationship between water discharge and TSS load was examined using linear regression correlation analysis in order to identify the degree of association and to establish the regression model between both variables. TSS ranged from 8.5 to 284.5 mg/L, while Water discharge varied from 2.55 to 45.47 m³/s. The correlation coefficients were obtained as 0.9721 at the upstream site, 0.9151 at the middle stream site, and 0.0006 at the downstream site. A strong positive correlation was found in the upstream and midstream segments, while the downstream segment showed no significant correlation, likely due to hydrological and tidal effects. These results contribute to a deeper understanding of sediment transport dynamics and can inform sustainable sediment management and dredging strategies in urban river systems.
Impact of Postharvest Processing on the Metabolite Profile of Arabica Green Coffee Beans
Postharvest techniques greatly affect the composition and quality of green coffee beans by altering some primary and secondary metabolites. This study aimed to assess the impact of three postharvest processing techniques (natural, honey, and fully washed) on the phenolic compounds, antioxidant activity, caffeine, protein, and color in Arabica green coffee beans from Solok Regency, Indonesia. Spectrophotometric and colorimetric analyses were performed. Data were analyzed using Duncan’s multiple range test, multivariate PCA, and heatmap clustering. Results showed that honey process had the highest polyphenol content of 3918.61 ± 349.85 mg GAE/g, whereas beans that were fully washed had the highest antioxidant activity (1.41 ± 0.21%) and caffeine content (1.41 ± 0.21%). Natural processing yielded the highest protein content (24.26 ± 0.40%). There were significant differences in the hue angle that were related to some of the biochemical differences among the treatments. This research emphasizes the importance of postharvest processing in defining the metabolite profile and quality features of green coffee beans. These findings can assist in formulating the processing steps to be used in refining coffee and maximizing its health-related advantages.
The Effect of Light Exposure Duration on the Oxidative Stability and Sensory Profile of Avocado (Persea americana)-Based Mayonnaise
Avocado-based mayonnaise presents a novel formulation aimed at reducing the saturated fat content typically associated with conventional emulsified condiments. This investigation sought to delineate the impact of light exposure duration on the oxidative integrity and sensory parameters of mayonnaise derived from Persea americana. Employing a completely randomized design (CRD) comprising a single experimental factor—duration of light exposure (0, 24, 48, and 72 hours) each treatment was replicated thrice to ensure experimental reliability and statistical robustness. Analytical endpoints included moisture content, pH, peroxide value, free fatty acid (FFA) concentration, and sensory attributes (color and aroma). Statistical analyses involved ANOVA followed by Duncan’s Multiple Range Test at a 5% significance threshold. The results revealed that prolonged exposure led to a statistically significant decline in sensory acceptance (color score: 6.07 to 3.47; aroma score: 6.20 to 3.67) and elevated oxidative markers including moisture content, peroxide value, and FFA. Among the tested conditions, treatments L1 and L2 preserved compositional and sensory quality within permissible SNI standards for FFA (<3%). The study underscores the necessity of light-protective packaging in mitigating oxidative degradation in avocado-based emulsions and emphasizes the relevance of optimizing environmental exposure parameters during storage and distribution of lipid-rich food emulsions.
Optimizing Coffee Flavor Through Roasting and Manual Brewing Using Chemical and Sensory Approach
The 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's 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' 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.
roasting, manual brew, analysis sensory coffee, biplot, compromise programming
Analysis of Temperature and Airflow Distribution during Carrot Drying Using a Tray Dryer Based on CFD Simulation
Carrot (Daucus carota L.) is a highly nutritious vegetable that is prone to spoilage due to its high moisture content. Drying is an effective method to extend its shelf life, with the tray dryer being one of the commonly used technologies. However, the efficiency of the drying process greatly depends on the uniformity of temperature and airflow distribution within the drying chamber. This study aims to analyze the distribution of temperature and airflow inside a tray dryer using Computational Fluid Dynamics (CFD) during the drying of carrots. Simulations were conducted at three different temperatures (50°C, 60°C, and 70°C) and two air velocities (2 m/s and 3 m/s) using SolidWorks, ANSYS Fluent, and Origin software. The simulation results showed that both temperature and airflow were unevenly distributed, particularly in the middle and upper sections of the dryer. This non-uniformity can lead to inconsistent drying and reduced product quality. Validation was carried out by comparing simulation results with experimental measurements using relative error analysis. The error values obtained ranged from 1.41% to 3.82% for temperature and 0.5% to 2.41% for air velocity, all below 4%. These results indicate that the CFD model is sufficiently accurate and can be applied to support the design and optimization of more efficient drying equipment.
ANSYS Fluent, Pengeringan, Perpindahan Panas, Suhu dan Thermodinamika
Shelf-Life Estimation of Dried Sago Noodles Using the Accelerated Shelf-Life Testing (ASLT) Method
Indonesia holds significant potential for sago (Metroxylon sp.) development, especially in the eastern regions. Sago starch offers an alternative to wheat flour in noodle production, promoting local food diversification. This study aims to estimate the shelf-life of dried sago noodles using the Accelerated Shelf-Life Testing (ASLT) method based on selected quality parameters. Dried sago noodles were produced from sago starch sourced from Merauke, South Papua, and stored at 35°C, 45°C, and 55°C. Quality attributes—moisture content, aroma, and taste—were analyzed during storage to model degradation kinetics and determine activation energy (Ea) using the Arrhenius approach. Among the tested parameters, aroma exhibited the lowest activation energy (636.04 cal/mol), indicating it as the critical parameter for predicting shelf-life. Based on the regression of ln k versus 1/T, the shelf-life at 27°C (room temperature) was estimated at 49 days. The study concludes that aroma degradation is the key factor in determining the shelf-life of dried sago noodles, and that accurate sensory evaluation using trained panelists is crucial for improving the reliability of shelf-life prediction.
Accelerated Shelf-Life Testing (ASLT), activation energy, Arrhenius equation, sago noodle, shelf-life modeling
Prediction of Chemical Content in Robusta Coffee Beans Using Near Infrared Spectroscopy and Artificial Neural Network
A rapid method to predict chemical content of robusta coffee is necessity, instead of chemical method that time consuming and expensive. This research aimed to predict chemical content of Pagar Alam robusta coffee beans using Near Infrared Spectroscopy (NIRS) and calibration through Principal Component Analysis-Artificial Neural Network (PCA-ANN). Reflectance of coffee bean was measured using NIR Flex N500 Spectrometer (1000–2500 nm), followed by determination of its chemical content using chemical method. The reflectance data were processed using spectra pre-treatment, then calibrated and validated with chemical content using PCA-ANN. The best model for moisture content was achieved with No1SG1 pre-treatment and 8 PC (r = 0.95; RPD = 2.95; consistency = 90.36%). Protein content can be predicted using 8 PC and No1SG1 pre-treatment (r = 0.92; RPD = 2.38; consistency = 96.78%). Good fat prediction employed No1SG1 pre-treatment and 10 PC (r = 0.93; RPD = 2.19; consistency = 80.08%). Ash content can be predicted using 8 PC and SG1 pre-treatment (r = 0.93; RPD = 2.29; consistency = 83.78%). The best carbohydrate prediction was obtained with No1SG1 pre-treatment and 5 PC (r = 0.93; RPD = 2.73; consistency = 108.85%). The results indicate that NIR spectroscopy can be used to predict accurately the chemical content of Pagar Alam robusta coffee beans.
Copyright (c) 2025 Jurnal Keteknikan Pertanian
Performance Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Logic Based Microclimate Control System in Plant Factory
Population 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.
Modification of a Rice Cooker into Black Garlic Aging Chamber and Performance Analysis
This study aimed to modify a conventional rice cooker into a temperature-controlled aging chamber for black garlic production. The modification involved disabling the automatic switch and replacing the internal sensor with an external DS18B20 sensor, connected to an STC-1000 digital thermostat. The system was designed to maintain consistent heating at three temperature settings: 60 °C, 70 °C, and 80 °C, and enabled real-time temperature monitoring using a thermo recorder. Results showed that the modified rice cooker with only a thermostat had a temperature fluctuation of 10.8 °C. When combined with 4 mm cardboard insulation, the fluctuation decreased significantly to 3.8 °C.
Keywords: Rice Cooker, Sensor, Heat, Thermostat, Thermocouple
Classification of Arabica Coffee Beans with Honey Processing Based on Starter Type Using Multi-Channel Spectra Sensor
Arabica coffee is significant commodity in Indonesia's agricultural sector, contributing as an income source for farmers and a major export revenue. The specialty coffee industry has begun to utilize microorganisms (starter) in the fermentation process, including the honey process, to obtain a distinctive flavor. However, the use of various starters in this process results in uniform coffee bean color, making it difficult to determine the authenticity of the type of starter used. This research classifies arabica coffee beans processed with different type of starter using multi-channel spectra sensor to ensure product quality and authenticity. This research uses arabica coffee beans that are still in the form of green beans in honey processed with three types of starters, namely Saccharomyces cerevisiae, Lactobacillus sp, and Rhizopus oryzae. Multi-channel spectra sensor with PLS-DA methods can classify arabica coffee beans processed in honey with 3 types of starters. The PLS-DA model built using de-trending pre-treatment was able to predict the calibration data of arabica coffee beans with starter types very well, with accuracy, sensitivity, specificity, and precision values, reaching 97%, 95%, 96%, 95%, respectively, and the PLS-DA model built using de-trending pre-treatment is able to predict the validation sample of arabica coffee beans with starter type very well, with accuracy, sensitivity, specificity, and precision values, reaching 100%.
Renewable Energy from Various Oil Palm Varieties in Penajam Paser Utara, Indonesia
Oil palm has the potential to serve as a strategic energy source for the national energy transition. This study evaluates the potential of four oil palm varieties Sain, Lonsum, Dumpy, and Marihat in Penajam Paser Utara (PPU) Regency as sources of biodiesel, edible oil, and solid biomass. The methods applied include fat content analysis, fatty acid profile evaluation, and utilization scenarios based on 2023 production data of Fresh Fruit Bunches (FFB) and Crude Palm Oil (CPO). The results showed that the Lonsum and Sain varieties, which have high fat content and are predicted to be dominated by saturated fatty acids (palmitic and stearic acids) based on literature-reported fatty acid profiles, are most suitable for biodiesel production. In contrast, the Dumpy and Marihat varieties exhibited lower fat content but are predicted to contain higher proportions of unsaturated fatty acids (oleic and linoleic acids), making them more suitable for edible oil applications. The Dumpy variety also has lower moisture content, making it favorable for solid biomass applications. With proportional allocation and conversion of FFB waste into briquettes, the production potential is approximately ±41,200 tons of biodiesel, ±26,159 tons of edible oil, and ±115,104 tons of solid biomass annually. The total energy potential reaches approximately ±3.49 million GJ per year. These findings indicate significant opportunities for the development of self-sufficient energy agroindustry based on oil palm to support energy security and sustainable development in the buffer zones of Indonesia’s new capital city (IKN).
oil palm, biodiesel, solid biomass, renewable energy, self-sufficient agroindustry, Penajam Paser Utara
Non-Destructive Prediction of Moisture Content in Cascara Using NIR Spectroscopy with PLS and PCR
Coffee cherry pulp is a by-product of coffee processing that has not been optimally utilized. Coffee cherry pulp can be dried to produce a herbal tea product, known as cascara. As an herbal tea product, moisture content is one of the most important quality parameters for assessing the quality of cascara. Therefore, a method is required to measure the moisture content of cascara. One of the methods developed is NIR spectroscopy, which is non-destructive, fast, and does not require chemicals. The purpose of this research is to explore the application of NIR spectroscopy in predicting cascara moisture content using partial least squares (PLS) and principal component regression (PCR) methods and to evaluate the performance of each method in building an optimal calibration model. Pretreatment of the spectrum data was carried out with standard normal variate (SNV), gap-segment 2nd derivative (dg2), and a combination of SNV+dg2. The results showed that the best prediction of cascara moisture content used the PLS calibration technique with dg2 pretreatment and five factors 5. The values obtained were Rc2 = 0.96, RMSEC = 0.87 %, SEC = 0.87 %, Rp2 = 0.90, RMSEP = 1.22 %, SEP = 1.16 %, and RPD = 3.44. Meanwhile, the PCR method produced good predictions using SNV pretreatment, with a factor of 8. The prediction results were Rc2 = 0.89, RMSEC = 1.40 %, Rp2 = 0.90, RMSEP = 1.33 %, and RPD = 3.15. NIR spectroscopy can predict the moisture content of cascara nondestructively and rapidly.
Cascara, Moisture Content, NIR Spectroscopy, PCR, PLS
Challenges in the Implementation of Internet of Things (IoT) in Irrigation and Fertilizer Management System in Indonesia
Agriculture is critical to many countries' 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.
Time Study Analysis on Rice Milling Process at Rice Milling Unit (RMU) “Teaching Industry” Unsoed
Rice is a major food crop in Indonesia that requires special attention in the milling process to ensure efficiency of working time. This study aims to identify the division of work elements in the rice milling process, determine the standard time used by operators, and provide recommendations for improving the work system in the Unsoed “Teaching Industry”. Research data were collected by measuring the time of each work element in the rice milling process using a stopwatch. The work elements are divided into two groups: rice milling elements and rice packaging elements. The analysis methods used include data adequacy and uniformity tests as well as calculations of standard time and normal time. The results of the analysis show that the standard time for the rice milling process is 5,920.55 seconds per 100 kg of rice, while the standard time for the rice packaging process reaches 27,549.81 seconds per 100 kg of rice. The time required is still relatively long compared to previous studies, so several improvements are recommended, including eliminating unnecessary work elements, carrying out thorough maintenance of the tools in the RMU, and placing a bucket in the outlet hole when changing sacks to reduce the time to collect scattered rice. In addition, in the rice packaging process, the element of rice sorting can be eliminated. Other recommendations include improving the working environment by encouraging workers to wear masks during the rice milling process.
Sawitania Christiany Dwi Utami Boru Situmorang