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All Journal HAYATI Journal of Biosciences Mechatronics, Electrical Power, and Vehicular Technology Jurnal Keteknikan Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Indonesian Journal of Electrical Engineering and Computer Science ALACRITY : Journal Of Education
Mohamad Solahudin
Department Of Mechanical And Biosystem Engineering, Faculty Of Agricultural Engineering And Technology, IPB University, Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Other

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Adaptive-Historical Energy-Efficient Temperature Control for Tropical Greenhouses Laumal, Folkes; Suhardiyanto, Herry; Solahudin, Mohamad; Widodo, Slamet
Jurnal Keteknikan Pertanian Vol. 13 No. 1 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.1.55-73

Abstract

Maintaining 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.
Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots Purwansya, Yuvicko Gerhaen; Solahudin, Mohamad; Supriyanto, Supriyanto
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.v13i2.350-360

Abstract

As the demand for shallot increases, wide-scale cultivation area must be managed efficiently. However, shallot productivity decreases every year because of plant diseases. Fusarium disease has an intensity up to 60% and can affect yield losses up to 50%. This study was conducted to develop the fusarium disease detection system for shallot using deep learning model and analyze the effect of preprocessing and augmentation adjustment. This study used YOLOv5 deep learning algorithm consisting of the following stages: (1) dataset acquisition, (2) dataset annotation, (3) dataset preprocessing and augmentation, (4) dataset training and validation, and (5) model testing and evaluation. A total 9,664 annotated dataset was trained to YOLOv5m pre-trained weights. Based on testing and evaluation results, precision, recall, and mean average precision (mAP) metrics of the model without preprocessing and augmentation were 55.5%; 54%; and 48.3% respectively. Metric values of the model were increased to 57.6%; 58.4%; and 54.1% respectively with adjustment of preprocessing and augmentation combination process. Percentage increase in metrics when compared to the control model for each value of precision, recall, and mAP were 2.1%; 4.4%; and 5.8%. This shows a significant impact on the addition of preprocessing and augmentation processes that match the characteristics of the dataset to increase the value of model performance. Keywords: Augmentation, Deep learning, Fusarium, Shallot.
Prediction of Phenotypic Parameters of Sugarcane Plants Based on Multispectral Drone Imagery and Machine learning Hasskavendo, Febri; Solahudin, Mohamad; Supriyanto, Supriyanto; Widodo, Slamet
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.v13i4.1182-1195

Abstract

Measuring phenotypic parameters is important in evaluating the productivity of sugarcane. Existing manual measurements are considered less efficient, so a better alternative method is needed. This research aims to explore the potential of using multispectral drone imagery and machine learning to estimate phenotypic parameters of sugarcane plants that are efficient, accurate, inexpensive, and support sustainable agricultural practices. Spectrum data captured by drones, namely Green, Red, RedEdge and NIR are used as inputs to estimate phenotypic parameters including brix value, number of stands, stem diameter, and plant height. Based on the results of machine learning model development, the ANN algorithm model is most effective in predicting Brix Value with R2 0.74 and RMSE 0.06 and number of stands with R2 0.68 and RMSE 2.13. All models could not predict stem diameter and plant height well. The best model to predict plant height was obtained by RF algorithm with R2 0.53 and RMSE 14.09. SVR algorithm was the best model to predict plant diameter with R2 0.39. and RMSE 0.49. This indicates that the effectiveness of an algorithm depends on the specific parameter being predicted and there is no dominant algorithm for all phenotypic parameters. Keywords: Machine learning, Multispectral drone imagery, Phenotypic parameter, Plant productivity, Sugarcane.
GIS-Based Spatial Analysis for Optimizing Spare Parts Distribution of Combine Harvesters in Lampung, Indonesia Nuargimah, Qouamunas Tsani; Setiawan, Radite Praeko Agus; Solahudin, Mohamad
Jurnal Keteknikan Pertanian Vol. 13 No. 4 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.4.642-652

Abstract

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
Design and performance of nutrient dosing control system for hydroponic chilli plant using fuzzy logic controller Prastono, Haryo; Solahudin, Mohamad; Supriyanto, Supriyanto
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.910

Abstract

The application of irrigation and nutrient provision is crucial for cultivating plants using hydroponic systems. This significance arises from the absence of natural nutrients in hydroponic growing media, which necessitates precise and tailored nutrient administration. This study aimed to discuss the design and construction of a nutrient dosing system employing both an on-off-based nutrient mixing control and a fuzzy logic-based fertigation control. Nutrient dosing system design entails establishing design criteria, functional and structural design, prototyping, programming, and testing. Performance testing involved a mixture of cocopeat and rice husk charcoal growing medium, with a 2-month-old chilli plant as the testing subject. The nutrient mixing control system resulted in a ready-to-use nutrient solution with a concentration of 1538.45 ppm, which slightly deviated from the 1500 ppm target. The total time required for nutrient mixing amounted to 3685.8 seconds. The calculations revealed a percentage error of 2.56 % for this nutrient mixing control system. The tested fertigation control system successfully maintained the moisture content of the growing medium within the available water zone with an error rate of 2.17 %. Observations over three days demonstrated that the control system activated fertigation processes twice daily, predominantly in the morning and evening. The total volume of fertigation administered ranged from 217 cm3 to 287 cm3 daily. All the components of the nutrient dosing system functioned effectively and performed well.
Artificial Neural Network Model for Shallot Disease Severity Prediction Using Drone Multispectral Imagery Angga Firmansyah; Mohamad Solahudin; Supriyanto Supriyanto
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 2 (2025): April 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i2.623-637

Abstract

Shallot plant diseases can reduce yields by up to 50% of total land area. Currently, shallot plant disease identification relies on direct observation, which is less effective and efficient due to varying intensities of disease and large cultivation areas. This study aims to develop a predictive model for shallot disease severity using multispectral drone imagery, apply Artificial Neural Network (ANN) algorithm to analyze multispectral band data, and evaluate the model's performance. The study used ANN algorithm with multi-layer perceptron regressor, involving following stages such as dataset acquisition, dataset stitching, dataset filtering and feature extraction, model development, and model evaluation. Multispectral data were taken using DJI Mavic 3 Multispectral drone, resulting 696 images per bands that were stitched into orthophoto map. The filtering process of plant objects yielded better model training results compared to unfiltered data. The optimal ANN model structure was identified as 4-6-2-1, with R² value of 0.9194 and MAE value of 0.0618. Model testing results demonstrated that using four input bands (G, R, RE, NIR) provided the best performance with R² value of 0.9194, followed by combination of two bands (R, RE) with R² value of 0.8883. This indicated that the R and RE bands were most strongly correlated with shallot disease severity. Keywords: Drone, Multi-layer perceptron, Multispectral imagery, Plant disease, Shallot.
Deep Learning-Based Detection for Early Germination Stages of Chili Pepper (Capsicum annuum L) Seedling in Greenhouse Jasmine Tasmara; Supriyanto Supriyanto; Mohamad Solahudin
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 4 (2025): August 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i4.1128-1139

Abstract

Nursery plays an important role on starting chili cultivation, determining the crop health, fertility from disease, and growth performance. Early-stage germination detection is necessary to minimize nursery failure and improve plant health, but manual detection is challenging for large scale nursery in the greenhouse. The aim of this research was to develop an automatic detection model integrated with a You Only Look Once (YOLO) based deep learning algorithm using RGB camera to monitor the chili germination stages. Method to detect germination was YOLO with several steps, included: (1) early stages chili germination images acquisition, (2) datasets preparations, (3) dataset annotation and labeling, (4) model development using deep learning YOLO algorithms, and (5) model testing and validation. The training of 11,423 images was conducted utilizing the YOLOv5 and YOLOv8 algorithms, which categorized into, three classes (germinated, not germinated, and cotyledon appearance). The model was evaluated using mean Average Precision (mAP), precision, accuracy, and recall with the respective values of 0.697, 73%, 75%, and 73% for YOLOv8, and 0.664, 70%, 73%, and 70% for YOLOv5. Both model achieved high accuracy, but YOLOv8 was better to detect and classify chili seedling growth stages than YOLOv5. This study also demonstrated that model can be implemented in real applications integrated with automatic monitoring system included in the model.   Keywords: Chili seedling, Deep learning, Detection system, Germination.
Development of Web-Based Application for Analysis and Design of Steam Power Plant System Parameters Using Biomass Fuel Offianda Kurniawan; Muhamad Yulianto; Mohamad Solahudin; Haris Mawardi; Lalu Muh Fathul Aziz Al Azhari
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 6 (2025): December 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i6.2439-2457

Abstract

The process of physically prototyping a power generation system is time consuming and costly. Adopting the concept of Digital Twin Technology offers a solution to improve the efficiency in prototyping processes. This study aims to develop a web-based application called ThePOCI for thermal analysis of steam power plant systems working with ideal Rankine cycle, and to evaluate the accuracy of the developed application. The ThePOCI thermal system application consisted of two main modules: Steam Power Plant Design and Combustion Analysis. Validation of the Combustion Analysis module revealed the largest calculation errors in the thermal-based model for variables including flue gas temperature (13.08%), temperature of boiler exit working fluid (16.93%), and turbine power (10.49%), yet all fall within the low error range. Validation of the Steam Power Plant Design module produced deviations of ideal and actual operating conditions of 2.22% and 0.88%, respectively, categorized as highly accurate. The validation results confirm that ThePOCI can accurately simulate the physical system of a steam power plant based on the ideal Rankine cycle. System emission calculations indicate potential for further research on the use of Calliandra biomass in Organic Rankine Cycle (ORC)-based steam power plants, identified as the fuel producing the lowest emissions at 3,742.20 kgCO2e/kW.
Design and Development of a Web-Based Thermal Application for Vapor Compression Refrigeration Systems Ratu Yanra Dewi; Muhamad Yulianto; Mohamad Solahudin; Leopold Oscar Nelwan; Ida Afriliana; Roni Darpono; N Nasruddin
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 1 (2026): February 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i1.19-32

Abstract

The growth of the global food industry has led to an increased demand for cold storage systems to maintain product quality. Cold storage systems based on the vapor compression cycle offer high energy efficiency. However, their design involves multiple stages, ranging from cooling load calculations to prototype development for performance evaluation. This study integrates digital twin–based thermal simulation with Life Cycle Climate Performance (LCCP) analysis into a single web-based platform, namely THE POCI, for cold storage design. The application allows system design, performance calculation, and estimation of the system emission. The development process followed the System Development Life Cycle (SDLC) methodology. Functional testing was conducted using Black-box Testing, while user evaluation was performed using the System Usability Scale (SUS). The results show that all modules provide the expected information and can be used effectively. Model validation against experimental data resulted in Mean Absolute Percentage Error (MAPE) values of 11% for compressor power, 17% for cooling capacity, and 14% for the coefficient of performance (COP). User evaluation involving 47 respondents across the four modules yielded a SUS score of 64.41, indicating that the application is well accepted and has an adequate level of usability.
Co-Authors Agus Buono Ahmar, Afdhalul Alvin Fatikhunnada Alvin Fatikhunnada Angga Firmansyah Eni Sumarni Eni Sumarni Erniati Erniati Erniati Eti Rohaeti Eti Rohaeti Fadhilah Khairani, Fadhilah Fahrezi, Rafli Arya Febri Hasskavendo Fenry Winna Mutawally Folkes Eduard Laumal Folkes Laumal Folkes Laumal folkes laumal, folkes Fuad Heru Setiawan Giska Priaji Gumilang Agus Gozali Haris Mawardi Hasskavendo, Febri Herry Suhardiyanto Heru Sukoco I Wayan Astika I Wayan Budiastra Ida Afriliana Ihsan, Mahlil Nurul Irmanida Batubara Jasmine Tasmara Jayawarsa, A.A. Ketut Kahfi Gunardi Kania Dewi, Kania Karlisa Priandana Khairani, Fadilah Khoirul Umam Kiswanto S. Heri Kudang Boro Seminar Lalu Muh Fathul Aziz Al Azhari Lilis Sucahyo Liyantono . Medria Kusuma Dewi Hardhienata Michael Alexander Hutabarat Mohamad Iqbal Suriansyah Mohamad Yanuar Jawardi Purwanto Muhamad Yulianto Muhamad Yulianto Muhammad Naufal Rauf Ibrahim N Nasruddin NANIK PURWANTI Nelwan, Leopold Oscar Nuargimah, Qouamunas Tsani Nurbaiti Araswati Offianda Kurniawan Omil Charmyn Chatib Prasetyo, Tegar Adi Prastono, Haryo Purwansya, Yuvicko Gerhaen Putri, Sindi Lestari Radite Praeko Agus Setiawan Ratu Yanra Dewi Rena Nurista Riyanti Riyanti Rokhani Hasbullah Romadhon, Akbar Roni Darpono Septiadi, Ananda Putra Shadila Fira Asoka Shadila Fira Asoka Shandra Amarillis Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Sucahyo, Lilis Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Tineke Mandang WULANDARI Y. Aris Purwanto Yanti, Delvi Yudi Chadirin Yudiwanti Yudiwanti Wahyu E. Kusumo Yuvicko Gerhaen Purwansya