Article Per Year (5 Year)
p-Index From 2021 - 2026
3.893
P-Index
This Author published in this journals
All Journal HAYATI Journal of Biosciences Teknotan: Jurnal Industri Teknologi Pertanian 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

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search

 1   2   3   4 
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.
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.
GIS-Based Spatial Analysis for Optimizing Spare Parts Distribution of Combine Harvesters in Lampung, Indonesia Qouamunas Tsani Nuargimah; Radite Praeko Agus Setiawan; Mohamad Solahudin
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
Development of Tropical Grape Greenhouse Monitoring and Nutrient Delivery Scheduling using MQTT Protocol and IoT Supriyanto, Supriyanto; Solahudin, Mohamad; Sucahyo, Lilis; Aryawan, Putu Oki Wiradita; Widodo, Slamet; Susanto, Slamet
Teknotan: Jurnal Industri Teknologi Pertanian Vol 20, No 2 (2026): TEKNOTAN, Agustus 2026
Publisher : Fakultas Teknologi Industri Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jt.vol20n2.6

Abstract

Grape production in Indonesia is limited due to environmental conditions and other factors. However, grape consumption in Indonesia has become popular over the last decade. Nutrient and water delivery are scheduled based on the growth stages of the grape, which consist of vegetative and generative stages. Those dynamic schedules should be assisted using automatic monitoring and controlled using the message queuing telemetry transport (MQTT) and Internet of Things protocol. However, the performance of monitoring and control should be evaluated. This study aimed to evaluate the performance of Tropical Grape Greenhouse Monitoring and Nutrient Delivery Scheduling using the MQTT protocol and IoT technique. The research stages include system analysis, design, implementation, and testing. The outcomes of this study consist of a monitoring system and a control system. The monitoring system is equipped with a main control unit (MCU) based on an ESP32, along with sensors for air temperature and humidity (DHT21), light intensity (lux sensor), water temperature (DS18B20), and soil moisture. Data is transmitted via an Aedes-based MQTT broker installed on the Node-RED platform on a virtual private server (VPS) integrated with a MySQL database. The monitoring and control system is implemented in the grape greenhouse at the Agribusiness and Technology Park (ATP) at IPB University. Specifically, the control system is designed and implemented in the grape greenhouse to activate solid-state relays (SSRs) and contactors to operate water pumps and solenoid valves, all managed through a dashboard interface. System performance testing was conducted to evaluate reliability and latency at MQTT quality-of-service (QoS) levels using the one-way delay (OWD) method. The results indicate that the monitoring node’s latency at QoS 0 is relatively stable, averaging 59.51 milliseconds. For the control system, the average latency test was 63.07 milliseconds for QoS 0, 72.21 milliseconds for QoS 1, and 85.64 milliseconds for QoS 2.
Co-Authors Agus Buono Ahmar, Afdhalul Alvin Fatikhunnada Alvin Fatikhunnada Angga Firmansyah Aryawan, Putu Oki Wiradita Eni Sumarni Eni Sumarni Erniati Erniati Erniati Eti Rohaeti Eti Rohaeti Fadhilah Khairani, Fadhilah 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 Nurbaiti Araswati Offianda Kurniawan Omil Charmyn Chatib Prastono, Haryo Purwansya, Yuvicko Gerhaen Putri, Sindi Lestari Qouamunas Tsani Nuargimah Radite Praeko Agus Setiawan Ratu Yanra Dewi Rena Nurista Riyanti Riyanti Rokhani Hasbullah Romadhon, Akbar Roni Darpono Shadila Fira Asoka Shadila Fira Asoka Shandra Amarillis Slamet Susanto Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Sucahyo, Lilis 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