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All Journal HAYATI Journal of Biosciences 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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Secondary Metabolites and Antioxidant Activity of Purwoceng (Pimpinella Pruatjan) Root Extracts from Various Hydroponic Planting Techniques Irmanida Batubara; Shadila Fira Asoka; Eni Sumarni; Herry Suhardiyanto; Mohamad Solahudin; Slamet Widodo; Supriyanto Supriyanto; Eti Rohaeti; Yudiwanti Wahyu; Folkes Laumal; Erniati Erniati
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 12, No 3 (2023): September 2023
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v12i3.641-652

Abstract

Purwoceng (Pimpinella pruatjan) is Indonesia’s native herbs that grow in the highlands but its existence is hard to find. The reason is that purwoceng is difficult to cultivate. A controlled factor in cultivation, such as hydroponic types and nutrient concentration, can be used as a solution to this problem. Hydroponic types and nutrient concentration treatments can affect the secondary metabolites and antioxidant activity of the purwoceng root extract produced. This study aimed to determine total phenolic and flavonoid content, as well as antioxidant activity in three different hydroponic systems (nonrecirculating drip, recirculating drip, and nutrient film technique (NFT)) and two nutrient concentrations (1.5‰ and 2.0‰). The combination of recirculating drip with low nutrient concentration was the best treatment to produce an extract with high phenolic and flavonoid content. Purwoceng root extracts from nonrecirculating with high nutrient concentrations produced high antioxidant activity. The characteristics of extracts from recirculating with low nutrient concentrations were similar to those from the nonrecirculating drip. In contrast, extracts from recirculating with high nutrient concentrations were closer to extracts from NFT, proven by principal component and heat map analysis. Antioxidant activity related to total phenolic content, also the presence of betaine and bergapten in purwoceng root extracts. Keywords: Flavonoid content, NFT, Nonrecirculating drip, Phenolic content, Recirculating drip
Perbandingan Algoritma Klasifikasi untuk Mendeteksi Kebutuhan Nitrogen Tanaman Padi Berdasarkan Data Citra Multi-spectral Drone Kahfi Gunardi; Karlisa Priandana; Medria Kusuma Dewi Hardhienata; Wulandari; Mohamad Solahudin
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.238-249

Abstract

Optimalisasi penggunaan pupuk Nitrogen (N) sangat penting untuk meningkatkan produktivitas tanaman padi. Untuk mengetahui jumlah pupuk yang diperlukan oleh tanaman padi, petani umumnya menggunakan Bagan Warna Daun (BWD) dengan cara mencocokkan warna daun padi dengan warna pada BWD secara manual. Namun, hal ini sangat memakan waktu. Salah satu strategi untuk meningkatkan efisiensi penentuan kebutuhan pupuk N adalah dengan menggunakan Multi-spectral Drone. Drone digunakan untuk mengambil citra multispectral, kemudian citra ini digunakan untuk menentukan kebutuhan pupuk N. Penelitian ini membandingkan beberapa algoritma klasifikasi untuk memodelkan kebutuhan pupuk N dari data citra multispectral, dengan menggunakan ground truth dari penskalaan BWD. Algoritma klasifikasi yang dibandingkan yaitu Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), dan K-Nearest Neighbour (KNN). Kinerja kelima algoritma klasifikasi diukur berdasarkan accuracy, recall, precision dan F1 score. Dalam penelitian ini, ditemukan bahwa model klasifikasi yang memiliki kinerja terbaik adalah algoritma Decision Tree (DT) baik dalam perlakuan tanpa normalisasi dan balancing dan dengan normalisasi dan balancing dengan nilai accuracy, recall, precision, dan­­­ F1-score di atas 90%.
Sistem Kendali Fertigasi Presisi Berbasis Logika Fuzzy untuk Budidaya Tanaman Hidroponik Prastono, Haryo; Solahudin, Mohamad; Supriyanto, Supriyanto
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 12 No 2 (2024): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v12i2.639

Abstract

The fertigation control system has been extensively developed, particularly for hydroponic plant cultivation. However, existing studies often overlook the characteristics of the interaction between the environment and plants. Evapotranspiration and the moisture status of the growing medium are two critical parameters in determining the volume of fertigation. As a control system, fuzzy logic can manage fertigation based on these two parameters. This study aims to deliver water and nutrients to plants more effectively and efficiently, aligning with current environmental conditions and plant needs. The research was conducted through several stages: defining design criteria, creating a fuzzy logic design, simulating the fuzzy logic-based fertigation control system, and testing the system. Simulation results indicate that when the moisture content of the growing medium is between 26% and 31%, the fertigation duration varies according to evapotranspiration values. When the moisture content exceeds 32%, fertigation does not occur regardless of evapotranspiration values. Testing results demonstrate that the fuzzy logic used in this control system is efficient in delivering fertigation, as evidenced by minimal runoff compared to systems without control. Additionally, the moisture status of the growing medium consistently remains within the available water zone or at the optimum condition for plant water absorption, with a mean absolute percentage error of 1.98%. The designed fuzzy logic control system is also effective in providing fertigation, with the total volume ranging from 132 to 308.4 ml/day, closely matching the daily evapotranspiration rate. Based on plant physiological measurements, the fuzzy logic control system outperforms fertigation without control.
Evaluation of Nutrient Dosing Methods for Hydroponic Crop Cultivation Prastono, Haryo; Solahudin, Mohamad; Supriyanto
Jurnal Keteknikan Pertanian Vol. 11 No. 3 (2023): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.011.3.279-293

Abstract

The application of control systems for hydroponic cultivation in greenhouses has developed rapidly, and one of them is the nutrient-dosing control system. Various nutrient dosing control systems, such as on/off, fuzzy logic, and K-NN, have been widely discussed by many researchers. Nevertheless, there has been limited discourse regarding the employed dosing methods, namely sensor-based nutrient dosing and nutrient dosing based on the nutritional dilution equation. The objective of this study was to assess the effectiveness and efficiency of three nutrient dosing methods that can be used in control systems: nutrient dosing based on nutrient dilution equations (method α), sensor-based (method β), and a mixed nutrient dosing method combining the two previous methods (method γ). This research begins by identifying several nutrient dosing methods that can be used in control systems. After that, the dosing methods were tested, and data analysis was carried out. Based on the three methods proposed in this study, method γ has the best results with a mean absolute percentage error (MAPE) of 0.77% and a root mean square error (RMSE) of 4.3, respectively, in relation to the targeted concentration of the nutrient solution. Method β has slightly higher results, with a MAPE of 0.82% and an RMSE of 4.62. Method α has the worst results, with a MAPE of 13.42% and a RMSE of 74.98. However, if the desired target nutrient concentration is less than 300 ppm, method β is the most suitable option compared to the other methods.
Low-Cost Monitoring and Control for Melon Cultivation in Greenhouse using Internet of Thing and Drip Irrigation Supriyanto; Fahrezi, Rafli Arya; Prasetyo, Tegar Adi; Septiadi, Ananda Putra; Sucahyo, Lilis; Solahudin, Mohamad
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 13 No 1 (2025): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v13i1.1154

Abstract

Melons are become a popular fruit cultivating inside the greenhouse using the drip irrigations in Indonesia. The application of internet of things-based monitoring is beneficial to optimize cultivation management. Another issue on melon cultivation inside the greenhouse is automation of the water and nutrient delivery. However, currently monitoring and control is expensive and difficult to modify by farmers. The aim of this study was to develop a low-cost technology and easy to use by farmers using internet of technology. The method used in this study consisted of analysis, design and implementation. The result of this study was a system monitoring to monitor air temperature, air humidity, media humidity and solar radiation inside the greenhouse integrated with nutrient or water delivery using drip irrigation. A web-based dashboard was developed as a user interface for the farmers and users. The overall cost to develop a system monitoring and control was 358.24 USD not including the water thank and nutrient delivery system (pump and irrigations pipe). The system was deployed and tested at Agribusiness and technology park IPB University.
Prediction of Phenotypic Parameters of Sugarcane Plants Based on Multispectral Drone Imagery and Machine learning Febri Hasskavendo; Mohamad Solahudin; Supriyanto Supriyanto; Slamet Widodo
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.
Classification of Arabica Coffee Beans Based on Starters Type with Honey Processing Using Multi-channel Spectral Sensor Romadhon, Akbar; Solahudin, Mohamad; Purwanti, Nanik; Widodo, Slamet
Jurnal Keteknikan Pertanian Vol. 13 No. 2 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.2.302-317

Abstract

Coffee is one of important agricultural commodities in Indonesia, 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 flavour. However, the use of various starters in this process produces coffee bean with similar color, making it difficult to determine the authenticity of the type of starter used. This research aims to classify arabica coffee beans processed with different types of starters using multi-channel spectral sensor to ensure product quality and authenticity. This research used arabica coffee beans, in the form of green beans, processed with three types of starters, namely Saccharomyces cerevisiae, Lactobacillus sp, and Rhizopus oryzae. Multi-channel spectral sensor was used to acquire the spectra data of coffee sample processed with different starters. The data was then analysed using multivariate analysis based on Partial Least Square – Discriminant Analysis (PLS-DA). In the calibration stage, PLS-DA model built using de-trending pre-treatment was able to predict the type of starter very well, with accuracy, sensitivity, specificity, and precision values, reaching 97%, 95%, 96%, 95%, respectively. This result is also confirmed during validation stage where the built PLS-DA model could predict the type of starter with accuracy, sensitivity, specificity, and precision values, reaching 100%.
Deep Learning-Based Detection for Early Germination Stages of Chili Pepper (Capsicum annuum L) Seedling in Greenhouse Tasmara, Jasmine; Supriyanto, Supriyanto; Solahudin, Mohamad
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.
Pengembangan Sistem E-Learning Inklusif untuk Pesantren dan Pendidikan Nonformal: Desain Teknologi Pembelajaran Berbasis Keunikan Lembaga Islam Tradisional Ihsan, Mahlil Nurul; Solahudin, Mohamad Nizan; Riyanti, Riyanti; Dewi, Kania; Putri, Sindi Lestari
ALACRITY : Journal of Education Volume 5 Nomor 2 Juni 2025
Publisher : LPPPI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52121/alacrity.v5i2.855

Abstract

Tujuan dari penelitian ini adalah untuk mengembangkan sistem e-learning inklusif yang sesuai dengan keunikan pesantren dan lembaga pendidikan nonformal Islam, guna mendukung pendidikan Islam yang lebih efektif dan adaptif di era digital. Penelitian ini menggunakan metode library research, yang mengkaji berbagai literatur dan studi kasus mengenai penerapan e-learning di lembaga pendidikan Islam tradisional, termasuk pesantren. Hasil penelitian menunjukkan bahwa sistem e-learning yang dirancang khusus untuk pesantren harus mempertimbangkan karakteristik unik pesantren, seperti interaksi personal dan pembelajaran berbasis pesantren. Teknologi pembelajaran berbasis digital, seperti platform pembelajaran online yang dapat diakses secara fleksibel, memiliki potensi besar untuk meningkatkan kualitas pendidikan di pesantren dan lembaga pendidikan nonformal Islam. Implikasi dari penelitian ini adalah pentingnya pengembangan sistem e-learning yang inklusif, ramah pengguna, dan sesuai dengan kebutuhan budaya serta tujuan pendidikan Islam tradisional.
Rapid Analysis of ICUMSA Value of Cane Sugar Using Multi-Channel Spectra Sensor Based-Portable Device Khairani, Fadhilah; Khairani, Fadilah; Solahudin, Mohamad; Widodo, Slamet
Jurnal Keteknikan Pertanian Vol. 12 No. 3 (2024): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.012.3.409-423

Abstract

One of important quality parameters of white crystal cane sugar is its color, which is measured as the ICUMSA value referring to the standard method established by the International Commission for Uniform Methods of Sugar Analysis (ICUMSA). It is usually measured in a laboratory using a complex and lengthy chemical analysis method. To overcome this challenge, this research attempts to explore the potential use of multi-channel spectral sensors in the UV-Vis-NIR region as an alternative method to predict the ICUMSA value. The proposed portable device uses an AS7265X sensor as the main component. The spectra data of 60 cane sugar samples were collected using the proposed device followed by measurements of ICUMSA value in the laboratory using standard methods as reference. The prediction using partial least squares regression (PLSR) model achieved R2 = 0.896, RMSEC = 0.072%, RMSEP = 0.103%, CV = 26.087%, and RPD = 3.104. The multiple linear regression (MLR) model achieved R2 = 0.910, RMSEC = 0.067%, RMSEP = 0.111%, CV = 24.328%, and RPD = 3.328. The artificial neural network (ANN) model achieved R2 = 0.999, RMSEC = 0.004%, RMSEP = 0.037%, CV = 1.433% and RPD = 9.543. This result indicates that the developed PLSR, MLR, and ANN models can predict the ICUMSA value well with ANN as the best model. It also can be concluded that the proposed portable device can be an alternative for rapid analysis of ICUMSA value.
Co-Authors Agus Buono Ahmar, Afdhalul Alvin Fatikhunnada Alvin Fatikhunnada Eni Sumarni Eni Sumarni Erniati Erniati Erniati Eti Rohaeti Eti Rohaeti Fadhilah Khairani, Fadhilah Fahrezi, Rafli Arya Febri Hasskavendo Fenry Winna Mutawally Firmansyah, Angga Folkes Eduard Laumal Folkes Laumal Folkes Laumal folkes laumal, folkes Fuad Heru Setiawan Giska Priaji Gumilang Agus Gozali Hasskavendo, Febri Herry Suhardiyanto Heru Sukoco I Wayan Astika I Wayan Budiastra Ihsan, Mahlil Nurul Irmanida Batubara Jayawarsa, A.A. Ketut Kahfi Gunardi Kania Dewi, Kania Karlisa Priandana Khairani, Fadilah Khoirul Umam Kiswanto S. Heri Kudang Boro Seminar Lilis Sucahyo Liyantono . Medria Kusuma Dewi Hardhienata Michael Alexander Hutabarat Mohamad Iqbal Suriansyah Mohamad Yanuar Jawardi Purwanto Muhammad Naufal Rauf Ibrahim NANIK PURWANTI Nelwan, Leopold Oscar Nurbaiti Araswati Omil Charmyn Chatib Prasetyo, Tegar Adi Prastono, Haryo Purwansya, Yuvicko Gerhaen Putri, Sindi Lestari Rena Nurista Riyanti Riyanti Rokhani Hasbullah Romadhon, Akbar 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 Tasmara, Jasmine Tineke Mandang WULANDARI Y. Aris Purwanto Yanti, Delvi Yudi Chadirin Yudiwanti Yudiwanti Wahyu E. Kusumo Yuvicko Gerhaen Purwansya