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Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target Iis Rodiah; Medria Kusuma Dewi Hardhienata; Agus Buono; Karlisa Priandana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4201

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.

Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States Hidayat; Agus Buono; Karlisa Priandana; Sri Wahjuni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4949

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.

Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points Try Kusuma Wardana; Yandra Arkeman; Karlisa Priandana; Farohaji Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4959

Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m.

Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.

Analysis and review of the possibility of using the generative model as a compression technique in DNA data storage: review and future research agenda Muhammad Rafi Muttaqin; Yeni Herdiyeni; Agus Buono; Karlisa Priandana; Iskandar Zulkarnaen Siregar
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1063

The amount of data in this world is getting higher, and overwriting technology also has severe challenges. Data growth is expected to grow to 175 ZB by 2025. Data storage technology in DNA is an alternative technology with potential in information storage, mainly digital data. One of the stages of storing information on DNA is synthesis. This synthesis process costs very high, so it is necessary to integrate compression techniques for digital data to minimize the costs incurred. One of the models used in compression techniques is the generative model. This paper aims to see if compression using this generative model allows it to be integrated into data storage methods on DNA. To this end, we have conducted a Systematic Literature Review using the PRISMA method in selecting papers. We took the source of the papers from four leading databases and other additional databases. Out of 2440 papers, we finally decided on 34 primary papers for detailed analysis. This systematic literature review (SLR) presents and categorizes based on research questions, namely discussing machine learning methods applied in DNA storage, identifying compression techniques for DNA storage, knowing the role of deep learning in the compression process for DNA storage, knowing how generative models are associated with deep learning, knowing how generative models are applied in the compression process, and knowing latent space can be formed. The study highlights open problems that need to be solved and provides an identified research direction.

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model Hidayat, Hidayat; Buono, Agus; Priandana, Karlisa; Wahjuni, Sri
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.18777

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning.

Development of Education Kit Prototype Based on Internet of Things (IoT) Arsy Novita Syahada; Doli Aryo Dani Sinaga; Karlisa Priandana
Cylinder : Jurnal Ilmiah Teknik Mesin Vol. 7 No. 1 (2021): APRIL 2021
Publisher : Department of Mechanical Engineering Atma Jaya Catholic University of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

In answering the challenges of industrial revolution 4.0, robotics and the Internet of Things (IoT) should be used as one of the learning curricula at all educational stages, especially at the high school level. Furthermore, robotics and IoT education are now becoming an important aspect to implement and learn about Science, Technology, Engineering, and Mathematics (STEM) education. To support robotics and IoT education, an easy and user-friendly education kit as a learning medium is required. This work aims at developing a prototype of a robotics and IoT education kit that is suitable for senior high school students. The education kit is designed to be interesting and challenging enough to increase the student's interest in learning robotics and IoT. The kit is developed by using various independent and unattached sensors so that the users, i.e., high-school students can choose their sensors and can investigate the microcontroller pins that are used for these components. In addition, users can also learn to connect an electronic component with other electronic components on the kit to be able to produce various logical embedded systems that can be connected to the internet.

Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.

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

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%.

Perbandingan Algoritma K-Means dan Fuzzy C-Means untuk Clustering Citra Daun Melon Siregar, Ardinsyah; Buono, Agus; Priandana, Karlisa
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2534

Melon plants are plants that are susceptible to disease, both diseases caused by viruses and those caused by bacteria. One part of the plant that can be affected by the disease is the leaves. Leaves on diseased plants generally change color which will then affect other leaves and inhibit the development and growth of these plants. This study aims to classify melon plant diseases from melon leaf images. The data used in this study are 160 images of melon leaves which will be grouped into several groups from the healthy group to the unhealthy group. The method used is the Clustering method, namely: K-Means algorithm and Fuzzy C-Means algorithm. Clustering results using K-Means and Fuzzy C-Means can be compared to get the best clustering results. The comparison results show the Fuzzy C-Means Clustering method with a validation value of 0.8359 and the K-Means Clustering method with a validation value of 0.5793. The final result shows that the Fuzzy C-Means Clustering method is better than the K-Means Clustering method because the validation value is close to 1.

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