Lab-IS/filkom-research-papers huggingface dataset:
Hybrid particle swarm optimization and simulated annealing for capacitated vehicle routing problem
Authors: Farhanna Mar'i, Wayan Firdaus Mahmudy, Purnomo Budi Santoso
Year: 2019
Topics: Optimization; Operations Research
Abstract: Distribution is one of the supply management activity that requires special attention in order to delivering the company's products appropriately to consumers with a optimal route to minimalize cost and increase profit of the company. To minimalize distribution costs, companies must plan the right route pattern so that the distance traveled by the vehicle in the distribution process is minimal. In this study, we will use meta-heuristic method to find optimal solution for finding optimal solution to solving one of vehicle routing problem with capacity constrain, namely Capacitated Vehicle Routing Problem (CVRP) which are one of complex combinatorial optimization problems. This study proposes Hybridization of Particle Swarm Optimization and Simulated Annealing to find route pattern in order to get best minimal distance. Hybridization is carried out because PSO and SA had and advantages and weakness in exploration …
Challenges and opportunities for applying meta-heuristic methods in vehicle routing problems: A review
Authors: Wayan Firdaus Mahmudy, Agus Wahyu Widodo, Alfabiet Husien Haikal
Year: 2024
Topics: Optimization; Operations Research
Abstract: The Vehicle Routing Problem (VRP) is related to determining the route of several vehicles to distribute goods to customers efficiently and minimize transportation costs or optimize other objective functions. VRP variations will continue to emerge as manufacturing industry production distribution problems become increasingly complex. Meta-heuristic methods have emerged as a powerful solution to overcome the complexity of VRP. This article provides a comprehensive review of the use of meta-heuristic methods in solving VRP and the challenges faced. A review of popular meta-heuristic methods is presented, including Simulated Annealing, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. The advantages of each method in solving the VRP and its role in solving complex distribution problems are discussed in detail. Challenges that may be encountered in using meta-heuristics for VRPs are analyzed, along with strategies to overcome these challenges. This article also recommends further research that includes adaptation to more complex VRP variants, incorporation of meta-heuristic methods, parameter optimization, and practical implementation in real-world scenarios. Overall, this review explains the important role of meta-heuristic methods as intelligent solutions to increasingly complex distribution and logistics challenges.
Extreme learning machine weight optimization using particle swarm optimization to identify sugar cane disease
Authors: Mukhammad Wildan Alauddin, Wayan Firdaus Mahmudy, Abdul Latief Abadi
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Machine Learning; Optimization
Abstract: Sugar cane disease is a major factor in reducing sugar cane yields. The low intensity of experts to go into the field to check the condition of sugar cane causes the handling of sugarcane disease tends to be slow. This problem can be solved by instilling expert intelligence on sugar cane into an expert system. In this study the method of classification of sugar cane disease was proposed using Extreme Learning Machine (ELM). However, ELM alone is not enough to classify multilabel and multiclass disease case data in this study. Therefore, it is proposed to optimize the weight of hidden neurons in ELM using Particle Swarm Optimization (PSO). The experimental results show that the classification using ELM alone can reach an accuracy rate of 71%. After the weight of hidden neurons from ELM was optimized, the accuracy rate became 79.92% or an increase of 8.92%.
Sudarto,“Land Clustering for Potato Plants Using Hybrid Particle Swarm Optimization and K-Means Improved by Random Injectio,”
Authors: YA Auliya, WF Mahmudy
Journal: J. Inf. Technol. Comput. Sci
Year: 2019
Topics: Machine Learning; Optimization
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting.
Authors: Gayatri Dwi Santika, Wayan Firdaus Mahmudy, Agus Naba
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Machine Learning; Optimization
Abstract: The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other wellknown method in the literature.
A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images.
Authors: Arif Muntasa, Rima Tri Wahyuningrum, Zabrina Tuzzahra, Abdelwahed Motwakel, Muhammad Yusuf, Wayan Firdaus Mahmudi
Journal: International Journal of Intelligent Engineering & Systems
Year: 2022
Topics: Deep Learning; Healthcare
Abstract: Many researchers have classified acute lymphoblastic leukemia using several methods. One of the methods is a convolutional neural network. However, the limitation of the convolutional neural network is a large number of trainable parameters updated. The paper proposes a new architecture based on the convolutional neural network. We have designed and implemented a convolutional neural network with different kernels, where we increase the number of kernels like pyramid models. We utilized the final convolution to conduct the fully connected, followed by the SoftMax function to classify the image. We have evaluated our proposed architecture using acute lymphoblastic leukemia image database (ALL-IDB2). The results show that our proposed method produced the average Accuracy, Precision, and Recall of 99.17%, 99.33%, and 99%, respectively. It has outperformed other models, ie, shape features, Multi distance of GLCM, CNN and SVM, Pretrained deep convolutional neural networks, AlexNet, ensemble network, convolutional and recurrent neural network, and hypercomplex-valued convolutional neural networks.
Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19.
Authors: Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti
Journal: Knowl. Eng. Data Sci.
Year: 2021
Topics: Deep Learning; Agriculture
Abstract: Kenaf plant fiber is one of the basic ingredients for environmental management products that have become the center of global attention. This fiber is intended to replace the consumption of plastic materials used in car interiors at popular automotive companies [1]. This causes the export value of raw materials to increase because they have to meet production needs. A problem was encountered in one of Kenaf's plantations, where some plants died, and the harvest failed. The cause of this crop failure was the kenaf plant which was attacked by diseases and pests. Farmers do not know the plants affected by this disease because of a lack of counseling from experts, which can be fatal. With the development of information technology systems, this can be overcome by detecting symptoms of disease/pests in kenaf plants from an early age to avoid the consequences of crop failure. One of the methods offered is to detect symptoms of disease/pests in kenaf plants through pictures. A machine carries out image recognition then the machine can make decisions based on the trained image. This machine-driven decision-making system is called machine learning [2]. One application of the machine learning method has been carried out by Saragih in the decision-making system for the classification of jatropha plant disease with an accuracy of 60.61%[3]. This result is categorized as quite good because it can achieve accuracy above 50%. The data used in this study [3] is a scoring of disease types in jatropha, which is given by the expert directly. The more data that is trained, the more complex the machine learning system will be as the data described in one …
Mangrove forest classification in drone images using HSV color moment and Haralick features extraction with k-nearest neighbor
Authors: Agus Wahyu Widodo, Deo Hernando, Wayan Firdaus Mahmudy
Journal: Signal and Image Processing Letters
Year: 2019
Topics: Machine Learning; Remote Sensing
Abstract: Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k= 3, with an accuracy of 84%.
FC-SVM: DNA binding Proteins prediction with Average Blocks (AB) descriptors using SVM with FC feature Selection
Authors: Achmad Ridok, Nashi Widodo, Wayan Firdaus Mahmudy, Muhaimin Rifai
Year: 2019
Topics: Machine Learning; Bioinformatics
Abstract: DNA binding proteins have important functions and roles in various biological processes, namely regulation of transcription, DNA replication, DNA packaging, DNA repairs, and DNA rearrangement. More than 135,000 atomic-level biomolecular structures from experimental results have been stored in the Protein Data Bank (PDB) database. Therefore, we need computational methods that can predict quickly and accurately the existence of DNA binding proteins. This research proposes a new method FC-SVM that combine Support Vector Machine (SVM) with F-score (FC) feature selection method to identify DNA binding protein using average block (AB) descriptor that was extracted from position specific scoring matrix (PSSM). Evaluation of the proposed method with 10 cross validations in three datasets of PDB186, PDB594 and PDB1075 shows the results of the performance 0.66, 0.72 and 0.75 resoectively.
Prediction of forest fire using neural network based on extreme learning machines (ELM)
Authors: Mochammad Anshori, Farhanna Mar'i, Mukhammad Wildan Alauddin, Wayan Firdaus Mahmudy
Year: 2019
Topics: Neural Networks; Environmental Science
Abstract: To prevent forest fires, predictions need to be made to find out areas of land that have the potential to burn based on meteorological conditions obtained from the sensor, so that it is expected to reduce the spread of fire before the fire spreads. Meteorological conditions used in this study to predict areas of land that will be affected by forest fires are temperature, wind, humidity, and rainfall. The method used in this study is a neural network with Extreme Learning Machines (ELM) training model. To improve the performance of the ELM method, in this study several tests will be carried out so that the resulting predictions are the best.
The Evaluation of Technology Startup Role on Indonesian SMEs Industry 4.0 Adoption Using CLD-ABM Integrated Model
Authors: Ishardita Pambudi Tama, Willy Satrio Nugroho, Wayan Firdaus Mahmudy, Purnami Purnami
Journal: Sustainability
Year: 2022
Topics: Technology Adoption; Business Modeling
Abstract: The role of the Association of Southeast Asia Nations (ASEAN) small and medium enterprises (SMEs) as the regional socioeconomic stabilizer is inseparable from endogenous multisector collaboration. Indonesian SMEs struggled with Industry 4.0 adoption due to the lower digital infrastructure (DI) and digital literacy (DL) index. This study aims to develop a provisional model to explore the role of technology startups (TS) in covering the DI and DL of SMEs. The evaluation was through a simulation model of Indonesian SMEs’ collaboration with the government and TS. The developed model is a concurrent real-time integration of an agent-based model (ABM) with a causal loop diagram (CLD). The simulation results imply that SMEs’ collaboration with TS leads to the early adoption of Industry 4.0 that balances the business competition environment. The model also shows that exponentially rising government aid can help the SMEs into late adoption of Industry 4.0. However, SMEs are still unable to sustain the business competition. Thus, the integrative simulation model is a state-action planning model with each state result bounded to the previous state result. The initial input parameters determine the model behavior. Thus, the model is a good resiliency planner for SMEs’ Industry 4.0 adoption.
Inflation Rate Forecasting Using Extreme Learning Machine and Improved Particle Swarm Optimization.
Authors: Wayan Firdaus Mahmudy, Adyan Nur Alfiyatin, Candra Fajri Ananda, Agus Wahyu Widodo
Journal: International Journal of Intelligent Engineering & Systems
Year: 2021
Topics: Machine Learning; Economics
Abstract: Inflation is an important tool to assess the current condition of a nation economy. Uncontrolled inflation rate may have a lot of negative impacts on economic development. Forecasting can be used to control the inflation by making appropriate economic policies. However, uncertainty pattern of the inflation rate may make it hard to forecast. This study proposes an extreme learning machine (ELM) for the inflation rate forecasting. As part of the machine learning algorithm, the ELM has an ability to address uncertainty in data input pattern. However, ELM had weakness in determining initial weights that may produce inaccurate results. So, we propose particle swarm optimization (PSO) to determine good initial weights for the ELM. PSO is a metaheuristic method that gives good results in local searches but requires longer computation time to locate its particles on the global optimum point in the vast search space area. To overcome this problem, auto-speed acceleration algorithm is employed to drive particles of the PSO in searching of the global optimum with lower computation time. The performance of the proposed approach is evaluated using root mean square error (RMSE). A series of computational experiments prove that the proposed approach achieves better results with the average RMSE of 0.01926. This result is better than RMSE of 0.02020 achieved by the original version of ELM.
Detection of online review spam: a literature review
Authors: Raga SH Istanto, Wayan Firdaus Mahmudy, Fitra A Bachtiar
Year: 2020
Topics: Natural Language Processing; Data Mining
Abstract: Online reviews have become an important resource for customers. It has become a habit for customers to first read a review before deciding to make a purchase. But it can be used by fraudsters to make review spam. This activity can result in the wrong customer purchase decision. Automatic opinion mining methods can also provide inaccurate conclusions due to this activity. This paper aims to provide a literature review on the online review spam detection topic. We identify papers relevant to related topics since 2015, understanding each paper to extract findings, similarities, and research gaps. We find that studies on this topic can be categorized into three focus groups. Focus on review spam detection methods, studies on individuals who write review spam, and studies that examine the spammer groups. Each focus of research has its strengths and weaknesses method which provide benefits in the field of review …
Kenaf plant pest and disease detection using faster regional based convolutional neural network
Authors: Alfita Rakhmandasari, Wayan Firdaus Mahmudy, Titiek Yulianti
Journal: Indonesian Journal of Electrical Engineering and Computer Science
Year: 2021
Topics: Computer Vision; Agriculture
Abstract: Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligencebased method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection.
Review of non-destructive banana ripeness identification using imagery data
Authors: Candra Dewi, Wayan Firdaus Mahmudy, Endang Arisoesilaningsih, Solimun Solimun
Year: 2021
Topics: Computer Vision; Agriculture
Abstract: Bananas have different nutritional content at each level of ripeness. Therefore, choosing a banana with the right level of ripeness is necessary in order to obtain fruit according to our needs. This paper conducted a review of the non-destructive identification of banana ripeness using banana fruit imagery and computer vision method. Through the review literature, this paper is presented in a concise and detailed manner related to the type of image used and the stages of the identification process starting from feature extraction, reduction of dimension and feature selection, and classification. The result of the review showed a considerable potential for the use of digital imagery and computer vision method in the automatically identifying of banana ripeness.
Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network
Authors: Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti
Journal: JITeCS (Journal of Information Technology and Computer Science)
Year: 2021
Topics: Computer Vision; Agriculture
Abstract: Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.
Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java
Authors: Andi Hamdianah, Wayan Firdaus Mahmudy, Eko Widaryanto
Journal: Journal of Information Technology and Computer Science
Year: 2020
Topics: Neural Networks; Agriculture
Abstract: Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10, 8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10, 3804.
Improve performance of extreme learning machine in classification of patchouli varieties with imbalanced class
Authors: Candra Dewi, Wayan Firdaus Mahmudy, Rio Arifando, Yoke Kusuma Arbawa, Beryl Labique Ahmadie
Year: 2020
Topics: Machine Learning; Agriculture
Abstract: Patchouli has various varieties with almost the same physical characteristics. This often makes it difficult to recognize varieties with a high PA (Patchouli Alcohol) content. In this study an improvisation was introduced in the identification of patchouli varieties using leaf images using Extreme Learning Machine (ELM). However, problems occur in ELM if the data used is not balanced where the training process can not able to recognize data in the minority class well. Therefore, this study conducted a process to balance the composition of the data using the Synthetic Minority Over-sampling Technique (SMOTE) method. The test results of 93 data on the imbalanced composition with a comparison of 70% of training data and 30% of test data obtained an average accuracy of 93.57%. After implementing SMOTE in the Tetraploid, Patchoulina and Sidikalang classes where the amount of data in each class becomes 58 data …
Relay nodes placement for optimal coverage, connectivity, and communication of wireless sensor networks: A PSO-based multi-objective optimization research idea
Authors: Kasyful Amron, Wuryansari M Kusumawinahyu, Syaiful Anam, Wayan F Mahmudy
Year: 2020
Topics: Wireless Networks; Optimization
Abstract: Designing a Wireless Sensor Networks (WSN) mostly was a great challenge. Shown in previous results, some design approaches lead to problems in its implementation. Deterministic methods face the NP-Hard complex problem. On the other side, heuristic methods sometimes produce a flawed result. With those situations, this research concern with exploring the possibility of a multi-objective optimization (MOO) method. As with the MOO method, some conflicted WSN aspects consider simultaneously. Started with the PSO algorithm, this developing method tries to find the best position of the WSN's relays. Closed neighbor sensor nodes are then will be connected. It is combined with the graph to constructs the best communication link. These steps will be done in a certain number of iterations to enhance fault-tolerance ability. This MOO approached method was implemented to different WSN topologies, with several …
A Support Vector Machine Based on Kernel K-Means for Detecting the Liver Cancer Disease.
Authors: Lailil Muflikhah, Widodo Widodo, Wayan Firdaus Mahmudy, Solimun Solimun
Journal: International Journal of Intelligent Engineering & Systems
Year: 2020
Topics: Machine Learning; Healthcare
Abstract: Liver cancer can be caused by hepatitis B virus (HBV) infection. This genome virus inserts genetic material into its host. Moreover, each gene has numerous HBV DNA sequences due to its high mutation rate in replication. Thus, detecting virus is a difficult task. A support vector machine (SVM) is a robust machine-learning algorithm for detecting liver cancer disease. However, a high data volume can reduce its computation speed and performance measures. Therefore, we propose data simplification using Kernel k-means clustering method to construct the SVM classifier model by minimizing objective function as object distance. Based on experimental results, the proposed method’s performance evaluation was higher than SVM algorithm without kernel k-means, especially for the sensitivity significantly increased. The accuracy rate and AUC of the proposed method were around 98% and 0.95. Furthermore, the performance of proposed method is also predominant of the other machine learning: Random Forest, Naïve Bayes, Naural Network and C5. 0.
Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage
Authors: Nindynar Rikatsih, Wayan Firdaus Mahmudy, Syafrial Syafrial
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Optimization; Genetic Algorithms
Abstract: Agricultural product storage has a problem that need to be noticedbecause it has an impact in gaining the profit according to the number ofproducts and the capacity of storage. Inappropriate combination of productcauses high expenses and low profit. To solve the problem, we propose geneticalgorithm (GA) as the optimization method. Although GA is good enough tosolve the problem, GA not always gives an optimum result in complex searchspaces because it is easy to be trapped in local optimum. Therefore, we presenta hybrid real-coded genetic algorithm and Variable Neighborhood Search(HRCGA-VNS) to solve the problem. VNS is applied after reproductionprocess of GA to repair the offspring and improve GA exploitation capabilitiesin local area to get better result. The test results show that the optimal popsizeof GA is 180, number of generations is 80, combination of cr and mr is 0.7 and0.3 while optimum Kmax of VNS is 40 with number of iterations 50. Eventhough HRCGA-VNS need longer computational time, HRCGA-VNS hasproven to provide a better result based on higher fitness value compared withclassical GA and VNS.
Utilization of Current Data for Geospatial Analysis of the Appropriateness of Apple Plantation Land Based on Fuzzy Inference Systems
Authors: Prayudi Lestantyo, Fatwa Ramdani, Wayan Firdaus Mahmudy
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Geospatial Analysis; Agriculture
Abstract: Apple is a high-value import fruit in Indonesia. One of the Apple production centers in Indonesia is Batu City, but the results tend to be declining in every year. To fulfill the demand of domestic apple industry, it is than a must to open new plantation land by observing the spatial factor. Expert and direct field review are needed to perform the analysis of land suitability, so that it will takes a lot of time and effort. Therefore, a smart system that can conduct geospatial analysis by using fuzzy inference system is developed. The data was obtained by using satellite imagery, data interpolation, and digitized and then analyzed into information. The analysis was performed on each pixel with six variable inputs including altitude, rainfall, humidity, air temperature, soil type and sun shine intensity. Besides that, the five-clustering output makes the results more accurate. From the results of the accuracy test, it is obtained a 92,86% accuracy, by comparing the results of the spatial analysis using fuzzy inference system with direct review on the field.
Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System
Authors: Rafiuddin Rody, Wayan Firdaus Mahmudy, Ishardita Pambudi Tama
Journal: J. Inf. Technol. Comput. Sci
Year: 2019
Topics: Genetic Algorithms; Operations Research
Abstract: Production and distribution system in a company should be managed carefully. Delay in product delivery not only results in a late penalty due to customer dissatisfaction or breach of contract, but also causes a supply chain failure. Of course, all these impacts will also reduce the reputation of a company. Scheduling integrated production-distribution is classified as NP-Hard problem. Genetic algorithm can be used to solve complex problem. In this paper, genetic algorithm is used for scheduling production-distribution in make to order system where each job has a different deadline and volume (size). This problem is represented on mixed integer programing model. We verify the genetic algorithm’s performance by comparing the results with the total cost calculated by lower bounds of the problems. Experiments show that the traditional initial random cannot produce good result with more than 15 job size problem. We proposed guided initial chromosome to tackle this problem. From further experiments shows that the proposed method approach can increase the performances of genetic algorithm in more than 15 job size problem. In general, proposed genetic algorithm with guided initial chromosome shows better solution quality and better time efficiency compared to previous related research.
Anomaly-based intrusion detector system using restricted growing self organizing map
Authors: Tomi Yahya Christyawan, Ahmad Afif Supianto, Wayan Firdaus Mahmudy
Journal: Indonesian Journal of Electrical Engineering and Computer Science
Year: 2019
Topics: Machine Learning; Self Organizing Maps
Abstract: The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference vector (RGSOM-CRV) and Parallel RGSOM-CRV to improve SOM efficiency in classification with accuracy consideration to solve Big Data problem. Growing process in RGSOM is restricted by maximum nodes and growing threshold, the reupdate weight process will update unused reference vector when map size already maximum, these two processes solve the consuming time of regular GSOM. From the results of this research against KDD Cup 1999 dataset, proposed method Parallel RGSOM-CRV able to give 91.86% accuracy, 20.58% false alarm rate, 95.32% recall or detection rate, and precision is 94.35% and time consuming is outperform than regular Growing SOM. This proposed method is very promising to handle big data problems compared with other methods.
Preprocessing Approach for Tuberculosis DNA Classification using Support Vector Machines (SVM)
Authors: Mochammad Anshori, Wayan Firdaus Mahmudy, Ahmad Afif Supianto
Journal: JITeCs (Journal of Information Technology and Computer Science)
Year: 2019
Topics: Machine Learning; Support Vector Machines
Abstract: Tuberculosis is a disease that caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is one of the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to the other lung disease. The challenge is how to get the best detection system for classification of Tuberculosis using Deoxyribo Nucleic Acid (DNA) sequence data from mycobacterium tuberculosis. One way to do the detection is using machine learning algorithm which is Support Vector Machines (SVM). Before making a detection for Tuberculosis DNA Classification, it is necessarry to preprocess the DNA datasets first. Preprocessing method that we used in this research are using k-Mer for feature extraction, then processed with TF-IDF to transform it into numerical value and uniform the data length. Not Only that, because the DNA datasets is very large so dimension reduction is really needed and we used Linear Discriminant Analysis (LDA). Classification of Tubercolosis DNA will be done using Support Vector Machine (SVM) method with the best preprocesing method. So, in this research to get the best detection for DNA classification will be tested several experiment of parameters value from the method that we used in this research. The overall result based on the experiment of this research, the best k of k-Mer value is 5 that produce accuracy, precision, recall, F Score, and MCC are 0.927, 0.927, 0.920, 0.875.
Advancements in Fire Alarm Detection using Computer Vision and Machine Learning: A Literature Review
Authors: M Fadli Ridhani, Wayan Firdaus Mahmudy
Year: 2023
Topics: Computer Vision; Machine Learning
Abstract: Fire is one of the most common and increasing emergencies that threaten public safety and social development. This can cause significant loss of life and damage. Fire detection systems play an important role in the early detection of fires. The purpose of this study is to provide a brief survey of the latest literature in the field, which can provide a foundation for researchers to develop a Fire Alarm Detection System with a Computer Vision and Machine Learning approach. The Computer Vision and Machine Learning approaches are popular and have been extensively studied because the advantages. The main challenges in fire detection systems are high false alarm rates and slow response times. This research presents potentials and emerging trends through Computer Vision and Machine Learning approaches for Fire Alarm Detection Systems in the future, including the selection of input features to the use of appropriate methods and the process flow of Fire Alarm Detection Systems.
The effect of feature selection on gray level co-occurrence matrix (GLCM) for the four breast cancer classifications
Authors: Marrisaeka Mawarni, Fitri Utaminingrum, Wayan Firdaus Mahmudy
Journal: Journal of Biomimetics, Biomaterials and Biomedical Engineering
Year: 2022
Topics: Machine Learning; Feature Selection
Abstract: Breast cancer is ranked first as the most common cancer case affecting women in the world. Early detection of breast cancer can increase the chances of survival in patients. The role of the radiologist is necessary for the detection of breast cancer, and the radiologists often have limitations in conducting disease consultations with so many patients. The detection gives a subjective result because the process is based on the decision-making of the radiologists. In this work, we proposed a system to detect and classify breast cancer accurately to anticipate delays in patient handling and subjective result. We proposed a digital image processing method using mammograms to classify breast cancer into four categories based on tissue density, namely BI-RADS I, II, III, and IV. The main stages carried out in this research are images processing, feature extraction, data normalization, feature selection, classification, and …
Genetic algorithmised neuro fuzzy system for forecasting the online journal visitors
Authors: Wayan Firdaus Mahmudy, Aji Prasetya Wibawa, Nadia Roosmalita Sari, PURNAWANSYAH Haviluddin, P Purnawansyah
Journal: Int. J. Comput
Year: 2021
Topics: Fuzzy Systems; Genetic Algorithms
Abstract: ABSTRACT Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.
Comparison of bagging ensemble combination rules for imbalanced text sentiment analysis
Authors: Reiza Adi Cahya, Fitra A Bachtiar, Wayan Firdaus Mahmudy
Journal: Journal Homepage: www. jitecs. ub. ac. id
Year: 2021
Topics: Machine Learning; Ensemble Learning
Abstract: The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, eg the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial Naïve Bayes, Complement Naïve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.
Combination of morphology, wavelet and convex Hull features in classification of patchouli varieties with imbalance data using artificial neural network
Authors: Wayan Firdaus Mahmudy, Candra Dewi, Rio Arifando, Beryl Labique Ahmadie, Muh Arif Rahman
Journal: Journal of applied research and technology
Year: 2021
Topics: Artificial Neural Networks; Feature Extraction
Abstract: Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological, texture, and shape features. The texture features are obtained using Wavelet transformation and the shape features are obtained using convex hull. The feature extraction results are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class of artificial neural network algorithms:(1) feedforward neural networks with backpropagation algorithm for training,(2) learning vector quantization (LVQ), and (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is employed to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%. The accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03%) or both (67.25%). This study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.
An efficient adaptive neuro fuzzy inference system for product demand forecasting
Authors: Agus Wahyu Widodo, Gusti Eka Yuliastuti, Agung Mustika Rizki, Wayan Firdaus Mahmudy
Year: 2020
Topics: Fuzzy Systems; Machine Learning
Abstract: A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.
Hybrid Genetic Algorithm & Learning Vector Quantization for Classification of Social Assistance Recipients
Authors: Rio Arifando, Fendy Yulianto, Wayan Firdaus Mahmudy, Bobby Apryanto Sander
Year: 2019
Topics: Genetic Algorithms; Machine Learning
Abstract: The social assistance program called “Rastra” is a government program that aims to ease the burden on poor families by providing food. However, the distribution of the food to prospective beneficiaries is still not accurate. So, a classification method is needed that can help to estimate the right target. In this study, the classification of social assistance recipients by using the Learning Vector Quantization (LVQ) method. LVQ weight vector is very important in the classification process because it affects the classification results. This study applies the Genetic Algorithm to optimize the LVQ weight vector to improve accuracy. The results obtained from this study indicate an LVQ accuracy of 84.16% and GA-LVQ gives a higher accuracy of 87.08%. Produces the best parameters: population size (popSize) 100, crossover rate (cr) 0.5, mutation rate (mr) 0.5, max generation 80, learning rate (a) 0.1 and reduce learning rate (dec …
Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization.
Authors: Tirana Noor Fatyanosa, Wayan Firdaus Mahmudy
Journal: International Journal on Electrical Engineering & Informatics
Year: 2019
Topics: Genetic Algorithms; Linear Programming
Abstract: The biggest obstacle faced by cattleman in cattle fattening is the high feed cost. Cattleman has to formulate cattle feed that meets the nutrient requirements of their cattle and minimize the feed costs. These problems belong to a class of constrained optimization. Various heuristic and deterministic algorithms have been used for solving the constrained optimization problems and applied to the feedstuffs composition. However, there is still some instability in finding mainly a reliable technique that can steadily discover solutions, which are truly near the global optimum of the problem. This paper proposes a combined Modified Evolution Strategies (MES) and Linear Programming (LP) method, named MESLP for optimizing the beef cattle feed. From this study, this novel method obtained a better result than either LP, Random Search, Genetic Algorithm, or Evolution Strategies with higher fitness and lower price.
Fish Image Classification Using Adaptive Learning Rate In Transfer Learning Method.
Authors: Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi
Journal: Knowl. Eng. Data Sci.
Year: 2022
Topics: Transfer Learning; Computer Vision
Abstract: The existence of fish species diversity in coastal ecosystems which include mangrove forests, seagrass beds and coral reefs is one of the benchmarks in determining health in coastal ecosystems. It is certain that we must maintain, preserve and care for so that conservation efforts need to be carried out in water areas. Many experts at the Indonesian Fisheries and Marine Research and Development Agency often classify fish images manually, of course it will take a long time, therefore with today's developments they can use the latest technology. One of the reliable techniques in terms of image classification is Convolutional Neural Network (CNN). As time goes by, of course, many people want fast learning and solving new problems faster and better, so transfer learning appears, which adopts part of CNN, the name is modified convolution layer. Observing the needs of experts in the field of marine conservation, the …
Hybrid Artificial Bee Colony and Improved Simulated Annealing for the Capacitated Vehicle Routing Problem.
Authors: Farhanna Mar'i, Hafidz Ubaidillah, Wayan Firdaus Mahmudy, Ahmad Afif Supianto
Journal: Knowl. Eng. Data Sci.
Year: 2022
Topics: Artificial Bee Colony; Simulated Annealing
Abstract: The Vehicle Routing Problem (VRP) is the most important in distribution management operations. VRP is faced by all organizations or companies involved in shipping and logistics. The primary purpose of VRP is to minimize travel costs for each vehicle route that serves customer requests with different location coordinates. Each delivery route will be started and ended by a depot or warehouse, and each customer will only be visited once [1][2]. VRP is one of the topics in optimizing complex combinatorial problems that researchers in computer science most often discuss. VRP solutions have specific objectives and limitations in real applications, making VRP have categories or variants [3]. The variants of VRP include VRP with Time Windows (VRPTW)[4], Multiple Depot VRP (MDVRP)[5], VRP with Backhauls [6], and Capacitated VRP (CVRP)[7]. One of the most popular VRP variants in this study will be discussed, namely the Capacitated Vehicle Routing Problem (CVRP). CVRP is included in the type of NP-Hard combinatorial problem that requires a high computational process [8]. In the case of CVRP, there is an additional constraint in the form of a capacity limit owned by the vehicle, so the complexity of the problem from CVRP is to find the optimum route pattern for minimizing travel costs which are also adjusted to customer demand and vehicle capacity for distribution [7]. One method of solving CVRP can be done by implementing a meta-heuristic algorithm that can be used to solve complex combinatorial optimization problems [9][10]. In recent years, the meta-heuristic algorithm has become a popular method researchers use because of its …
Detection of coronary heart disease using modified K-NN method with recursive feature elimination
Authors: William Hutamaputra, Marrisaeka Mawarni, Rifky Yunus Krisnabayu, Wayan Firdaus Mahmudy
Year: 2021
Topics: Machine Learning; Feature Selection
Abstract: Heart disease has infected many people in the world. One of the most common and deadly heart diseases is coronary artery disease (CAD). Several parameters can diagnose a patient who is positive for coronary artery disease (CAD). These parameters based on demographic, symptom and examination, ECG, and laboratory and echo features. For prevention needs to be done by early detection of patients who have the potential to have CAD. One way to do early detection is by building a system for making predictions. In this study, researchers used the KNN method by developing the weight of each class to increase accuracy. Before entering the KNN, the data will perform feature selection using SVM-RFE to find the ideal features and speed up computing time. The results of the KNN without feature selection are 82.65% of accuracy, the KNN with feature selection achieves 86.33% of accuracy, the Weighted KNN …