Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865585
T. Hariguna, Sarmini, A. Hananto
A web-based online complaint portal is one of the e-government public services. The complaint's content must be categorised in order for it to be transmitted to the appropriate agency swiftly and properly. The most often used standard classification algorithms are the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), both of which classify just one label and must be tuned. The purpose of this project is to categorize complaint messages that include several labels simultaneously using NBC tuned for particle swarm optimization (PSO). The data source is the Open Data Jakarta and is partitioned into 70% training data and 30% test data for classification into seven labels. The NBC and k-NN algorithms are used to compare PSO's optimization performance. Cross-validation ten times revealed that optimizing NBC with PSO obtained an accuracy of 88.16%, much superior than k-NN at 83% and NBC at 70.57%. This optimization approach may be used to improve the efficacy of community-based e-government services.
基于网络的网上投诉门户是电子政务公共服务的一种。投诉的内容必须分类，以便迅速和适当地转交给适当的机构。最常用的标准分类算法是朴素贝叶斯分类器(NBC)和k-最近邻分类器(k-NN)，这两种算法都只分类一个标签，并且必须进行调优。本项目的目的是对同时包含多个标签的投诉消息进行分类，使用针对粒子群优化(PSO)进行调优的NBC。数据源为Open data Jakarta，将其划分为70%的训练数据和30%的测试数据，分类为7个标签。用NBC算法和k-NN算法比较了粒子群算法的优化性能。10次交叉验证表明，PSO优化NBC的准确率为88.16%，明显优于k-NN的83%和NBC的70.57%。这种优化方法可用于提高基于社区的电子政务服务的效率。
{"title":"E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm","authors":"T. Hariguna, Sarmini, A. Hananto","doi":"10.1109/CyberneticsCom55287.2022.9865585","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865585","url":null,"abstract":"A web-based online complaint portal is one of the e-government public services. The complaint's content must be categorised in order for it to be transmitted to the appropriate agency swiftly and properly. The most often used standard classification algorithms are the Naive Bayes Classifier (NBC) and k-Nearest Neighbor (k-NN), both of which classify just one label and must be tuned. The purpose of this project is to categorize complaint messages that include several labels simultaneously using NBC tuned for particle swarm optimization (PSO). The data source is the Open Data Jakarta and is partitioned into 70% training data and 30% test data for classification into seven labels. The NBC and k-NN algorithms are used to compare PSO's optimization performance. Cross-validation ten times revealed that optimizing NBC with PSO obtained an accuracy of 88.16%, much superior than k-NN at 83% and NBC at 70.57%. This optimization approach may be used to improve the efficacy of community-based e-government services.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124910319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865265
Wahyu Nurharjadmo, Mutiara Auliya Khadija, T. Wahyuning
The use of android applications is substantial among the general public, making android applications one of the media used in the trade sector. Entrepreneurs compete in promoting products sold through the application to reach all circles of society. Although many have used applications in product sales, micro, small and medium entrepreneurs still have not used the Android inventory Information System due to the limited capabilities and information of micro, small and medium enterprises owners. In this study, there will be an application based on no code that can help small business owners to maintain their inventory products and buyers. Platform no code is a visual software development environment platform where users can drag and drop components such as buttons, drop-down boxes, etc. and connect them without a line of code or less. It is a quick way to develop a definite software or website. The platform based on no code used is AppSheet. AppSheet is an application development platform connected to the google sheet and google cloud designed for all users who want to create applications without having coding knowledge. This research will be made android application based on no code for micro, small and medium enterprises so that business owners can make applications without using coding in inventory issue. The owner can solve manual problem of administrative propose. After the application is made, the business owners can design the application as needed to obtain information from the products sold directly, maintain inventory data to accelerate digital transformation easily.
{"title":"Modern No Code Software Development Android Inventory System for Micro, Small and Medium Enterprises","authors":"Wahyu Nurharjadmo, Mutiara Auliya Khadija, T. Wahyuning","doi":"10.1109/CyberneticsCom55287.2022.9865265","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865265","url":null,"abstract":"The use of android applications is substantial among the general public, making android applications one of the media used in the trade sector. Entrepreneurs compete in promoting products sold through the application to reach all circles of society. Although many have used applications in product sales, micro, small and medium entrepreneurs still have not used the Android inventory Information System due to the limited capabilities and information of micro, small and medium enterprises owners. In this study, there will be an application based on no code that can help small business owners to maintain their inventory products and buyers. Platform no code is a visual software development environment platform where users can drag and drop components such as buttons, drop-down boxes, etc. and connect them without a line of code or less. It is a quick way to develop a definite software or website. The platform based on no code used is AppSheet. AppSheet is an application development platform connected to the google sheet and google cloud designed for all users who want to create applications without having coding knowledge. This research will be made android application based on no code for micro, small and medium enterprises so that business owners can make applications without using coding in inventory issue. The owner can solve manual problem of administrative propose. After the application is made, the business owners can design the application as needed to obtain information from the products sold directly, maintain inventory data to accelerate digital transformation easily.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865548
Nona Zarima, K. Muchtar, Akhyar Bintang, Maulisa Oktiana, Novi Maulina
Malaria is a parasitic infection spread by the plasmodium parasite. Malaria continues to be a major threat to world health, with an estimated 200 million cases and over 400,000 fatalities each year. When exposed to this disease, symptoms develop 10–15 days after the parasite enters the body. This disease becomes chronic if it is not treated medically, and it eventually leads to death. Using spatial information collected from microscopic images, several techniques based on image processing and machine learning have been utilized to diagnose malaria. Using the Local Binary Pattern (LBP) texture feature as a feature extraction approach, this study contributes to the development of a predictive and high-accuracy deep learning model by testing multiple Deep Learning models and determining which model delivers the best accuracy. To be specific, we tested frequently used baseline methods, namely ResNet34, VGG16, Inception V3, and EfficientNet. The results demonstrate that EfficientNet has a 91 percent outstanding accuracy rate, compared to 87 percent for VGG16, 81 percent for Resnet34, and 77 percent for InceptionV3, respectively.
{"title":"A Comparative Analysis of Deep Learning Models for Detecting Malaria Disease Through LBP Features","authors":"Nona Zarima, K. Muchtar, Akhyar Bintang, Maulisa Oktiana, Novi Maulina","doi":"10.1109/CyberneticsCom55287.2022.9865548","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865548","url":null,"abstract":"Malaria is a parasitic infection spread by the plasmodium parasite. Malaria continues to be a major threat to world health, with an estimated 200 million cases and over 400,000 fatalities each year. When exposed to this disease, symptoms develop 10–15 days after the parasite enters the body. This disease becomes chronic if it is not treated medically, and it eventually leads to death. Using spatial information collected from microscopic images, several techniques based on image processing and machine learning have been utilized to diagnose malaria. Using the Local Binary Pattern (LBP) texture feature as a feature extraction approach, this study contributes to the development of a predictive and high-accuracy deep learning model by testing multiple Deep Learning models and determining which model delivers the best accuracy. To be specific, we tested frequently used baseline methods, namely ResNet34, VGG16, Inception V3, and EfficientNet. The results demonstrate that EfficientNet has a 91 percent outstanding accuracy rate, compared to 87 percent for VGG16, 81 percent for Resnet34, and 77 percent for InceptionV3, respectively.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865480
Kasiful Aprianto, Arie Wahyu Wijayanto, S. Pramana
Satellite imageries data provides abundant geospatial features of infrastructures, land uses, land covers, and economic activity footprints that are potential for domainspecific tasks. In this study, we investigate the use of satellite imageries data as spatial-based proxy indicators in predicting the percentage of poverty in Banten Province, Indonesia using a deep learning approach. The poverty dataset is taken from the Village Potential Data Survey (PODES) 2018 results published by Statistics Indonesia (BPS) as the assumed ground-truth labels. Our finding reveals a correlation between the night-time light satellite imagery and the percentage of poverty, hence the regression model to predict the percentage of poverty is constructed using convolutional neural networks (CNN) architecture. The correlation between night-time image data and the percentage of poverty in each village is negative 52 percent under log transformation. Our proposed model generates a promising root mean squared error (RMSE) of 5.3023 which is potentially beneficial to support the construction and monitoring of poverty statistics in Indonesia.
{"title":"Deep Learning Approach using Satellite Imagery Data for Poverty Analysis in Banten, Indonesia","authors":"Kasiful Aprianto, Arie Wahyu Wijayanto, S. Pramana","doi":"10.1109/CyberneticsCom55287.2022.9865480","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865480","url":null,"abstract":"Satellite imageries data provides abundant geospatial features of infrastructures, land uses, land covers, and economic activity footprints that are potential for domainspecific tasks. In this study, we investigate the use of satellite imageries data as spatial-based proxy indicators in predicting the percentage of poverty in Banten Province, Indonesia using a deep learning approach. The poverty dataset is taken from the Village Potential Data Survey (PODES) 2018 results published by Statistics Indonesia (BPS) as the assumed ground-truth labels. Our finding reveals a correlation between the night-time light satellite imagery and the percentage of poverty, hence the regression model to predict the percentage of poverty is constructed using convolutional neural networks (CNN) architecture. The correlation between night-time image data and the percentage of poverty in each village is negative 52 percent under log transformation. Our proposed model generates a promising root mean squared error (RMSE) of 5.3023 which is potentially beneficial to support the construction and monitoring of poverty statistics in Indonesia.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865662
Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi
Overly trust in the autopilot agent has been identi-fied as the primary factor of road incidents involving autonomous cars. As this agent is considered a human driver counterpart in the collaborative driving context, many researchers suggest its transparency to mitigate such overly trust mental model. Hence, this paper aims to develop a driving situation inference method as a transparency provider explaining the types of situations the autopilot agent encounters leading to its certain decision. The proposed method is verified using an autonomous driving simulator called Carla. The findings show that the proposed method can generate situations which enable the human driver to calibrate their trust in the autopilot agent.
{"title":"A Driving Situation Inference for Autopilot Agent Transparency in Collaborative Driving Context","authors":"Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi","doi":"10.1109/CyberneticsCom55287.2022.9865662","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865662","url":null,"abstract":"Overly trust in the autopilot agent has been identi-fied as the primary factor of road incidents involving autonomous cars. As this agent is considered a human driver counterpart in the collaborative driving context, many researchers suggest its transparency to mitigate such overly trust mental model. Hence, this paper aims to develop a driving situation inference method as a transparency provider explaining the types of situations the autopilot agent encounters leading to its certain decision. The proposed method is verified using an autonomous driving simulator called Carla. The findings show that the proposed method can generate situations which enable the human driver to calibrate their trust in the autopilot agent.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133578792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865394
Abira Massi Armond, Y. D. Prasetyo, W. Ediningrum
The ever-increasing population and high mobility impact the massive number of vehicles that affect the development of public transportation and the determination of effective routes. These factors make it very important to optimize the route because it will impact operational costs and the punctuality of picking up passengers. Determining the optimal route can be categorized as a Traveling Salesman Problem (TSP). TSP is the activity of a salesman to visit each city exactly once and return to his hometown by minimizing the total cost. This study purposed to determine the optimal Trans Banyumas route by applying the Ant Colony Optimization (ACO) algorithm. ACO is an algorithm inspired by the behavior of ant colonies in searching for food by finding the shortest distance between the nest and the food source. The parameter values used in the ACO algorithm significantly affect the quality of the solution. The parameters used in this research are the maximum number of iterations, the number of ants, the pheromone evaporation constant, the pheromone intensity control, and the visibility control value. Based on the test results for the Trans Banyumas Corridor 3 using optimal parameters, the ACO algorithm found the shortest route with a total distance of 29.8 km. The determination of new corridor routes using the ACO algorithm was also successfully carried out, Corridor 4 with a distance of 30.8 km and Corridor 5 about 21.6 km.
{"title":"Application of Ant Colony Optimization (ACO) Algorithm to Optimize Trans Banyumas Bus Routes","authors":"Abira Massi Armond, Y. D. Prasetyo, W. Ediningrum","doi":"10.1109/CyberneticsCom55287.2022.9865394","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865394","url":null,"abstract":"The ever-increasing population and high mobility impact the massive number of vehicles that affect the development of public transportation and the determination of effective routes. These factors make it very important to optimize the route because it will impact operational costs and the punctuality of picking up passengers. Determining the optimal route can be categorized as a Traveling Salesman Problem (TSP). TSP is the activity of a salesman to visit each city exactly once and return to his hometown by minimizing the total cost. This study purposed to determine the optimal Trans Banyumas route by applying the Ant Colony Optimization (ACO) algorithm. ACO is an algorithm inspired by the behavior of ant colonies in searching for food by finding the shortest distance between the nest and the food source. The parameter values used in the ACO algorithm significantly affect the quality of the solution. The parameters used in this research are the maximum number of iterations, the number of ants, the pheromone evaporation constant, the pheromone intensity control, and the visibility control value. Based on the test results for the Trans Banyumas Corridor 3 using optimal parameters, the ACO algorithm found the shortest route with a total distance of 29.8 km. The determination of new corridor routes using the ACO algorithm was also successfully carried out, Corridor 4 with a distance of 30.8 km and Corridor 5 about 21.6 km.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132723821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865364
A. Wijayanto, Salwa Rizqina Putri
To enhance sustainable food security, the cost-efficient data collection technology for estimating rice production in a major agriculture nation such as Indonesia is undoubtedly vital to support the existing official data collection. The current official data collection is still facing great challenges in terms of its high cost and laborious nature. This study aims to build machine learning-based models for rice production estimation by utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data obtained from Landsat-8 remote sensing satellite imagery focusing on Ngawi Regency, East Java, Indonesia as a case study area. Our investigation reveals the quarterly changes in vegetation conditions of the rice fields can be captured through the NDVI value. Four different machine learning models are constructed and evaluated to process the satellite data. Support vector regression (SVR) was shown to obtain the best performance from 10-folds cross-validation with the average root mean square error (RMSE) of 6952.89 tons and has a quite high coefficient of determination (R2) score which is up to 0.9. The current estimation results provide an incentive to use satellite imagery data and machine learning models to support agricultural monitoring and decision-making.
{"title":"Estimating Rice Production using Machine Learning Models on Multitemporal Landsat-8 Satellite Images (Case Study: Ngawi Regency, East Java, Indonesia)","authors":"A. Wijayanto, Salwa Rizqina Putri","doi":"10.1109/CyberneticsCom55287.2022.9865364","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865364","url":null,"abstract":"To enhance sustainable food security, the cost-efficient data collection technology for estimating rice production in a major agriculture nation such as Indonesia is undoubtedly vital to support the existing official data collection. The current official data collection is still facing great challenges in terms of its high cost and laborious nature. This study aims to build machine learning-based models for rice production estimation by utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data obtained from Landsat-8 remote sensing satellite imagery focusing on Ngawi Regency, East Java, Indonesia as a case study area. Our investigation reveals the quarterly changes in vegetation conditions of the rice fields can be captured through the NDVI value. Four different machine learning models are constructed and evaluated to process the satellite data. Support vector regression (SVR) was shown to obtain the best performance from 10-folds cross-validation with the average root mean square error (RMSE) of 6952.89 tons and has a quite high coefficient of determination (R2) score which is up to 0.9. The current estimation results provide an incentive to use satellite imagery data and machine learning models to support agricultural monitoring and decision-making.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133903309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865598
Erwin Ardianto Halim, Gunaputra Wardhana, A. Sasongko
This study influenced the increasing use of e-commerce to buy and sell. Many offline stores today migrate to online stores. Seeing the opportunity to use e-commerce with billions of users enables businesses to expand sales and add customers as the business grows. The impact of a bad reputation and negative offline reviews, makes some sellers experience stagnation and difficulty in selling and even fail in online sales. Because of that, the sellers need to organize their and avoid losing trust. This study uses a Systematic Literature Review (SLR) for writing and modeling. It complements with a purposive sampling method by collecting questionnaire data, as many as 137 data on 22–24 April 2022 in Indonesia's Jabodetabek area. This study discusses the importance of variables in buying and selling in e-commerce for sellers, especially newcomers who have difficulty dealing with this problem, can overcome their problems. Those variables include Seller Attribute, Product Attribute, Customer Review, Customer Trust, and Purchase Intention. This study uses Structural Equation Modeling (SEM) with SmartPLS as a statistical analysis tool. A model approved six from seven hypotheses found to have a significant impact, with one hypothesis insignificant compared with previous research.
{"title":"Online Customer Reviwes as a Marketing Tool to Generate Customer Purchase Intention in Ecommerce","authors":"Erwin Ardianto Halim, Gunaputra Wardhana, A. Sasongko","doi":"10.1109/CyberneticsCom55287.2022.9865598","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865598","url":null,"abstract":"This study influenced the increasing use of e-commerce to buy and sell. Many offline stores today migrate to online stores. Seeing the opportunity to use e-commerce with billions of users enables businesses to expand sales and add customers as the business grows. The impact of a bad reputation and negative offline reviews, makes some sellers experience stagnation and difficulty in selling and even fail in online sales. Because of that, the sellers need to organize their and avoid losing trust. This study uses a Systematic Literature Review (SLR) for writing and modeling. It complements with a purposive sampling method by collecting questionnaire data, as many as 137 data on 22–24 April 2022 in Indonesia's Jabodetabek area. This study discusses the importance of variables in buying and selling in e-commerce for sellers, especially newcomers who have difficulty dealing with this problem, can overcome their problems. Those variables include Seller Attribute, Product Attribute, Customer Review, Customer Trust, and Purchase Intention. This study uses Structural Equation Modeling (SEM) with SmartPLS as a statistical analysis tool. A model approved six from seven hypotheses found to have a significant impact, with one hypothesis insignificant compared with previous research.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133940785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865266
J. Novelero, J. D. dela Cruz
Coconut harvesting in the Philippines is considered one of the most dangerous agricultural jobs because it is typically done by climbing the tree. Due to the height and structure of the tree, harvesting the so-called tree of life may pose fatal injuries or even death to the pickers. This paper presents an approach to leveraging Unmanned Aerial Vehicles (UAVs) to detect the mature on-tree coconut fruits. The proposed method would help set up the vision of the autonomous robots to be employed for coconut harvesting. The model used a Deep Learning algorithm, specifically the YOLOv5 Neural Network, to train, validate and test the custom dataset for coconut fruits and finally detect on-tree coconut fruits in real-time. The dataset was composed of 588 images for training, 168 images for validation, and 84 images for testing, where a DJI Mini SE drone captured all images and real-time detection scenarios. On the other hand, Python 3 Google Compute Engine backend (Tesla K80 GPU) in Google Collab was used to process the images and implement the algorithm. The investigation confirmed that the YOLOv5 model could instantaneously detect the on-tree mature coconut fruits. With an accuracy of 88.4%, the proposed approach will be of great value in eliminating the risks of harvesting coconuts in the future. The model can also be used for coconut crop yield estimation as the system mainly detects the visible mature fruits on the coconut tree. Finally, additional images with the presence of mature coconut fruits need to be collected to be used for training to improve the mAP of the proposed system.
{"title":"On-tree Mature Coconut Fruit Detection based on Deep Learning using UAV images","authors":"J. Novelero, J. D. dela Cruz","doi":"10.1109/CyberneticsCom55287.2022.9865266","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865266","url":null,"abstract":"Coconut harvesting in the Philippines is considered one of the most dangerous agricultural jobs because it is typically done by climbing the tree. Due to the height and structure of the tree, harvesting the so-called tree of life may pose fatal injuries or even death to the pickers. This paper presents an approach to leveraging Unmanned Aerial Vehicles (UAVs) to detect the mature on-tree coconut fruits. The proposed method would help set up the vision of the autonomous robots to be employed for coconut harvesting. The model used a Deep Learning algorithm, specifically the YOLOv5 Neural Network, to train, validate and test the custom dataset for coconut fruits and finally detect on-tree coconut fruits in real-time. The dataset was composed of 588 images for training, 168 images for validation, and 84 images for testing, where a DJI Mini SE drone captured all images and real-time detection scenarios. On the other hand, Python 3 Google Compute Engine backend (Tesla K80 GPU) in Google Collab was used to process the images and implement the algorithm. The investigation confirmed that the YOLOv5 model could instantaneously detect the on-tree mature coconut fruits. With an accuracy of 88.4%, the proposed approach will be of great value in eliminating the risks of harvesting coconuts in the future. The model can also be used for coconut crop yield estimation as the system mainly detects the visible mature fruits on the coconut tree. Finally, additional images with the presence of mature coconut fruits need to be collected to be used for training to improve the mAP of the proposed system.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116001497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865582
Dionisius Adianto Tirta Nugraha, A. Nasution
This paper describes research on texture feature extraction for COVID-19 detection. Fractal Dimension Texture Analysis (FDTA) and Gray Level Co-occurrence Matrix (GLCM) were used for feature extraction. A dense neural network is used for classification. Three classes were used for classification to classify Normal, COVID-19, and Other pneumonia. The data entered in the texture feature extraction is a chest x-ray (CXR) image that is grey scaled and resized into 400x400 pixels. Performance analysis of the model uses a confusion matrix. The best performance feature extraction method for detecting COVID-19 is FDTA, with an accuracy testing of 62.5%.
{"title":"Comparison of Texture Feature Extraction Method for COVID-19 Detection With Deep Learning","authors":"Dionisius Adianto Tirta Nugraha, A. Nasution","doi":"10.1109/CyberneticsCom55287.2022.9865582","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865582","url":null,"abstract":"This paper describes research on texture feature extraction for COVID-19 detection. Fractal Dimension Texture Analysis (FDTA) and Gray Level Co-occurrence Matrix (GLCM) were used for feature extraction. A dense neural network is used for classification. Three classes were used for classification to classify Normal, COVID-19, and Other pneumonia. The data entered in the texture feature extraction is a chest x-ray (CXR) image that is grey scaled and resized into 400x400 pixels. Performance analysis of the model uses a confusion matrix. The best performance feature extraction method for detecting COVID-19 is FDTA, with an accuracy testing of 62.5%.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115331080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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