Erwin Ardianto Halim, Gunaputra Wardhana, A. Sasongko
{"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}
{"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}
Rinta Kridalukmana, D. Eridani, Risma Septiana, A. F. Rochim, Charisma T. Setyobudhi
{"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}
Y. Heryadi, B. Wijanarko, D. F. Murad, C. Tho, Kiyota Hashimoto
{"title":"Aspect-based Sentiment Analysis for Improving Online Learning Program Based on Student Feedback","authors":"Y. Heryadi, B. Wijanarko, D. F. Murad, C. Tho, Kiyota Hashimoto","doi":"10.1109/CyberneticsCom55287.2022.9865450","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865450","url":null,"abstract":"This paper presents an empiric results of aspectbased sentiment analysis in education to extract and classify opinions, sentiments, evaluations, attitudes, and emotions from newly graduates of an online learning program. As part of continuous education monitoring system, the sentiment analysis process produces valuable input to leverage service quality of online learning program. In this study, the aspect-based sentiment analysis is implemented to analyze a set of feedbacks from 162 newly graduate from Binus Online Program majoring in Accounting, Management, Information System, and Computer Science. The important qualitative results of this study are confirmation that the main benefits of online learning from student perspective are mainly: the knowledge they gained from the program, learning guidance, reliable student team to work on thesis, quality of education support system, and learning happiness.","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":"114163529","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}
Nona Zarima, K. Muchtar, Akhyar Bintang, Maulisa Oktiana, Novi Maulina
{"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}
{"title":"Classifying Portable Executable Malware Using Deep Neural Decision Tree","authors":"Rico S. Santos, E. Festijo","doi":"10.1109/CyberneticsCom55287.2022.9865320","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865320","url":null,"abstract":"Despite the extensive use of malware technologies, malware detection is still a challenge today, especially with the daily cyber-attack barrage. Data analysis coupled with machine learning techniques is gaining popularity as one of the approaches deployed to address this issue. This paper proposed a new technique for classifying malware from a large Portable Executable file (PEFile) using a deep neural decision tree. Every node in a hybrid approach represents a neural network trained to identify a single output category using binary classification as a decision tree. The dataset used in this study includes both benign (7,196) and malicious (16,698) PE files with 14 features extracted from the PEFile headers. Precision is 0.88, Recall is 0.32, Matthew Coefficient Correlation (MCC) is 0.302, Area Under the Curve (AUC) Receiving Operating Characteristic (ROC) with an AUC value of 0.63, and Average Precision score of 0.69 was used to evaluate the classifier. The result shows that binary classifier can distinguish between two classes: (1) malware and (2) benign.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"107 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":"116997121","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}
Kasiful Aprianto, Arie Wahyu Wijayanto, S. Pramana
{"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}
Wahyu Nurharjadmo, Mutiara Auliya Khadija, T. Wahyuning
{"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}
{"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}
{"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}