Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127768
Ronald Adrian, Tasya Widiasari, M. A. R. Somardani, Ahmad Jayadi Okke
IoT has become a magnet in today’s cyber world. In the past, household devices were still operated manually, but now they can connect to the internet. The advantage is that house residents can easily monitor and control their devices. With so many IoT devices, it will be more attractive for hackers to take them. There are lots of valuable assets on IoT devices that must be secured. One of them is data from these IoT devices. IoT hardware limitations are the main problem when carrying out a comprehensive data security process. The high computational load to perform this task cannot be adequately accommodated by IoT devices. Through this paper, we propose a clustering system based on the moth flame optimization algorithm to ease the performance of IoT hardware in securing each data. This method is efficient enough to reduce the computational load handled by the RAM and IoT processor. It is open to further improvement to get an end-to-end IoT security system and low computing load.
{"title":"Malware Clustering System using Moth-Flame Optimization as IoT Security Strengthening","authors":"Ronald Adrian, Tasya Widiasari, M. A. R. Somardani, Ahmad Jayadi Okke","doi":"10.1109/ICCoSITE57641.2023.10127768","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127768","url":null,"abstract":"IoT has become a magnet in today’s cyber world. In the past, household devices were still operated manually, but now they can connect to the internet. The advantage is that house residents can easily monitor and control their devices. With so many IoT devices, it will be more attractive for hackers to take them. There are lots of valuable assets on IoT devices that must be secured. One of them is data from these IoT devices. IoT hardware limitations are the main problem when carrying out a comprehensive data security process. The high computational load to perform this task cannot be adequately accommodated by IoT devices. Through this paper, we propose a clustering system based on the moth flame optimization algorithm to ease the performance of IoT hardware in securing each data. This method is efficient enough to reduce the computational load handled by the RAM and IoT processor. It is open to further improvement to get an end-to-end IoT security system and low computing load.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115295079","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127742
Kania Pradnya Sekardefi, Ridha Muldina Negara
The world has entered the development of the digital era, where all information can be obtained easily through internet services. The number of requests that are frequently requested makes internet network traffic only able to accommodate some user requests. A named data network (NDN) is here as a solution to overcome this problem. NDN changed the focus of the internet architecture, which was initially host-centric, to become content-centric. Caching on NDN router nodes can be used as a repository for passing content. Because IoT data always requires fresh data and real-time, one of the features in NDN called freshness can help maintain data freshness in the NDN router cache. This paper explores implementing the freshness method for content replacement decisions in the two cache replacement policies. Cache replacement policies are Least Recently Used (LRU) and First-in, first-out (FIFO). To validate the effectiveness of adding freshness-aware in the caching model, we run the emulation using an NDN emulator, Mini-NDN. The results show that freshness can maintain the freshness of data in IoT data and the performance of NDN caching with LRU policy increases based on parameters of the cache hit ratio and RTT compared to the FIFO policy.
{"title":"Impact of Data Freshness-aware in Cache Replacement Policy for NDN-based IoT Network","authors":"Kania Pradnya Sekardefi, Ridha Muldina Negara","doi":"10.1109/ICCoSITE57641.2023.10127742","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127742","url":null,"abstract":"The world has entered the development of the digital era, where all information can be obtained easily through internet services. The number of requests that are frequently requested makes internet network traffic only able to accommodate some user requests. A named data network (NDN) is here as a solution to overcome this problem. NDN changed the focus of the internet architecture, which was initially host-centric, to become content-centric. Caching on NDN router nodes can be used as a repository for passing content. Because IoT data always requires fresh data and real-time, one of the features in NDN called freshness can help maintain data freshness in the NDN router cache. This paper explores implementing the freshness method for content replacement decisions in the two cache replacement policies. Cache replacement policies are Least Recently Used (LRU) and First-in, first-out (FIFO). To validate the effectiveness of adding freshness-aware in the caching model, we run the emulation using an NDN emulator, Mini-NDN. The results show that freshness can maintain the freshness of data in IoT data and the performance of NDN caching with LRU policy increases based on parameters of the cache hit ratio and RTT compared to the FIFO policy.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131597548","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127677
Annisa Nurfadhilah
The current electricity demand is getting higher. In various parts of the world, especially Indonesia, electrical Energy is widely used for various activities, especially in economic and industrial activities. Based on forecasts of electricity demand from 2016 to 2025, electricity demand in 2025 is 457 TWh. Fossil (nonrenewable) energy generators still dominate Indonesia's power generation system. As a result, installed capacity in 2018 was primarily derived from fossil energy generation, especially coal (50%), followed by natural gas (29%), fuels (7%), and renewables (14%). continued. With the enactment of Law No. 16 of 2016 on the Paris Agreement of the United Nations Framework Convention on Climate Change, RUPTL supports the government's commitment to reduce greenhouse gas emissions by 29% by 2030. In this paper, we propose a grid-connected PV-Ocean Wave hybrid renewable energy generation system with batteries as energy storage devices as one of the solutions for maximum energy generation. The research location is located in Kahu-Kahu, Bontoharu, Selayar Islands Regency. This location is one of several remote areas that need electricity evenly. This research demonstrates an optimal hybrid power plant design in terms of economy and Energy produced using EHO (Elephant Herding Optimization) in MATLAB. The results showed that the best value for a hybrid generator between PV and wave energy was the best power per year: 216943.935 kWh. The number of batteries needed is 132 pieces. The total number of PV units required is 1824. The number of wave generators required is three units. Total NPCs: Rp. 257,281,230.8308. COE value: Rp. 9,632,081.6556.
{"title":"Design Optimization of Hybrid Generation System Using Solar Energy and Ocean Waves With Elephant Herding Optimization Method","authors":"Annisa Nurfadhilah","doi":"10.1109/ICCoSITE57641.2023.10127677","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127677","url":null,"abstract":"The current electricity demand is getting higher. In various parts of the world, especially Indonesia, electrical Energy is widely used for various activities, especially in economic and industrial activities. Based on forecasts of electricity demand from 2016 to 2025, electricity demand in 2025 is 457 TWh. Fossil (nonrenewable) energy generators still dominate Indonesia's power generation system. As a result, installed capacity in 2018 was primarily derived from fossil energy generation, especially coal (50%), followed by natural gas (29%), fuels (7%), and renewables (14%). continued. With the enactment of Law No. 16 of 2016 on the Paris Agreement of the United Nations Framework Convention on Climate Change, RUPTL supports the government's commitment to reduce greenhouse gas emissions by 29% by 2030. In this paper, we propose a grid-connected PV-Ocean Wave hybrid renewable energy generation system with batteries as energy storage devices as one of the solutions for maximum energy generation. The research location is located in Kahu-Kahu, Bontoharu, Selayar Islands Regency. This location is one of several remote areas that need electricity evenly. This research demonstrates an optimal hybrid power plant design in terms of economy and Energy produced using EHO (Elephant Herding Optimization) in MATLAB. The results showed that the best value for a hybrid generator between PV and wave energy was the best power per year: 216943.935 kWh. The number of batteries needed is 132 pieces. The total number of PV units required is 1824. The number of wave generators required is three units. Total NPCs: Rp. 257,281,230.8308. COE value: Rp. 9,632,081.6556.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131937174","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127767
Evan Enza Rizqi, Cutifa Safitri
The manufacturing company with a core business of manufacturing electricity meters has a testing system for the metrology industry, namely calibration testing. The part of calibration testing on an electricity meter is the verification test, which uses the method of comparing it to the standard meter test bench to calculate the accuracy error of the measurement. However, the problem is that carrying out this test requires a long cycle time, and it is difficult to increase production capacity without adding a standard meter calibration test bench which has an expensive investment. The smart factory concept that supports industry 4.0 can open up opportunities for research on information technology using artificial intelligence with machine learning models to solve this problem with the idea of creating intelligent testing. This research requires data collection on a calibration test bench machine, then processed to find model predictions so that they can be implemented into an intelligent test using the XGBoost Regression with Hyperparameter Tuning and Optimization methods as the Goal of this Research. In the results of this research, the evaluation of the XGBoost using the Hyperparameter Tuning and Optimization method, which is implemented in this case, could improve the accuracy and RMSE data testing modelling comparing other scenario models as defined before in the literature review. So, this can be an excellent solution to be applied in metrology manufacturing, especially verification tests in Manufacturing Calibration Testing on Electricity Meter, which is faster and low-investment testing with the implementation of an Intelligent Manufacturing Calibration Test.
{"title":"An Intelligent Calibration Testing of Electricity Meter using XGBoost for Manufacturing 4.0","authors":"Evan Enza Rizqi, Cutifa Safitri","doi":"10.1109/ICCoSITE57641.2023.10127767","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127767","url":null,"abstract":"The manufacturing company with a core business of manufacturing electricity meters has a testing system for the metrology industry, namely calibration testing. The part of calibration testing on an electricity meter is the verification test, which uses the method of comparing it to the standard meter test bench to calculate the accuracy error of the measurement. However, the problem is that carrying out this test requires a long cycle time, and it is difficult to increase production capacity without adding a standard meter calibration test bench which has an expensive investment. The smart factory concept that supports industry 4.0 can open up opportunities for research on information technology using artificial intelligence with machine learning models to solve this problem with the idea of creating intelligent testing. This research requires data collection on a calibration test bench machine, then processed to find model predictions so that they can be implemented into an intelligent test using the XGBoost Regression with Hyperparameter Tuning and Optimization methods as the Goal of this Research. In the results of this research, the evaluation of the XGBoost using the Hyperparameter Tuning and Optimization method, which is implemented in this case, could improve the accuracy and RMSE data testing modelling comparing other scenario models as defined before in the literature review. So, this can be an excellent solution to be applied in metrology manufacturing, especially verification tests in Manufacturing Calibration Testing on Electricity Meter, which is faster and low-investment testing with the implementation of an Intelligent Manufacturing Calibration Test.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132059786","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127718
Aldiyan Farhan Nugroho, Reza Rendian Septiawan, I. Kurniawan
Dementia is a fast-growing public health problem, with an estimated 47 million people currently living with the condition. By 2030, this total is predicted to reach 75 million. By 2050, it will have tripled were, given the urgent need to address this problem. Alzheimer's disease is characterized by a steady decline in cognitive capacities beginning with a decrease in the brain's capacity to form new memories. Significant attention has been focused on developing therapeutic strategies and drugs to treat Alzheimer's disease, which is the most common form of dementia. In this study, the feature used is the PubChem Fingerprint representing the molecule's structure with a total of 822 data for class 0 and 691 data for class 1. We developed a fingerprint-based artificial neural network (ANN) model to predict Beta-secretase 1 (BACE-1) inhibitors as therapeutic agents for Alzheimer's disease. Three optimization strategies, namely the Bat Algorithm, the Hybrid Bat Algorithm, and the Adaptive Bat Algorithm, were used to optimize the architecture of the ANN. This nature-inspired optimization technique mimics the echolocation behavior of bats. The best model was obtained from ANN optimized using Hybrid Bat Algorithm with the value of accuracy and F1-score are 0.81 and 0.78, respectively.
{"title":"Prediction of Human β-secretase 1 (BACE-1) Inhibitors for Alzheimer Therapeutic Agent by Using Fingerprint-based Neural Network Optimized by Bat Algorithm","authors":"Aldiyan Farhan Nugroho, Reza Rendian Septiawan, I. Kurniawan","doi":"10.1109/ICCoSITE57641.2023.10127718","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127718","url":null,"abstract":"Dementia is a fast-growing public health problem, with an estimated 47 million people currently living with the condition. By 2030, this total is predicted to reach 75 million. By 2050, it will have tripled were, given the urgent need to address this problem. Alzheimer's disease is characterized by a steady decline in cognitive capacities beginning with a decrease in the brain's capacity to form new memories. Significant attention has been focused on developing therapeutic strategies and drugs to treat Alzheimer's disease, which is the most common form of dementia. In this study, the feature used is the PubChem Fingerprint representing the molecule's structure with a total of 822 data for class 0 and 691 data for class 1. We developed a fingerprint-based artificial neural network (ANN) model to predict Beta-secretase 1 (BACE-1) inhibitors as therapeutic agents for Alzheimer's disease. Three optimization strategies, namely the Bat Algorithm, the Hybrid Bat Algorithm, and the Adaptive Bat Algorithm, were used to optimize the architecture of the ANN. This nature-inspired optimization technique mimics the echolocation behavior of bats. The best model was obtained from ANN optimized using Hybrid Bat Algorithm with the value of accuracy and F1-score are 0.81 and 0.78, respectively.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514960","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127691
Y. Heryadi, B. Wijanarko, Dina Fitria Murad, C. Tho, Kiyota Hashimoto
Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively; and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively; and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset.
当人们与说不同语言的人交流时，将一种语言翻译成另一种语言已经成为一种工具。然而，在存在语言资源缺口的情况下，语言翻译并不是一项简单的计算任务。本文以印尼语和巽他语为研究对象，对两种机器语言翻译模型:长短期记忆模型和双向长短期记忆模型的性能进行了实证研究。实证结果表明，双向长短期记忆模型在Sundanese语和Bahasa Indonesia -反之的语言翻译中获得了更高的表现(平均训练准确率分别为0.95和0.95);和平均测试BLEU分数分别为0.90和0.89)比长短期记忆模型作为语言翻译从巽他语到印尼语，反之亦然(平均训练准确率分别为0.93和0.92;BLEU平均分分别为0.91和0.88)。这些结果验证了先前报道的一些研究，这些研究声称双向长短期记忆模型在处理序列数据集时可能优于长短期记忆模型。
{"title":"Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model","authors":"Y. Heryadi, B. Wijanarko, Dina Fitria Murad, C. Tho, Kiyota Hashimoto","doi":"10.1109/ICCoSITE57641.2023.10127691","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127691","url":null,"abstract":"Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively; and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively; and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126634781","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127694
Efendi Efendi, Gabriel Michael Ivan Santosa, Christopher Jourdan, A. Gui, A. A. Pitchay, Y. Ganesan
In the midst of the ongoing industrial revolution and significant technological developments, it is important for every industry to apply the latest technologies to increase the company's growth scale in this intense competition. Customer Relationship Management (CRM) is a technology that has been widely used in various industries to increase user intensity in using products and interacting with users. The purpose of this study is to analyse the effectiveness of CRM implementation using health mobile application in Indonesia. The nature of this research is quantitative and the data collected is through questionnaires which are distributed by purposive sampling technique on social media which is focused on respondents who understand CRM technology in the Indonesian region. This study found that the healthcare applications that will implement CRM tend to increase customer satisfaction. In addition, high customer satisfaction is considered to increase user retention and also studies find that the implementation of a CRM system affects customer retention and satisfaction.
{"title":"Customer Relationship Management, Customer Retention, and the Mediating Role of Customer Satisfaction on a Healthcare Mobile Applications","authors":"Efendi Efendi, Gabriel Michael Ivan Santosa, Christopher Jourdan, A. Gui, A. A. Pitchay, Y. Ganesan","doi":"10.1109/ICCoSITE57641.2023.10127694","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127694","url":null,"abstract":"In the midst of the ongoing industrial revolution and significant technological developments, it is important for every industry to apply the latest technologies to increase the company's growth scale in this intense competition. Customer Relationship Management (CRM) is a technology that has been widely used in various industries to increase user intensity in using products and interacting with users. The purpose of this study is to analyse the effectiveness of CRM implementation using health mobile application in Indonesia. The nature of this research is quantitative and the data collected is through questionnaires which are distributed by purposive sampling technique on social media which is focused on respondents who understand CRM technology in the Indonesian region. This study found that the healthcare applications that will implement CRM tend to increase customer satisfaction. In addition, high customer satisfaction is considered to increase user retention and also studies find that the implementation of a CRM system affects customer retention and satisfaction.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113949473","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127752
Rudy, Mulan Mudita Tantra, Yosua Sinatra, Jordy Jonathan
During post–COVID 19 in Indonesia government encouraged their people to use digital payment to prevent any close contact between people that can increase the spread of COVID - 19 virus. All businesses from small to a big company are required to move fast to provide innovations to suit this. Along with the development of digital payment today, there are several start up digital wallets that show up with several options. With a lot of digital wallets showing up will definitely increase competitors and more options to users can use, however because of lack of knowledge and resources, small businesses still cannot implement digital payment on their business. This paper will examine several subjects that impact customer satisfaction to increase competitions between digital wallets. We offer valuable information about proving several subjects that will improve customer satisfaction and these companies can get an advantage compared to their competitors so they can increase their active users.
{"title":"Customer Satisfaction of Using Digital Wallet During Post – COVID 19","authors":"Rudy, Mulan Mudita Tantra, Yosua Sinatra, Jordy Jonathan","doi":"10.1109/ICCoSITE57641.2023.10127752","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127752","url":null,"abstract":"During post–COVID 19 in Indonesia government encouraged their people to use digital payment to prevent any close contact between people that can increase the spread of COVID - 19 virus. All businesses from small to a big company are required to move fast to provide innovations to suit this. Along with the development of digital payment today, there are several start up digital wallets that show up with several options. With a lot of digital wallets showing up will definitely increase competitors and more options to users can use, however because of lack of knowledge and resources, small businesses still cannot implement digital payment on their business. This paper will examine several subjects that impact customer satisfaction to increase competitions between digital wallets. We offer valuable information about proving several subjects that will improve customer satisfaction and these companies can get an advantage compared to their competitors so they can increase their active users.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121516607","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127699
Nicholas Hadi, V. C. Mawardi, J. Hendryli
Discord is growing in popularity, makes hard for an admin of Discord server maintaining their member in their everyday chat activity in their server. This no longer an issue if there is Discord bot that can detect hate speech feature in text message that member send and automatically censor them. The classifier for this experiment is using Convolutional Neural Network (CNN) method. The dataset for training and validation model are containing total 6 category of hates speech, abusive language, religion, race, gender, physical, and non-hate speech. The Discord bot program only can classify a message in Indonesian language. The dataset used for training and validation models was obtained from Kaggle and for additional data taken from Discord server messages totaling 18,986 sentences which will be divided by 80% training data and 20% test data. The final results of the training model experiment, this CNN model can classify test data with an average precision value of 89%, 90% recall, and 88,33% F1 score. The CNN model is integrated into a bot application which will be tested on messages sent from the test Discord server. Out of 279 messages, the designed Discord bot can obtain an accuracy of 70.6%.
{"title":"Discord Bot Design for Hate Speech Sensor Using Convolutional Neural Networks (CNN) Method","authors":"Nicholas Hadi, V. C. Mawardi, J. Hendryli","doi":"10.1109/ICCoSITE57641.2023.10127699","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127699","url":null,"abstract":"Discord is growing in popularity, makes hard for an admin of Discord server maintaining their member in their everyday chat activity in their server. This no longer an issue if there is Discord bot that can detect hate speech feature in text message that member send and automatically censor them. The classifier for this experiment is using Convolutional Neural Network (CNN) method. The dataset for training and validation model are containing total 6 category of hates speech, abusive language, religion, race, gender, physical, and non-hate speech. The Discord bot program only can classify a message in Indonesian language. The dataset used for training and validation models was obtained from Kaggle and for additional data taken from Discord server messages totaling 18,986 sentences which will be divided by 80% training data and 20% test data. The final results of the training model experiment, this CNN model can classify test data with an average precision value of 89%, 90% recall, and 88,33% F1 score. The CNN model is integrated into a bot application which will be tested on messages sent from the test Discord server. Out of 279 messages, the designed Discord bot can obtain an accuracy of 70.6%.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116607623","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 : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127834
Novialdi Ashari, Mokhamad Zukhruf Mifta Al Firdaus, I. Budi, A. Santoso, Prabu Kresna Putra
Electrical vehicles (EVs) are one of the solutions to tackle the issues of greenhouse gas emissions and climate change in the world. In Indonesia, the government has made regulations supporting the implementation of EVs through various incentive programs and infrastructure developments, which are expected to increase public interest in the use of EVs. However, there are still many pros and cons found in the use of EVs in Indonesia, especially in social media. In this paper, we discuss the implementation of sentiment analysis models through social media, Twitter. It uses supervised learning methods, such as Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting Algorithm, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). The total data used is 7102 tweets with 2847 tweet samples to become labeling data. The results of the analysis are as many as 1586 tweets (55,71%) responded positively and 1261 (44,29%) responded negatively to EVs. SVM is the best model with 75.08% accuracy and the most topics that support EVs to appear were the temporary G20 activities and the benefit of EVs with positive support of tweets. And others tend to prioritize primary needs than own EVs. We utilize Latent Dirichlet Allocation (LDA) to examine topics related to EVs in Indonesia. Finally, this paper contributes to extending knowledge of sentiment methods from the discussion that sticks out on social media, and suitable techniques for conducting research related to sentiment analysis as well as topics of discussion that are closely related to the issue of EVs.
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