Pub Date : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.627
Muhammad Imron Rosadi, Lukman Hakim, M. Faishol A.
Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
{"title":"Classification of Coffee Leaf Diseases using the Convolutional Neural Network (CNN) EfficientNet Model","authors":"Muhammad Imron Rosadi, Lukman Hakim, M. Faishol A.","doi":"10.34306/conferenceseries.v4i1.627","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.627","url":null,"abstract":"Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"182 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172428","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.649
Suryadi, Murhaban Murhaban, Rivansyah Suhendra
This study aimed to develop and compare classification models utilizing Decision Tree and K-Nearest Neighbors (KNN) in the detection of diseases in coffee leaf images. The dataset comprises coffee leaf images categorized into four different disease types, namely Nodisease, Miner, Phoma, and Rust. To facilitate model training and testing, the dataset was divided into training and validation data using a cross-validation approach. Both the Decision Tree and KNN models underwent meticulous parameter tuning. The experimental results reveal that the Decision Tree model achieved an accuracy rate of 98.20% on the validation data, while the KNN model achieved an accuracy rate of 75.01%. Furthermore, the Decision Tree model exhibited an AUC of 0.9879, recall of 0.9820, precision of 0.9835, and an F1-score of 0.9819 on the validation data. Conversely, the KNN model achieved an AUC of 0.9465, recall of 0.7501, precision of 0.7569, and an F1-score of 0.7485. These findings suggest that the Decision Tree model surpasses the KNN model in accurately detecting coffee leaf diseases, as demonstrated by higher accuracy and other evaluation metrics. However, the relevance of the KNN model remains contingent on application requirements and modeling preferences. These outcomes may contribute to the development of automated systems for disease detection in coffee plants, ultimately promoting more sustainable agricultural practices.
{"title":"Comparative Analysis of the Performance of the Decision Tree and K-Nearest Neighbors Methods in Classifying Coffee Leaf Diseases","authors":"Suryadi, Murhaban Murhaban, Rivansyah Suhendra","doi":"10.34306/conferenceseries.v4i1.649","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.649","url":null,"abstract":"This study aimed to develop and compare classification models utilizing Decision Tree and K-Nearest Neighbors (KNN) in the detection of diseases in coffee leaf images. The dataset comprises coffee leaf images categorized into four different disease types, namely Nodisease, Miner, Phoma, and Rust. To facilitate model training and testing, the dataset was divided into training and validation data using a cross-validation approach. Both the Decision Tree and KNN models underwent meticulous parameter tuning. The experimental results reveal that the Decision Tree model achieved an accuracy rate of 98.20% on the validation data, while the KNN model achieved an accuracy rate of 75.01%. Furthermore, the Decision Tree model exhibited an AUC of 0.9879, recall of 0.9820, precision of 0.9835, and an F1-score of 0.9819 on the validation data. Conversely, the KNN model achieved an AUC of 0.9465, recall of 0.7501, precision of 0.7569, and an F1-score of 0.7485. These findings suggest that the Decision Tree model surpasses the KNN model in accurately detecting coffee leaf diseases, as demonstrated by higher accuracy and other evaluation metrics. However, the relevance of the KNN model remains contingent on application requirements and modeling preferences. These outcomes may contribute to the development of automated systems for disease detection in coffee plants, ultimately promoting more sustainable agricultural practices.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"30 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171174","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.629
Didik Indrayana, Prajoko, Asril Adi Sunarto
PT XYZ Auto Body Repair is a company that focuses on repairing and servicing vehicles, especially cars that have been involved in accidents or disasters. Currently, data processing still uses physical forms, which has proven to be inefficient because it takes significant time, labor, and resources. Collecting and inputting data from various forms requires a large effort, while systems that are not integrated cause delays in providing the required information. These challenges impact the company's ability to make decisions quickly and on time, especially in the face of increasingly tight and complex business competition. Therefore, an efficient and integrated solution is needed. Seeing this problem, it was decided to develop an integrated vehicle repair system by applying agile development methods, especially the Extreme Programming model. This approach allows development in an iterative, fast, adaptive manner, and actively involves users at every stage of development. Experience has shown that applying the Extreme Programming model can produce an integrated system that meets user needs in a short time. With this system, companies can produce reports quickly without reduplication or repetitive data processing. All parts involved in the vehicle repair process will be connected to one company server, creating the efficiency and accuracy needed to support business growth in a dynamic business environment.
PT XYZ Auto Body Repair 是一家专注于维修和保养汽车的公司，尤其是涉及事故或灾难的汽车。目前，数据处理仍使用物理表单，这已被证明是低效的，因为它需要大量的时间、人力和资源。从各种表格中收集和输入数据需要耗费大量精力，而未集成的系统会导致所需信息的延迟提供。这些挑战影响了公司快速、及时做出决策的能力，尤其是在面对日益激烈和复杂的商业竞争时。因此，需要一个高效的集成解决方案。看到这个问题，公司决定采用敏捷开发方法，特别是极限编程模型，开发一个集成的车辆维修系统。这种方法允许以迭代、快速、适应性强的方式进行开发，并让用户积极参与开发的每个阶段。经验表明，应用极限编程模型可以在短时间内开发出满足用户需求的集成系统。有了这个系统，公司就可以快速生成报告，而无需重复或重复数据处理。车辆维修过程中涉及的所有部分都将连接到一个公司服务器，从而提高效率和准确性，在动态的商业环境中支持业务增长。
{"title":"Application of Agile Development Methods in the Development of Integrated Systems for Vehicle Body Repair","authors":"Didik Indrayana, Prajoko, Asril Adi Sunarto","doi":"10.34306/conferenceseries.v4i1.629","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.629","url":null,"abstract":"PT XYZ Auto Body Repair is a company that focuses on repairing and servicing vehicles, especially cars that have been involved in accidents or disasters. Currently, data processing still uses physical forms, which has proven to be inefficient because it takes significant time, labor, and resources. Collecting and inputting data from various forms requires a large effort, while systems that are not integrated cause delays in providing the required information. These challenges impact the company's ability to make decisions quickly and on time, especially in the face of increasingly tight and complex business competition. Therefore, an efficient and integrated solution is needed. Seeing this problem, it was decided to develop an integrated vehicle repair system by applying agile development methods, especially the Extreme Programming model. This approach allows development in an iterative, fast, adaptive manner, and actively involves users at every stage of development. Experience has shown that applying the Extreme Programming model can produce an integrated system that meets user needs in a short time. With this system, companies can produce reports quickly without reduplication or repetitive data processing. All parts involved in the vehicle repair process will be connected to one company server, creating the efficiency and accuracy needed to support business growth in a dynamic business environment.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"147 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171653","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.614
Wowon Priatna, Eka Nur A’ini, Joni Warta, Agus Hidayat, Tyastuti Sri Lestari, Rasim
In 2023, Indonesia was again devastated by a hacker known as Bjorka. Bjorka did not act just once or twice; every time, Bjorka made the entire Indonesian population proud. The 19 million BPJS Employment data belonging to the Indonesian people that Bjorka hacked is the BPJS Employment data belonging to the Indonesian people that Bjorka hacked. Since the release of the Bjorka story, there has been a surge in the number of people criticizing it on social media, particularly Facebook, so the criticism or opinions can be used to conduct sentiment analysis. Based on this, developing a method that can automatically classify beliefs into positive and negative categories through sentiment analysis is necessary. The sentiment analysis process begins with data preprocessing, followed by keyword analysis using the TF-IDF method, algorithm development, and analysis of classification results. The data classification methods used in this study are Naive Bayes and C4.5. The data will be analyzed using text mining and classified using the Naive Bayes and C4.5 algorithms. Based on the results of the tests, the best classification was achieved by Nave Bayes, with a score of 70 percent for the C4.5 algorithm and 68 percent for the C4.5 algorithm. The Nave Bayes algorithm can predict up to 70% data transmission rates for both positive and negative signals.
{"title":"Sentiment Analysis of Bjorka Hacker Using the Naive Bayes and C.45 Algorithms","authors":"Wowon Priatna, Eka Nur A’ini, Joni Warta, Agus Hidayat, Tyastuti Sri Lestari, Rasim","doi":"10.34306/conferenceseries.v4i1.614","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.614","url":null,"abstract":"In 2023, Indonesia was again devastated by a hacker known as Bjorka. Bjorka did not act just once or twice; every time, Bjorka made the entire Indonesian population proud. The 19 million BPJS Employment data belonging to the Indonesian people that Bjorka hacked is the BPJS Employment data belonging to the Indonesian people that Bjorka hacked. Since the release of the Bjorka story, there has been a surge in the number of people criticizing it on social media, particularly Facebook, so the criticism or opinions can be used to conduct sentiment analysis. Based on this, developing a method that can automatically classify beliefs into positive and negative categories through sentiment analysis is necessary. The sentiment analysis process begins with data preprocessing, followed by keyword analysis using the TF-IDF method, algorithm development, and analysis of classification results. The data classification methods used in this study are Naive Bayes and C4.5. The data will be analyzed using text mining and classified using the Naive Bayes and C4.5 algorithms. Based on the results of the tests, the best classification was achieved by Nave Bayes, with a score of 70 percent for the C4.5 algorithm and 68 percent for the C4.5 algorithm. The Nave Bayes algorithm can predict up to 70% data transmission rates for both positive and negative signals.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"85 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171202","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.633
Suprapto, Kenty W. Anita
According to Globocan records, in Indonesia in 2020 there were 396.314 new cancer cases. And 234.511 people were declared dead. Women are a group with a high risk of developing cancer. If cancer is detected at an early stage, this can increase the chance of cure to 80-90%. Early detection of cancer can be done using several methods, for example, for breast cancer, the method of checking can be using the SADANIS (Clinical Breast Examination) and SADARI (Self Breast Examination) methods. In this research, a mobile application will be developed that can be used as a guide in carrying out early cancer detection independently. The early detection system uses an Android-based expert system and certainty factor method. The case study in this research is on breast cancer. Based on the results of accuracy testing with expert diagnosis as a reference, an accuracy value of 90% was obtained. The inaccuracy of this expert system is 10% which can be caused by several possibilities, namely the expert's subjectivity in providing confidence values for disease symptoms or the small number of symptoms entered.
{"title":"Breast Cancer Screening Application Based on Android with the Certainty Factor Method","authors":"Suprapto, Kenty W. Anita","doi":"10.34306/conferenceseries.v4i1.633","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.633","url":null,"abstract":"According to Globocan records, in Indonesia in 2020 there were 396.314 new cancer cases. And 234.511 people were declared dead. Women are a group with a high risk of developing cancer. If cancer is detected at an early stage, this can increase the chance of cure to 80-90%. Early detection of cancer can be done using several methods, for example, for breast cancer, the method of checking can be using the SADANIS (Clinical Breast Examination) and SADARI (Self Breast Examination) methods. In this research, a mobile application will be developed that can be used as a guide in carrying out early cancer detection independently. The early detection system uses an Android-based expert system and certainty factor method. The case study in this research is on breast cancer. Based on the results of accuracy testing with expert diagnosis as a reference, an accuracy value of 90% was obtained. The inaccuracy of this expert system is 10% which can be caused by several possibilities, namely the expert's subjectivity in providing confidence values for disease symptoms or the small number of symptoms entered.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172253","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.643
A. M. Husein, Chalvin, Kalvintirta Ciptady Ciptady, Raymond Suryadi, M. Harahap
Football is one of the most popular sports worldwide and capable of attracting the attention of millions of fans to a single match in the top leagues. The English Premier League, Spanish LaLiga, German Bundesliga, Italian Serie A, and French Ligue 1 are the five best leagues in the world today. There was an experiment where researchers want to analyze the efficiency and accuracy percentage of tracking and detection using the deep learning method of the Mask R-CNN model in classifying positive and negative X-Ray images in football matches. In this study, we applied Mask R-CNN for the segmentation and detection of football players. This model was based on two different backbones, namely ResNet101 and DenseNet. Both backbones produced accuracy values that were not significantly different, but the DenseNet approach performed better than ResNet101 based on testing results in the validation and testing sets. Based on comprehensive experiment results on the dataset, it has been shown that the Mask R-CNN approach with DenseNet can achieve better results compared to Mask R-CNN with ResNet101. Due to insufficient understanding of the characteristics of image types and the uneven distribution of various types of data sourced from random videos, there was still room for improvement in the trained model.
{"title":"Detecting and Tracking Player in Football Videos Using Two-Stage Mask R-CNN Approach","authors":"A. M. Husein, Chalvin, Kalvintirta Ciptady Ciptady, Raymond Suryadi, M. Harahap","doi":"10.34306/conferenceseries.v4i1.643","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.643","url":null,"abstract":"Football is one of the most popular sports worldwide and capable of attracting the attention of millions of fans to a single match in the top leagues. The English Premier League, Spanish LaLiga, German Bundesliga, Italian Serie A, and French Ligue 1 are the five best leagues in the world today. There was an experiment where researchers want to analyze the efficiency and accuracy percentage of tracking and detection using the deep learning method of the Mask R-CNN model in classifying positive and negative X-Ray images in football matches. In this study, we applied Mask R-CNN for the segmentation and detection of football players. This model was based on two different backbones, namely ResNet101 and DenseNet. Both backbones produced accuracy values that were not significantly different, but the DenseNet approach performed better than ResNet101 based on testing results in the validation and testing sets. Based on comprehensive experiment results on the dataset, it has been shown that the Mask R-CNN approach with DenseNet can achieve better results compared to Mask R-CNN with ResNet101. Due to insufficient understanding of the characteristics of image types and the uneven distribution of various types of data sourced from random videos, there was still room for improvement in the trained model.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"382 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172155","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.623
Eltyasar Putrajati Noman, D. Setyohadi
Blockchain technology enables users to connect without the need for a third party or central server. This is achieved through the use of a decentralized system, ensuring that all data and information transacted are encrypted, verified, validated, and stored using mathematical consensus algorithms. This leads to blockchain being recognized as a technology characterized by decentralization, security, anonymity, transparency, immutable data, and trust. Blockchain is frequently associated with digital currency, although digital currency is just one of the outcomes of applying blockchain technology, resulting in cryptocurrencies. Currently, blockchain technology is a trend among academics and practitioners who are researching and developing blockchain technology for application in various domains, including government. Government systems and public servants often encounter issues related to data security. Hence, the research has the purpose to offer comprehension and perspectives on implementing blockchain technology within the government sector to enhance public service information security. The research was carried out by reviewing Scopus-indexed international articles published between 2019 and 2023, which are relevant to frameworks, consensus algorithms, and applications employed in the governmental domain. The research outcomes revealed that the Hyperledger Fabric framework, coupled with the Practical Byzantine Fault Tolerance (PBFT) algorithm, is the most suitable option for potentially developing blockchain-based government or public service applications for future implementation. Regarding this research, there are future challenges in the form of constructing prototypes and evaluating their effectiveness and efficiency. Therefore, further research and development efforts are essential to ensure that the application of blockchain technology in the government sector can be realized as required in the future.
{"title":"A Survey of Blockchain in Governance: Framework Selection and Future Implementation in Indonesian Government","authors":"Eltyasar Putrajati Noman, D. Setyohadi","doi":"10.34306/conferenceseries.v4i1.623","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.623","url":null,"abstract":"Blockchain technology enables users to connect without the need for a third party or central server. This is achieved through the use of a decentralized system, ensuring that all data and information transacted are encrypted, verified, validated, and stored using mathematical consensus algorithms. This leads to blockchain being recognized as a technology characterized by decentralization, security, anonymity, transparency, immutable data, and trust. Blockchain is frequently associated with digital currency, although digital currency is just one of the outcomes of applying blockchain technology, resulting in cryptocurrencies. Currently, blockchain technology is a trend among academics and practitioners who are researching and developing blockchain technology for application in various domains, including government. Government systems and public servants often encounter issues related to data security. Hence, the research has the purpose to offer comprehension and perspectives on implementing blockchain technology within the government sector to enhance public service information security. The research was carried out by reviewing Scopus-indexed international articles published between 2019 and 2023, which are relevant to frameworks, consensus algorithms, and applications employed in the governmental domain. The research outcomes revealed that the Hyperledger Fabric framework, coupled with the Practical Byzantine Fault Tolerance (PBFT) algorithm, is the most suitable option for potentially developing blockchain-based government or public service applications for future implementation. Regarding this research, there are future challenges in the form of constructing prototypes and evaluating their effectiveness and efficiency. Therefore, further research and development efforts are essential to ensure that the application of blockchain technology in the government sector can be realized as required in the future.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"148 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171559","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.655
Mohammad Amin, Wahyu Widji Pamungkas, Djoko Harsono
Applications of power supply systems to supply sensors that require high voltage values are widely available in the market in the form of modules. However, in general, setting the voltage value is open by providing a voltage value from the potentiometer or trimmer component which is rotated manually. This becomes less flexible because the operator must always be nearby. The solution option is to implement automatic regulation via a potentiometer attached to an interface component connected to the microcontroller via a serial communication line called I2C. Furthermore, the microcontroller is programmed to receive regulatory commands and monitor the desired voltage value from a computer or mobile phone. This study uses the ATmega328P microcontroller, the MCP4725 DAC module and the CA12P-5TR series HV module from EMCO products. The results of this study are the design, implementation and prototype scheme.
{"title":"Application System for Setting Values on High Voltage Power Supply Using MCP4725 Module Based on ATmega328P Microcontroller","authors":"Mohammad Amin, Wahyu Widji Pamungkas, Djoko Harsono","doi":"10.34306/conferenceseries.v4i1.655","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.655","url":null,"abstract":"Applications of power supply systems to supply sensors that require high voltage values are widely available in the market in the form of modules. However, in general, setting the voltage value is open by providing a voltage value from the potentiometer or trimmer component which is rotated manually. This becomes less flexible because the operator must always be nearby. The solution option is to implement automatic regulation via a potentiometer attached to an interface component connected to the microcontroller via a serial communication line called I2C. Furthermore, the microcontroller is programmed to receive regulatory commands and monitor the desired voltage value from a computer or mobile phone. This study uses the ATmega328P microcontroller, the MCP4725 DAC module and the CA12P-5TR series HV module from EMCO products. The results of this study are the design, implementation and prototype scheme.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"35 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171172","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.619
Green Ferry Mandias, Christiady Somba Sirappa, Pierre Jericho Effendy, Timothy Matthew, Jeremi Dirk
In everyday human life, there are many aspects that cause a decision to be made. Agreements can be made in writing or unwritten, reciprocal agreements and unilateral agreements, obligatory agreements and one of them is a lease agreement. The lease agreement can help the parties, both from the leaser and the lessee. Car Rental is one of the businesses providing transportation services that involves the use of mobile devices to find out information about the services provided by the company. Car Rental is closely related to transportation services to help people who need car rental for various purposes. To use rental services in Manado City, usually the tenant must go to the rental place, and that is less efficient to do. Therefore, the problem found is how to make the Semosemo Vehicle Rental Application in Manado City. With the aim of making the Semosemo Vehicle Rental Application in the City of Manado. This research uses the prototype method and is also assisted by software such as React Native, Figma, and Visual Studio Code. The result is that the Semosemo application can be made to help rent vehicles in the city of Manado and the application can run accordingly.
{"title":"The Semosemo: Vehicle Rental Application in Manado City","authors":"Green Ferry Mandias, Christiady Somba Sirappa, Pierre Jericho Effendy, Timothy Matthew, Jeremi Dirk","doi":"10.34306/conferenceseries.v4i1.619","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.619","url":null,"abstract":"In everyday human life, there are many aspects that cause a decision to be made. Agreements can be made in writing or unwritten, reciprocal agreements and unilateral agreements, obligatory agreements and one of them is a lease agreement. The lease agreement can help the parties, both from the leaser and the lessee. Car Rental is one of the businesses providing transportation services that involves the use of mobile devices to find out information about the services provided by the company. Car Rental is closely related to transportation services to help people who need car rental for various purposes. To use rental services in Manado City, usually the tenant must go to the rental place, and that is less efficient to do. Therefore, the problem found is how to make the Semosemo Vehicle Rental Application in Manado City. With the aim of making the Semosemo Vehicle Rental Application in the City of Manado. This research uses the prototype method and is also assisted by software such as React Native, Figma, and Visual Studio Code. The result is that the Semosemo application can be made to help rent vehicles in the city of Manado and the application can run accordingly.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"222 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171111","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 : 2023-12-19DOI: 10.34306/conferenceseries.v4i1.653
Ivana Lucia Kharisma, Kamdan Kamdan, Anggun Fergina, Tofik Hidayat, Moh. Abd. Aziz Hidayat, Muhamad Muslih, A. Erfina
The need for effective, accurate and precise understanding of information will provide optimization of the decision-making process, increase knowledge and quality of life. Understanding information in relation to the document summarization process, if done manually, sometimes takes quite a long time. Text summarization techniques which are useful as document summarizers have been developed and applied to various things such as summarizing important documents, news texts or customer feedback. In this article, text summarization using the text rank method and transformer modeling integrated with text to speech techniques is developed in the Bukudio application, which is an application that provides audio versions of book documents in the application database. Based on the test results, the evaluation process was carried out using the Rouge method and gave the best results in calculating the Rouge 1 overlap monogram resulting in 0.523 for the F1 Score value, 0.434 for the precision value and 0.659 for the recall value. This research will be developed using other methods so that not only files in PDF document format can be processed, but other EPUB (Electronic Publication) files.
{"title":"Integration of Transformer Model Text Summarization and Text-to-Speech in Helping Document Understanding in the Bukudio Application","authors":"Ivana Lucia Kharisma, Kamdan Kamdan, Anggun Fergina, Tofik Hidayat, Moh. Abd. Aziz Hidayat, Muhamad Muslih, A. Erfina","doi":"10.34306/conferenceseries.v4i1.653","DOIUrl":"https://doi.org/10.34306/conferenceseries.v4i1.653","url":null,"abstract":"The need for effective, accurate and precise understanding of information will provide optimization of the decision-making process, increase knowledge and quality of life. Understanding information in relation to the document summarization process, if done manually, sometimes takes quite a long time. Text summarization techniques which are useful as document summarizers have been developed and applied to various things such as summarizing important documents, news texts or customer feedback. In this article, text summarization using the text rank method and transformer modeling integrated with text to speech techniques is developed in the Bukudio application, which is an application that provides audio versions of book documents in the application database. Based on the test results, the evaluation process was carried out using the Rouge method and gave the best results in calculating the Rouge 1 overlap monogram resulting in 0.523 for the F1 Score value, 0.434 for the precision value and 0.659 for the recall value. This research will be developed using other methods so that not only files in PDF document format can be processed, but other EPUB (Electronic Publication) files.","PeriodicalId":505674,"journal":{"name":"Conference Series","volume":"48 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172081","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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