Every time they get a project, planning consultants and building implementers always work with contractors to work on the project. However, in project work, there are often several obstacles to the selected contractor, such as delays in work, workmanship not according to plan, and inappropriate building specifications to unskilled experts. This happens because the contractor selection process that is not right causes the construction phase to be not optimal it affects the quality of the resulting building. The selection of contractors involves a complex multi-criteria where each criterion used has different importance and the information about it is not known precisely so a method is needed to overcome these problems. For this reason, it is necessary to have a system in supporting contractor selection decisions, known as the Decision Support System. This system was built using the Rapid Application Development (RAD) design method with the stages of Requirements Planning, User Design, Construction, and Cutover. The construction of this DSS uses one of the methods in making decisions, namely Simple Additive Weighting (SAW) to make an assessment based on predetermined decision-making criteria. From this research, a website-based decision support system was obtained using the javascript programming language, with a score of 79.7% for the ease of use of the system, and a 93.3% score for the usefulness of the system.
{"title":"Simple Additive Weighting in the Development of a Decision Support System for the Selection of House Construction Project Teams","authors":"H. Aulawi, Fitri Nuraeni, Ridwan Setiawan, Wiby Fabian Rianto, Adhitya Surya Pratama, Helmi Maulana","doi":"10.1109/ICCoSITE57641.2023.10127813","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127813","url":null,"abstract":"Every time they get a project, planning consultants and building implementers always work with contractors to work on the project. However, in project work, there are often several obstacles to the selected contractor, such as delays in work, workmanship not according to plan, and inappropriate building specifications to unskilled experts. This happens because the contractor selection process that is not right causes the construction phase to be not optimal it affects the quality of the resulting building. The selection of contractors involves a complex multi-criteria where each criterion used has different importance and the information about it is not known precisely so a method is needed to overcome these problems. For this reason, it is necessary to have a system in supporting contractor selection decisions, known as the Decision Support System. This system was built using the Rapid Application Development (RAD) design method with the stages of Requirements Planning, User Design, Construction, and Cutover. The construction of this DSS uses one of the methods in making decisions, namely Simple Additive Weighting (SAW) to make an assessment based on predetermined decision-making criteria. From this research, a website-based decision support system was obtained using the javascript programming language, with a score of 79.7% for the ease of use of the system, and a 93.3% score for the usefulness of the system.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"329 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":"122743058","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/ICCoSITE57641.2023.10127676
Jajang Taupik, Tossin Alamsyah, Asri Wulandari, Edmund Ucok Armin, A. Hikmaturokhman
Today, every airport manager in various countries has tightened runway security to avoid the entry of foreign objects that can endanger passengers and aircraft both when landing and taking off. Inspection and supervision of the runway must be carried out regularly. However, there are still many airports that carry out inspections and supervision by human labor without any technological support. Whereas inspection and supervision using human labor takes a relatively long time and is prone to errors, especially in bad weather and limited visibility. Technological developments in runway security using radar are one of the solutions. However, radar technology is relatively expensive, so many airport managers use computer vision because it is considered cheaper and more accurate. The use of computer vision has grown rapidly in monitoring FOD on aircraft runways. Our method is an impovement of the YOLOX architecture by moving output objects to branch classes. Our method got a MAP score of 0.832 which has an increase in score of 0.021 from the previous method in detecting FOD in classes of people, vehicles, birds, cats and dogs.
{"title":"Airport Runway Foreign Object Debris (FOD) Detection Based on YOLOX Architecture","authors":"Jajang Taupik, Tossin Alamsyah, Asri Wulandari, Edmund Ucok Armin, A. Hikmaturokhman","doi":"10.1109/ICCoSITE57641.2023.10127676","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127676","url":null,"abstract":"Today, every airport manager in various countries has tightened runway security to avoid the entry of foreign objects that can endanger passengers and aircraft both when landing and taking off. Inspection and supervision of the runway must be carried out regularly. However, there are still many airports that carry out inspections and supervision by human labor without any technological support. Whereas inspection and supervision using human labor takes a relatively long time and is prone to errors, especially in bad weather and limited visibility. Technological developments in runway security using radar are one of the solutions. However, radar technology is relatively expensive, so many airport managers use computer vision because it is considered cheaper and more accurate. The use of computer vision has grown rapidly in monitoring FOD on aircraft runways. Our method is an impovement of the YOLOX architecture by moving output objects to branch classes. Our method got a MAP score of 0.832 which has an increase in score of 0.021 from the previous method in detecting FOD in classes of people, vehicles, birds, cats and dogs.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"6 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":"125594548","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/ICCoSITE57641.2023.10127706
Farrel Rasyad, Hardi Andry Kongguasa, Nicholas Christandy Onggususilo, Anderies, Afdhal Kurniawan, A. A. Gunawan
Humans have studied calligraphy and calculated programs to foster creativity for years. Image generation technology using artificial intelligence and Generative Adversarial Networks is currently reaching the peak of its performance. While there are newer and newer algorithms to improve the image generation system, the output of the images is still suitable at best and only excels in their category. While it is true that some of the images generated are good enough to be used, it is still unclear whether the capabilities of AI image generation can outperform their creative human counterparts. Therefore, this literature study aims to explore the basics of AI image generation, how they work, and what factors contribute to creating art such as simple pictures. Previous studies from several years ago show that most generated images are not good enough for creative usage because they only replicate traces of their dataset. The most significant factor contributing to this is the algorithm used and how it is used to create new images. In general, the concluded that while current AI-generated images are improving, they are still not creative enough to replace human creativity.
{"title":"A Systematic Literature Review of Generative Adversarial Network Potential In AI Artwork","authors":"Farrel Rasyad, Hardi Andry Kongguasa, Nicholas Christandy Onggususilo, Anderies, Afdhal Kurniawan, A. A. Gunawan","doi":"10.1109/ICCoSITE57641.2023.10127706","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127706","url":null,"abstract":"Humans have studied calligraphy and calculated programs to foster creativity for years. Image generation technology using artificial intelligence and Generative Adversarial Networks is currently reaching the peak of its performance. While there are newer and newer algorithms to improve the image generation system, the output of the images is still suitable at best and only excels in their category. While it is true that some of the images generated are good enough to be used, it is still unclear whether the capabilities of AI image generation can outperform their creative human counterparts. Therefore, this literature study aims to explore the basics of AI image generation, how they work, and what factors contribute to creating art such as simple pictures. Previous studies from several years ago show that most generated images are not good enough for creative usage because they only replicate traces of their dataset. The most significant factor contributing to this is the algorithm used and how it is used to create new images. In general, the concluded that while current AI-generated images are improving, they are still not creative enough to replace human creativity.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"76 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":"128393549","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/ICCoSITE57641.2023.10127683
Muhammad Fikri Hasani, Y. Heryadi, Yulyani Arifin, Lukas, W. Suparta
Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.
{"title":"Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering","authors":"Muhammad Fikri Hasani, Y. Heryadi, Yulyani Arifin, Lukas, W. Suparta","doi":"10.1109/ICCoSITE57641.2023.10127683","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127683","url":null,"abstract":"Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"40 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":"124795779","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/ICCoSITE57641.2023.10127853
A. T. Santoso, M. R. Rosa, Edwar
This paper proposes the Model Reference Adaptive Control (MRAC) design for the CubeSat 1U prototype with a magnetorquer to control the yaw angle. In practice, the system dynamics parameters of the CubeSat 1U, such as the moment inertia and mass, are unknown. To handle the uncertainties of the parameters, the authors propose MRAC to control the yaw angle of the CubeSat 1U. The controller is designed and deployed using MATLAB, which is connected via Bluetooth to the CubeSat 1U. In the experiment, the communication delay occurs and causes deteriorated output response of standard MRAC. The modified MRAC and redesigned reference signal are used to reduce the time delay effect for the proposed controller. The numerical simulation and experiment are used to show the effectiveness of the proposed controller design. It is shown by modifying the standard MRAC and the reference signal, the system error can be reduced from +110-20 degrees to +10-10 degrees.
{"title":"Model Reference Adaptive Control Design for CubeSat with Magnetorquer","authors":"A. T. Santoso, M. R. Rosa, Edwar","doi":"10.1109/ICCoSITE57641.2023.10127853","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127853","url":null,"abstract":"This paper proposes the Model Reference Adaptive Control (MRAC) design for the CubeSat 1U prototype with a magnetorquer to control the yaw angle. In practice, the system dynamics parameters of the CubeSat 1U, such as the moment inertia and mass, are unknown. To handle the uncertainties of the parameters, the authors propose MRAC to control the yaw angle of the CubeSat 1U. The controller is designed and deployed using MATLAB, which is connected via Bluetooth to the CubeSat 1U. In the experiment, the communication delay occurs and causes deteriorated output response of standard MRAC. The modified MRAC and redesigned reference signal are used to reduce the time delay effect for the proposed controller. The numerical simulation and experiment are used to show the effectiveness of the proposed controller design. It is shown by modifying the standard MRAC and the reference signal, the system error can be reduced from +110-20 degrees to +10-10 degrees.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"5 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":"130951397","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/ICCoSITE57641.2023.10127743
Muhammad Zulfikar Fauzi, R. Sarno, S. Hidayati
In this study, a sign language-to-speech system was developed to recognize and convert BISINDO's sign language into speech using a machine learning approach. The speech output will make it easier for the user to communicate with the other person and will make it easier for the other person to understand sign language and will improve the quality of communication. Using the dataset produced in this study and Mediapipe for feature extraction, the model accuracy was able to obtain a score of 98% using the Support Vector Machine method. However, the accuracy score of the model decreased drastically reaching 78% in trials conducted directly on users because the testing exceeded the system effective range. The results of the implementation of Sign Language-to-Speech succeeded in producing an output in form of audio speech without using an internet connection. The system was able to detect both dynamic and static gesture from the user in real-time.
{"title":"Recognition of Real-Time BISINDO Sign Language-to-Speech using Machine Learning Methods","authors":"Muhammad Zulfikar Fauzi, R. Sarno, S. Hidayati","doi":"10.1109/ICCoSITE57641.2023.10127743","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127743","url":null,"abstract":"In this study, a sign language-to-speech system was developed to recognize and convert BISINDO's sign language into speech using a machine learning approach. The speech output will make it easier for the user to communicate with the other person and will make it easier for the other person to understand sign language and will improve the quality of communication. Using the dataset produced in this study and Mediapipe for feature extraction, the model accuracy was able to obtain a score of 98% using the Support Vector Machine method. However, the accuracy score of the model decreased drastically reaching 78% in trials conducted directly on users because the testing exceeded the system effective range. The results of the implementation of Sign Language-to-Speech succeeded in producing an output in form of audio speech without using an internet connection. The system was able to detect both dynamic and static gesture from the user in real-time.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"3 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":"122316055","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/ICCoSITE57641.2023.10127799
A. Firmansyah, T. F. Kusumasari, E. N. Alam
Face recognition is one of the biometric-based authentication methods known for its reliability. In addition, face recognition is also currently very concerning, especially with the growing use and available technology. Many frameworks can be used for the face recognition process, one of which is DeepFace. DeepFace has many models and detectors that can be used for face recognition with an accuracy above 93%. However, the accuracy obtained needs to be tested, especially when faced with a dataset of Indonesian faces. This research will discuss the accuracy comparison of the Facenet model, Facenet512, from ArcFace, available in the DeepFace framework. From the comparison results, it is obtained that Facenet512 has a high value in accuracy calculation which is 0.974 or 97.4%, compared to Facenet, which has an accuracy of 0.921 or 92.1%, and ArcFace, which has an accuracy of 0.878 or 87.8%. The benefit of this research is to test how high the accuracy of the existing model in DeepFace is if tested with the Indonesian dataset. In this test, Facenet512 is the model that has the highest accuracy when compared to ArcFace and Facenet. This research is expected to help DeepFace users determine the best model to use and provide references to DeepFace developers for future development.
{"title":"Comparison of Face Recognition Accuracy of ArcFace, Facenet and Facenet512 Models on Deepface Framework","authors":"A. Firmansyah, T. F. Kusumasari, E. N. Alam","doi":"10.1109/ICCoSITE57641.2023.10127799","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127799","url":null,"abstract":"Face recognition is one of the biometric-based authentication methods known for its reliability. In addition, face recognition is also currently very concerning, especially with the growing use and available technology. Many frameworks can be used for the face recognition process, one of which is DeepFace. DeepFace has many models and detectors that can be used for face recognition with an accuracy above 93%. However, the accuracy obtained needs to be tested, especially when faced with a dataset of Indonesian faces. This research will discuss the accuracy comparison of the Facenet model, Facenet512, from ArcFace, available in the DeepFace framework. From the comparison results, it is obtained that Facenet512 has a high value in accuracy calculation which is 0.974 or 97.4%, compared to Facenet, which has an accuracy of 0.921 or 92.1%, and ArcFace, which has an accuracy of 0.878 or 87.8%. The benefit of this research is to test how high the accuracy of the existing model in DeepFace is if tested with the Indonesian dataset. In this test, Facenet512 is the model that has the highest accuracy when compared to ArcFace and Facenet. This research is expected to help DeepFace users determine the best model to use and provide references to DeepFace developers for future development.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"13 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":"116498131","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}
Despite the popularity of online dating Application, there are increasing security issues and challenges with them, such as the creation of fake accounts and phishing, which are commonly called romance scams, one of which is fake user data or even completely fake profiles. This research will discuss the profile verification technology that has been developed in several online dating applications to verify the authenticity of user profiles using an algorithm capable of detecting fake profiles. This study used Sequential Equation Modeling (SEM) method and SMART PLS as a statistical tool. A total of 561 data from online dating application users in Indonesia were collected in October 2022. The purpose of this study was to determine the impact of Attitude, Trust, and Subjective Norm to intention to use profile verification in Dating Application in Indonesia. Attitude, Trust, and Subjective Norms will be special variables that affect the user's intention to use Profile Verification on Dating Applications in Indonesia. The results of the study found that all research hypotheses had a significant effect on each variable relationship in the research model.
{"title":"Analysis of Attitude, Trust, and Subjective Norm Impact on Intention to Use Profile Verification in Dating Applications in Indonesia","authors":"Kenny Prasetyo, Xenia Dharmawan, Erwin Ardianto Halim, Marylise Hebrard","doi":"10.1109/ICCoSITE57641.2023.10127777","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127777","url":null,"abstract":"Despite the popularity of online dating Application, there are increasing security issues and challenges with them, such as the creation of fake accounts and phishing, which are commonly called romance scams, one of which is fake user data or even completely fake profiles. This research will discuss the profile verification technology that has been developed in several online dating applications to verify the authenticity of user profiles using an algorithm capable of detecting fake profiles. This study used Sequential Equation Modeling (SEM) method and SMART PLS as a statistical tool. A total of 561 data from online dating application users in Indonesia were collected in October 2022. The purpose of this study was to determine the impact of Attitude, Trust, and Subjective Norm to intention to use profile verification in Dating Application in Indonesia. Attitude, Trust, and Subjective Norms will be special variables that affect the user's intention to use Profile Verification on Dating Applications in Indonesia. The results of the study found that all research hypotheses had a significant effect on each variable relationship in the research model.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"63 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":"126367225","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/ICCoSITE57641.2023.10127835
Muhammad Yusuf Kardawi, R. Sarno
Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.
{"title":"Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning","authors":"Muhammad Yusuf Kardawi, R. Sarno","doi":"10.1109/ICCoSITE57641.2023.10127835","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127835","url":null,"abstract":"Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"174 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":"132766821","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/ICCoSITE57641.2023.10127765
Matthew Christopher Albert, Hubertus Hans, Herlangga Karteja, M. H. Widianto
Hydroponic farming is limited by inefficient monitoring and maintenance, which can affect plant growth and yield. This paper proposes using IoT technology, specifically a combination of STM32 microcontroller and sensors with 4G connection to cloud, to automate the monitoring and maintenance of hydroponic plants. The system monitors water and air temperature, pH, and TDS, and controls the hydroponics by adding nutrient in the form of AB mix. An automatic decision maker is built using KNN with an accuracy of 92.86% based on Euclidean distance algorithm. This technology could optimize the growth of hydroponic plants, as it provides continuous monitoring and maintenance.
{"title":"Development of Hydroponic IoT-based Monitoring System and Automatic Nutrition Control using KNN","authors":"Matthew Christopher Albert, Hubertus Hans, Herlangga Karteja, M. H. Widianto","doi":"10.1109/ICCoSITE57641.2023.10127765","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127765","url":null,"abstract":"Hydroponic farming is limited by inefficient monitoring and maintenance, which can affect plant growth and yield. This paper proposes using IoT technology, specifically a combination of STM32 microcontroller and sensors with 4G connection to cloud, to automate the monitoring and maintenance of hydroponic plants. The system monitors water and air temperature, pH, and TDS, and controls the hydroponics by adding nutrient in the form of AB mix. An automatic decision maker is built using KNN with an accuracy of 92.86% based on Euclidean distance algorithm. This technology could optimize the growth of hydroponic plants, as it provides continuous monitoring and maintenance.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"29 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":"127024018","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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