The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.
{"title":"Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language","authors":"Ruben Cornelius Siagian","doi":"10.22146/ijccs.82781","DOIUrl":"https://doi.org/10.22146/ijccs.82781","url":null,"abstract":" The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48801716","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}
Anita Desiani, Yuli Andriani, Irmeilyana Irmeilyana, Rifkie Primartha, M. Arhami, Dwi Fitrianti, Henny Nur Syafitri
One of the datasets used to predict heart disease is UCI dataset. unfortunately, the dataset contains missing data. the missing data dramatically affects the performance of the backpropagation classification method. One of the techniques used to handle missing data is feature selection. This study compares the ReliefF and the C4.5 algorithm in feature selection to handle missing data. The results of these algorithms are applied to the classification of heart disease using the Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are an accuracy of 82.653%, a precision of 82.7%, and a recall of 82.7%. The performance results of the C4.5 and backpropagation are an accuracy of 80.61%, a precision of 80.4%, and a recall of 80.6%. Based on the results it can be concluded that the ReliefF gives better performance results on backpropagation than the performance results of the C4.5. Although, the results of C4.5 are below ReliefF but the results are quite satisfactory because of the accuracy, precision and recall results obtained above 80%. This shows that ReliefF and C4.5 can select features that affect the UCI heart disease patient dataset.
{"title":"The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation","authors":"Anita Desiani, Yuli Andriani, Irmeilyana Irmeilyana, Rifkie Primartha, M. Arhami, Dwi Fitrianti, Henny Nur Syafitri","doi":"10.22146/ijccs.82948","DOIUrl":"https://doi.org/10.22146/ijccs.82948","url":null,"abstract":"One of the datasets used to predict heart disease is UCI dataset. unfortunately, the dataset contains missing data. the missing data dramatically affects the performance of the backpropagation classification method. One of the techniques used to handle missing data is feature selection. This study compares the ReliefF and the C4.5 algorithm in feature selection to handle missing data. The results of these algorithms are applied to the classification of heart disease using the Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are an accuracy of 82.653%, a precision of 82.7%, and a recall of 82.7%. The performance results of the C4.5 and backpropagation are an accuracy of 80.61%, a precision of 80.4%, and a recall of 80.6%. Based on the results it can be concluded that the ReliefF gives better performance results on backpropagation than the performance results of the C4.5. Although, the results of C4.5 are below ReliefF but the results are quite satisfactory because of the accuracy, precision and recall results obtained above 80%. This shows that ReliefF and C4.5 can select features that affect the UCI heart disease patient dataset.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42321577","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}
Mutammimul Ula, Gita Perdinanta, R. Hidayat, Ilham Sahputra
PT. Perkebunan Nusantara 1 is a company that works in palm oil mills with a total land area of 1,144 Ha in Aceh Utara. This research aimed to determine the cluster of the productive palm oil production's target. The expected results of palm oil production are for the following year so that it can be used as a recommendation for the managers to maximize performance. Research data are taken from PTPTN 1 PKS Cot Girek consisting of plantation and oil palm production data. The results of PKS Cot Girek palm oil production data for 2019-2022 from January to December were 1,365,530, while in 2022, it reached 1,768,720. The overall value obtained is 4,431,180 production data. The results of a land area of 1,144 Ha got 800.4 Ha of productive land and 343.6 Ha of less effective land. The test result in the first iteration of the C-Means process is 1.87, the second iteration is 3.87, the first iteration of the K-Means is 2.27, and the seventh iteration is 4.165 with an accuracy of 0.46 and 0.295. Meanwhile, the prediction model results have an accuracy rate of 90.77%. As a comparison, the fuzzy time series' accuracy level is 81.27%.
{"title":"Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara","authors":"Mutammimul Ula, Gita Perdinanta, R. Hidayat, Ilham Sahputra","doi":"10.22146/ijccs.83195","DOIUrl":"https://doi.org/10.22146/ijccs.83195","url":null,"abstract":"PT. Perkebunan Nusantara 1 is a company that works in palm oil mills with a total land area of 1,144 Ha in Aceh Utara. This research aimed to determine the cluster of the productive palm oil production's target. The expected results of palm oil production are for the following year so that it can be used as a recommendation for the managers to maximize performance. Research data are taken from PTPTN 1 PKS Cot Girek consisting of plantation and oil palm production data. The results of PKS Cot Girek palm oil production data for 2019-2022 from January to December were 1,365,530, while in 2022, it reached 1,768,720. The overall value obtained is 4,431,180 production data. The results of a land area of 1,144 Ha got 800.4 Ha of productive land and 343.6 Ha of less effective land. The test result in the first iteration of the C-Means process is 1.87, the second iteration is 3.87, the first iteration of the K-Means is 2.27, and the seventh iteration is 4.165 with an accuracy of 0.46 and 0.295. Meanwhile, the prediction model results have an accuracy rate of 90.77%. As a comparison, the fuzzy time series' accuracy level is 81.27%.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46357062","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}
The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.
{"title":"Siamese-Network Based Signature Verification using Self Supervised Learning","authors":"Muhammad Fawwaz Mayda, Aina Musdholifah","doi":"10.22146/ijccs.74627","DOIUrl":"https://doi.org/10.22146/ijccs.74627","url":null,"abstract":"The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48817857","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}
Klara Bonita Madao, I. Gusti, Ayu Ngurah, Kade Sukiastini, Engelina Prisca Kalensun, Kata kunci — Kehadiran, Monte Simulasi, Prediksi carlo
In lectures, attendance is one of the assessment points that plays an important role in determining a student's graduation. The attendance prediction simulation is an estimate of the calculation of student attendance in lectures. This type of research is quantitative research using data collection techniques by means of observation and documentation study. In the process of analysis, the observed data were attendance data of 5th semester computer engineering study program students and a sample of 40 people as research subjects. The stages of the monte carlo simulation are used: Determining variable frequency; Calculating cumulative probabilities; Determine random number intervals; Create a simulation to determine student attendance; Generate random numbers; Make a simulation of the experimental circuit. Based on a series of experimental data that has been The simulation results obtained predicted attendance and absence of computer engineering study program students at the STMIK Agamua Wamena campus from November 7 to December 19, 2022 with an average attendance of above 50%. Keywords— Attendance, Simulation, Monte carlo, Prediction
{"title":"SIMULATION TECHNIQUE IN DETERMINING STUDENT ATTENDANCE USING THE MONTE CARLO METHOD","authors":"Klara Bonita Madao, I. Gusti, Ayu Ngurah, Kade Sukiastini, Engelina Prisca Kalensun, Kata kunci — Kehadiran, Monte Simulasi, Prediksi carlo","doi":"10.22146/ijccs.83891","DOIUrl":"https://doi.org/10.22146/ijccs.83891","url":null,"abstract":"In lectures, attendance is one of the assessment points that plays an important role in determining a student's graduation. The attendance prediction simulation is an estimate of the calculation of student attendance in lectures. This type of research is quantitative research using data collection techniques by means of observation and documentation study. In the process of analysis, the observed data were attendance data of 5th semester computer engineering study program students and a sample of 40 people as research subjects. The stages of the monte carlo simulation are used: Determining variable frequency; Calculating cumulative probabilities; Determine random number intervals; Create a simulation to determine student attendance; Generate random numbers; Make a simulation of the experimental circuit. Based on a series of experimental data that has been The simulation results obtained predicted attendance and absence of computer engineering study program students at the STMIK Agamua Wamena campus from November 7 to December 19, 2022 with an average attendance of above 50%. Keywords— Attendance, Simulation, Monte carlo, Prediction","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43643965","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}
A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.
{"title":"Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation","authors":"Wawan Gunawan, N. Latifah","doi":"10.22146/ijccs.81521","DOIUrl":"https://doi.org/10.22146/ijccs.81521","url":null,"abstract":"A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193531","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}
Tight business competition demands business actors to make responsive, timely decisions to survive the uncertainty. Food business, especially cafes, has emerged as one of the most popular business types recently. One cafe concept that draws most customers' interest is modern concepts, friendly service, and affordable prices. Finn Coffee is one of the cafes providing a range of foods and beverages, especially coffee-based beverages. Customer satisfaction defines one's feelings when comparing performance. It denotes customer's responses to their satisfied needs. The term satisfaction itself is described as one's happy expression after receiving a quality product with affordable price and satisfying quality. The present study aimed to analyze cafe customer satisfaction using the C4.5 algorithm with predetermined criteria. Customer satisfaction was classified using C4.5. The algorithm displays the level of customer satisfaction based on the customers' response to the Google form distributed by the cafe employees/owner.
{"title":"Applying Data Mining to Classify Customer Satisfaction using C4.5 Algorithm Decision Tree","authors":"J. Prayoga, Zelvi Gustiana, Sabrina Aulia Rahmah","doi":"10.22146/ijccs.83535","DOIUrl":"https://doi.org/10.22146/ijccs.83535","url":null,"abstract":"Tight business competition demands business actors to make responsive, timely decisions to survive the uncertainty. Food business, especially cafes, has emerged as one of the most popular business types recently. One cafe concept that draws most customers' interest is modern concepts, friendly service, and affordable prices. Finn Coffee is one of the cafes providing a range of foods and beverages, especially coffee-based beverages. Customer satisfaction defines one's feelings when comparing performance. It denotes customer's responses to their satisfied needs. The term satisfaction itself is described as one's happy expression after receiving a quality product with affordable price and satisfying quality. The present study aimed to analyze cafe customer satisfaction using the C4.5 algorithm with predetermined criteria. Customer satisfaction was classified using C4.5. The algorithm displays the level of customer satisfaction based on the customers' response to the Google form distributed by the cafe employees/owner.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46783353","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}
Ariana Yunita, Sara Florensia Telaumbanua, A. Irawan
The amount of unstructured data is increasing annually, which is promising forgaining insights. Twitter, a platform producing unstructured data, is currently one of the mostpopular media platforms used for conducting research on a topic's trend. This study attempts toanalyze the topic of New and Renewable Energy (NRE) in Indonesia. The purpose of this studyis to gain insights into the NRE topic trend over the last ten years by modeling the topicsdiscussed on Twitter and examining the location distribution of users who post tweets about thetopic. Accordingly, this study employed descriptive analysis, geocoding analysis, and topicmodeling. The results of descriptive analysis show that the development of NRE has acceleratedin recent years, particularly in 2021. Geocoding analysis reveals that the distribution of peoplewho engage in NRE posting activities is dominated by DKI Jakarta province. Topic modelingyielding two topics that were discussed the most by Indonesians over a 10-year period. The twotopics are related to government policies that support the development of NRE and electricity,which is Indonesia's focus in NRE. This study highlights the importance of analyzing theTweetology of NRE.
{"title":"The Tweetology of New and Renewable Energy in Indonesia","authors":"Ariana Yunita, Sara Florensia Telaumbanua, A. Irawan","doi":"10.22146/ijccs.81397","DOIUrl":"https://doi.org/10.22146/ijccs.81397","url":null,"abstract":"The amount of unstructured data is increasing annually, which is promising forgaining insights. Twitter, a platform producing unstructured data, is currently one of the mostpopular media platforms used for conducting research on a topic's trend. This study attempts toanalyze the topic of New and Renewable Energy (NRE) in Indonesia. The purpose of this studyis to gain insights into the NRE topic trend over the last ten years by modeling the topicsdiscussed on Twitter and examining the location distribution of users who post tweets about thetopic. Accordingly, this study employed descriptive analysis, geocoding analysis, and topicmodeling. The results of descriptive analysis show that the development of NRE has acceleratedin recent years, particularly in 2021. Geocoding analysis reveals that the distribution of peoplewho engage in NRE posting activities is dominated by DKI Jakarta province. Topic modelingyielding two topics that were discussed the most by Indonesians over a 10-year period. The twotopics are related to government policies that support the development of NRE and electricity,which is Indonesia's focus in NRE. This study highlights the importance of analyzing theTweetology of NRE.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48859467","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}
Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction.
{"title":"World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model","authors":"Stanislaus Jiwandana Pinasthika, D. Fudholi","doi":"10.22146/ijccs.82280","DOIUrl":"https://doi.org/10.22146/ijccs.82280","url":null,"abstract":"Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41370933","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}
The article discusses how impact actions, such as conflict and warfare, can negatively impact the structural integrity of concrete structures and how detecting hidden defects in concrete structures is difficult without expert knowledge. The paper presents a new technique that combines thermal imaging and artificial intelligence to detect hidden defects in concrete structures. The authors trained an AI model on simulated data and achieved a validation accuracy of 99.93%. They then conducted a laboratory experiment to create a dataset of concrete blocks with and without subsurface cracks and trained a new model, which achieved a validation accuracy of 100%. The article concludes that AI can detect hidden defects and subsurface cracks in concrete structures by classifying thermal images of concrete surfaces.
{"title":"Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network","authors":"Mabrouka Abuhmida","doi":"10.22146/ijccs.82912","DOIUrl":"https://doi.org/10.22146/ijccs.82912","url":null,"abstract":"The article discusses how impact actions, such as conflict and warfare, can negatively impact the structural integrity of concrete structures and how detecting hidden defects in concrete structures is difficult without expert knowledge. The paper presents a new technique that combines thermal imaging and artificial intelligence to detect hidden defects in concrete structures. The authors trained an AI model on simulated data and achieved a validation accuracy of 99.93%. They then conducted a laboratory experiment to create a dataset of concrete blocks with and without subsurface cracks and trained a new model, which achieved a validation accuracy of 100%. The article concludes that AI can detect hidden defects and subsurface cracks in concrete structures by classifying thermal images of concrete surfaces.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49008992","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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