The bin packing problem is a problem where goods with different volumes and dimensions are put into a container so that the volume of goods inserted is maximized. The problem of multi-objective bin packing is a problem that is more commonly found in everyday life, because what is considered in packing is usually not only volume.In this research, a multi-objective genetic algorithm is proposed to solve the multi-objective bin packing problem. The proposed genetic algorithm uses non-dominated sorting and crowding distance methods to get the best solution for each objective and to avoid bias. The algorithm is then tested with several test classes that represent different combinations of item and container sizes.From the results of the tests carried out, it was found that the proposed algorithm can find several solutions which are the best candidate solutions for each objective. Also found how the correlation of each objective in the population.
{"title":"Fast Non-dominated Sorting in Multi Objective Genetic Algorithm for Bin Packing Problem","authors":"Muhammad Bintang Bahy, Aina Musdholifah","doi":"10.22146/ijccs.70677","DOIUrl":"https://doi.org/10.22146/ijccs.70677","url":null,"abstract":"The bin packing problem is a problem where goods with different volumes and dimensions are put into a container so that the volume of goods inserted is maximized. The problem of multi-objective bin packing is a problem that is more commonly found in everyday life, because what is considered in packing is usually not only volume.In this research, a multi-objective genetic algorithm is proposed to solve the multi-objective bin packing problem. The proposed genetic algorithm uses non-dominated sorting and crowding distance methods to get the best solution for each objective and to avoid bias. The algorithm is then tested with several test classes that represent different combinations of item and container sizes.From the results of the tests carried out, it was found that the proposed algorithm can find several solutions which are the best candidate solutions for each objective. Also found how the correlation of each objective in the population.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42414933","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 development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.
{"title":"The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning)","authors":"Sensa G. S. Syahra, Yunita Sari, Y. Suyanto","doi":"10.22146/ijccs.69906","DOIUrl":"https://doi.org/10.22146/ijccs.69906","url":null,"abstract":"The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45030260","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 Lampung script is often used in writing words in Lampung language. The Lampung language itself is used by native Lampung people and people who learn Lampung language. The Lampung script is difficult to learn because there are many combinations of parent characters and subletters. CNN is a method in the field of object recognition that has a specific layer, namely a convolution layer and a pooling layer that allows the feature learning process well. Handwriting recognition as in character recognition in MNIST, CNN produces better performance compared to other methods. From the advantages of CNN, the CNN method with DenseNet architecture was chosen as the best architecture to recognize each Lampung script. In this study, there are 2 main processes, namely preprocessing, and recognition. This study succeeded in applying the CNN method which can recognize Lampung script. The dataset is divided into 4 groups of characters that have different sounds. First, the parent character data get 98% accuracy. Second, the parent letter data with the above letters get 98% accuracy. Third, the parent character data with the sub-letters on the side get 98% accuracy. Fourth, the parent letter data with the lower letters get 97% accuracy.
{"title":"Lampung Script Recognition Using Convolutional Neural Network","authors":"Panji Bintoro, A. Harjoko","doi":"10.22146/ijccs.70041","DOIUrl":"https://doi.org/10.22146/ijccs.70041","url":null,"abstract":"The Lampung script is often used in writing words in Lampung language. The Lampung language itself is used by native Lampung people and people who learn Lampung language. The Lampung script is difficult to learn because there are many combinations of parent characters and subletters. CNN is a method in the field of object recognition that has a specific layer, namely a convolution layer and a pooling layer that allows the feature learning process well. Handwriting recognition as in character recognition in MNIST, CNN produces better performance compared to other methods. From the advantages of CNN, the CNN method with DenseNet architecture was chosen as the best architecture to recognize each Lampung script. In this study, there are 2 main processes, namely preprocessing, and recognition. This study succeeded in applying the CNN method which can recognize Lampung script. The dataset is divided into 4 groups of characters that have different sounds. First, the parent character data get 98% accuracy. Second, the parent letter data with the above letters get 98% accuracy. Third, the parent character data with the sub-letters on the side get 98% accuracy. Fourth, the parent letter data with the lower letters get 97% accuracy.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46102285","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}
There are many agencies that have databases but are left without proper management. For example, in tourist attractions in Kota & Kab. Tegal, along with the rapid development of tourism technology, the tourism industry requires the tourism industry to apply information technology to provide convenience for tourists to find out tourist areas according to the cost, and the distance of the tourist attractions entered. The provision of tourism information helps tourists to consider and make decisions to travel. Tahani Fuzzy Logic was chosen because the concept of Fuzzy logic is easy to understand, flexible, and because the Tahani logic method is a form of decision support where the main tool is functional with the main input criteria determined by the user/tourist. This system was implemented using web programming and MySQL database, where the variables to be considered are Type of Tour, Number of Facilities, Price of Tour Tickets, Number of Tourist Visitors, Travel Distance from City Center. The results of this study are a decision support system for tourism selection in Tegal using Fuzzy Tahani which can recommend tourist attractions in Tegal which are determined by tourists depending on tourist criteria based on the firestrength of the selected variables
{"title":"Tegal Tourism Object Selection Decision Support System Using Fuzzy Logic","authors":"Dika Permana Putra, Sigit Priyanta","doi":"10.22146/ijccs.70226","DOIUrl":"https://doi.org/10.22146/ijccs.70226","url":null,"abstract":"There are many agencies that have databases but are left without proper management. For example, in tourist attractions in Kota & Kab. Tegal, along with the rapid development of tourism technology, the tourism industry requires the tourism industry to apply information technology to provide convenience for tourists to find out tourist areas according to the cost, and the distance of the tourist attractions entered. The provision of tourism information helps tourists to consider and make decisions to travel. Tahani Fuzzy Logic was chosen because the concept of Fuzzy logic is easy to understand, flexible, and because the Tahani logic method is a form of decision support where the main tool is functional with the main input criteria determined by the user/tourist. This system was implemented using web programming and MySQL database, where the variables to be considered are Type of Tour, Number of Facilities, Price of Tour Tickets, Number of Tourist Visitors, Travel Distance from City Center. The results of this study are a decision support system for tourism selection in Tegal using Fuzzy Tahani which can recommend tourist attractions in Tegal which are determined by tourists depending on tourist criteria based on the firestrength of the selected variables","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47731787","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}
Ventilator shortages is a common problem faced by hospitals during the COVID-19 pandemic era. Healthcare workers are forced to make choices because of how big the difference between resources and lives needing it. This issue rarely comes, because normally every patient has the same rights to receive treatment and resources, but it becomes a clear problem when there are barely enough resources. Therefore, a prioritization mechanism that can objectively decide the allocation must be made to achieve the best outcome.A decision support system is a system that can support humans using data as decision makers to help them decide semi-structured/unstructured problems. The goal of this research is to create a DSS to prioritize patients who need a ventilator by incorporating two different methods, which are AHP, Interpolation, and SAW. It is hoped that the result of the research can be used to rank patients based on predetermined criterias and policy.
{"title":"Decision Support System to Prioritize Ventilators for COVID-19 Patients using AHP, Interpolation, and SAW","authors":"Nikolas Adhi Prasetyo, Retantyo Wardoyo","doi":"10.22146/ijccs.70985","DOIUrl":"https://doi.org/10.22146/ijccs.70985","url":null,"abstract":"Ventilator shortages is a common problem faced by hospitals during the COVID-19 pandemic era. Healthcare workers are forced to make choices because of how big the difference between resources and lives needing it. This issue rarely comes, because normally every patient has the same rights to receive treatment and resources, but it becomes a clear problem when there are barely enough resources. Therefore, a prioritization mechanism that can objectively decide the allocation must be made to achieve the best outcome.A decision support system is a system that can support humans using data as decision makers to help them decide semi-structured/unstructured problems. The goal of this research is to create a DSS to prioritize patients who need a ventilator by incorporating two different methods, which are AHP, Interpolation, and SAW. It is hoped that the result of the research can be used to rank patients based on predetermined criterias and policy.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45255364","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}
Eka Karyawati, Prasetyo Adi Utomo, Gede Arta Wibawa
SARA is a sensitive issue based on sentiments about self-identity regarding ancestry, religion, nationality or ethnicity. The impact of the issue of SARA is conflict between groups that leads to hatred and division. SARA issues are widely spread through social media, especially Twitter. To overcome the problem of SARA, it is necessary to develop an effective method to filter negative SARA. This study aims to analyze Indonesian-language tweets and determine whether the tweet contains positive or negative SARA or does not contain SARA (neutral). Machine learning (i.e., SVM) and lexicon-based method (i.e., LIWC) were compared based on 450 tweet data to determine the best approach for each sentiment (positive, negative, and neutral). The best evaluation results are shown in the negative SARA classification using SVM with λ = 3 and γ = 0.1, where Precision = 0.9, Recall = 0.6, and F1-Score = 0.72. The best results from the positive SARA classification were shown in the LIWC method, where Precision = 0.6, Recall = 0.8, and F1-Score = 0.69. The best evaluation results for neutral classification are shown in SVM with λ = 3 and γ = 0.1, with Precision = 0.52, Recall = 0.87, and F1-Score = 0.65.
{"title":"Comparison of SVM and LIWC for Sentiment Analysis of SARA","authors":"Eka Karyawati, Prasetyo Adi Utomo, Gede Arta Wibawa","doi":"10.22146/ijccs.69617","DOIUrl":"https://doi.org/10.22146/ijccs.69617","url":null,"abstract":"SARA is a sensitive issue based on sentiments about self-identity regarding ancestry, religion, nationality or ethnicity. The impact of the issue of SARA is conflict between groups that leads to hatred and division. SARA issues are widely spread through social media, especially Twitter. To overcome the problem of SARA, it is necessary to develop an effective method to filter negative SARA. This study aims to analyze Indonesian-language tweets and determine whether the tweet contains positive or negative SARA or does not contain SARA (neutral). Machine learning (i.e., SVM) and lexicon-based method (i.e., LIWC) were compared based on 450 tweet data to determine the best approach for each sentiment (positive, negative, and neutral). The best evaluation results are shown in the negative SARA classification using SVM with λ = 3 and γ = 0.1, where Precision = 0.9, Recall = 0.6, and F1-Score = 0.72. The best results from the positive SARA classification were shown in the LIWC method, where Precision = 0.6, Recall = 0.8, and F1-Score = 0.69. The best evaluation results for neutral classification are shown in SVM with λ = 3 and γ = 0.1, with Precision = 0.52, Recall = 0.87, and F1-Score = 0.65.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49009553","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 Rabin-Karp algorithm is used to detect similarity using hashing techniques, from related studies modifications have been made in the hashing process but in previous studies have not been conducted research for the best k value in the K-Gram process. At the stage of stemming the Nazief & Adriani algorithm is used to transform the words into basic words. The researcher uses several variations of K-Gram values to determine the best K-Gram values. The analysis was performed using Ukara Enhanced public data obtained from the Kaggle with a total of 12215 data. The student essay answers data totaled to 258 data in the group A and 305 in the group B, every student essay answers data in each group will be compared with the answers of other fellow group member. Research results are the value of k = 3 has the best performance which has the highest some interpretations of 1-14% (Little degree of similarity) and 15-50% (Medium level of similarity) compared to values of k = 5, 7, and 9 which have the highest number of interpretation results 0%-0.99% (Document is different). However, if the students essay answers compared have 100% (Exactly the same) interpretations, the k value on K-Gram does not affect the results.
{"title":"Selection of the Best K-Gram Value on Modified Rabin-Karp Algorithm","authors":"Wahyu Hidayat, Ema Utami, A. Sunyoto","doi":"10.22146/ijccs.63686","DOIUrl":"https://doi.org/10.22146/ijccs.63686","url":null,"abstract":"The Rabin-Karp algorithm is used to detect similarity using hashing techniques, from related studies modifications have been made in the hashing process but in previous studies have not been conducted research for the best k value in the K-Gram process. At the stage of stemming the Nazief & Adriani algorithm is used to transform the words into basic words. The researcher uses several variations of K-Gram values to determine the best K-Gram values. The analysis was performed using Ukara Enhanced public data obtained from the Kaggle with a total of 12215 data. The student essay answers data totaled to 258 data in the group A and 305 in the group B, every student essay answers data in each group will be compared with the answers of other fellow group member. Research results are the value of k = 3 has the best performance which has the highest some interpretations of 1-14% (Little degree of similarity) and 15-50% (Medium level of similarity) compared to values of k = 5, 7, and 9 which have the highest number of interpretation results 0%-0.99% (Document is different). However, if the students essay answers compared have 100% (Exactly the same) interpretations, the k value on K-Gram does not affect the results.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48752118","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 process of selecting from many alternatives to the criteria is a decision that is often determined in decision making. The criteria for which criteria can consist of many attributes are used by decision-makers in making the selection or are called multi-criteria decision making (MADM ). Determining the Artist Music Festival at an event has quite a complicated difficulty, because the assessment of the criteria is heterogeneous. The spider web's approach to integrating criteria results in the selection of the artist form that attracts the most attention, and public interest. Model AHP use of multi-attributes is used in selecting artists to perform at music festivals, selecting artists using criteria, namely Number of Followers (C1), stamp C2, Average Popular Tracks (C3), Average Youtube Viewers (C4), and the price of the artist (C5). Data on the number of followers, popularity, average popular tracks, and average YouTube viewers were obtained using the Spotify and Youtube APIs. The settlement method applied is the Analytical Hierarchy Process (AHP) and the Rating Scale algorithm, with an alternative using five samples of Indonesian artists. The research results are expected to provide recommendations for artists as performers in the music festival.
{"title":"Modification Weight Criteria With Webbed Model For Selection Artist Music Festival Using Analytical Hierarchy Process (AHP)","authors":"Gede Iwan Sudipa, P. Sugiartawan, I. A. Wiguna","doi":"10.22146/ijccs.72434","DOIUrl":"https://doi.org/10.22146/ijccs.72434","url":null,"abstract":"The process of selecting from many alternatives to the criteria is a decision that is often determined in decision making. The criteria for which criteria can consist of many attributes are used by decision-makers in making the selection or are called multi-criteria decision making (MADM ). Determining the Artist Music Festival at an event has quite a complicated difficulty, because the assessment of the criteria is heterogeneous. The spider web's approach to integrating criteria results in the selection of the artist form that attracts the most attention, and public interest. Model AHP use of multi-attributes is used in selecting artists to perform at music festivals, selecting artists using criteria, namely Number of Followers (C1), stamp C2, Average Popular Tracks (C3), Average Youtube Viewers (C4), and the price of the artist (C5). Data on the number of followers, popularity, average popular tracks, and average YouTube viewers were obtained using the Spotify and Youtube APIs. The settlement method applied is the Analytical Hierarchy Process (AHP) and the Rating Scale algorithm, with an alternative using five samples of Indonesian artists. The research results are expected to provide recommendations for artists as performers in the music festival.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48487997","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}
COVID-19 prevention procedures are executed to support public services and business continuity in a pandemic situation. Manual mask use monitoring is not efficient as it requires resources to monitor people at all times. Therefore, this task can be supported by automated surveillance systems based on Deep Learning. We performed mask detection and face recognition for a real-environment dataset. YOLOV3 as a one-stage detector was implemented to simultaneously generate a bounding box of the face area and class prediction. In face recognition, we compared the performance of three pre-trained models, namely ResNet152V2, InceptionV3, and Xception. The mask detection showed promising results with MAP=0.8960 on training and MAP=0.8957 on validation. We chose the Xception model for face recognition because it has equal quality as ResNet152V2 but has fewer parameters. Xception achieved a minimal loss value in the validation of 0.09157 with perfect accuracy on facial images larger than 100 pixels. Overall the system delivers promising results and can identify faces, even those behind the mask.
{"title":"Behind the Mask: Detection and Recognition Based-on Deep Learning","authors":"Ade Nurhopipah, Irfan Rifai Azziz, Jali Suhaman","doi":"10.22146/ijccs.72075","DOIUrl":"https://doi.org/10.22146/ijccs.72075","url":null,"abstract":"COVID-19 prevention procedures are executed to support public services and business continuity in a pandemic situation. Manual mask use monitoring is not efficient as it requires resources to monitor people at all times. Therefore, this task can be supported by automated surveillance systems based on Deep Learning. We performed mask detection and face recognition for a real-environment dataset. YOLOV3 as a one-stage detector was implemented to simultaneously generate a bounding box of the face area and class prediction. In face recognition, we compared the performance of three pre-trained models, namely ResNet152V2, InceptionV3, and Xception. The mask detection showed promising results with MAP=0.8960 on training and MAP=0.8957 on validation. We chose the Xception model for face recognition because it has equal quality as ResNet152V2 but has fewer parameters. Xception achieved a minimal loss value in the validation of 0.09157 with perfect accuracy on facial images larger than 100 pixels. Overall the system delivers promising results and can identify faces, even those behind the mask.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42897875","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 accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value.
{"title":"Face Image Generation and Enhancement Using Conditional Generative Adversarial Network","authors":"Ainil Mardiah, Sri Hartati, Agus Sihabuddin","doi":"10.22146/ijccs.58327","DOIUrl":"https://doi.org/10.22146/ijccs.58327","url":null,"abstract":"The accuracy and speed of a single image super-resolution using a convolutional neural network is often a problem in improving finer texture details when using large enhancement factors. Some recent studies have focused on minimal mean square error, resulting in a high peak signal to noise ratio. Generally, although the peak signal to noise ratio has a high value, the output image is less detailed. This shows that the determination of super-resolution is not optimal. Conditional Generative Adversarial Network based on Boundary Equilibrium Generative Adversarial Network, by combining Mean Square Error Loss and GAN Loss as a loss function to optimize the super-resolution model and produce super-resolution images. Also, the generator network is designed with skip connection architecture to increase convergence speed and strengthen feature distribution. Image quality value parameters used in this study are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The results showed the highest image quality values using dataset validation were 26.55 for PSNR values and 0.93 for SSIM values. The highest image quality values using the testing dataset are 24.56 for the PSNR value and 0.91 for the SSIM value.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49197833","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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