Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127820
Al-Khowarizmi, S. Efendi, M. K. Nasution, Mawengkang Herman
Prediction is included in the data mining process to predict future data based on learning from past data. Various techniques are used in making predictions. The Regression method also includes techniques for making predictions. Various regressions such as Linear Regression, Ridge Regression, Lasso Regression, and Multivariate Adaptive Regression Splines (MARS) are regression techniques that are fond of being used in predicting data in business. Every prediction is always measured success with several formulations. As MAPE is a measuring tool in obtaining accuracy, so it is trying to be designed with the role of Detection Rate (DR) in order to get a smaller error value in obtaining accuracy. In this paper, the process of obtaining accuracy in Linear Regression is carried out to obtain a MAPE of 0.15874361801345002 % and the role of DR in MAPE is 0.1410249900632677 %. At Ridge Regression get a MAPE of 0.15820461185453846 % and the role of DR in MAPE is 0.14077739389387 %. On Lasso Regression get a MAPE of 0.14793925681569248 % and the role of DR in MAPE is 0.1370143839961479 %. On MARS get a MAPE of 0.16209808399129746 % and the role of DR in MAPE is 0.14528079908718253 %.
{"title":"The Role of Detection Rate in MAPE to Improve Measurement Accuracy for Predicting FinTech Data in Various Regressions","authors":"Al-Khowarizmi, S. Efendi, M. K. Nasution, Mawengkang Herman","doi":"10.1109/ICCoSITE57641.2023.10127820","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127820","url":null,"abstract":"Prediction is included in the data mining process to predict future data based on learning from past data. Various techniques are used in making predictions. The Regression method also includes techniques for making predictions. Various regressions such as Linear Regression, Ridge Regression, Lasso Regression, and Multivariate Adaptive Regression Splines (MARS) are regression techniques that are fond of being used in predicting data in business. Every prediction is always measured success with several formulations. As MAPE is a measuring tool in obtaining accuracy, so it is trying to be designed with the role of Detection Rate (DR) in order to get a smaller error value in obtaining accuracy. In this paper, the process of obtaining accuracy in Linear Regression is carried out to obtain a MAPE of 0.15874361801345002 % and the role of DR in MAPE is 0.1410249900632677 %. At Ridge Regression get a MAPE of 0.15820461185453846 % and the role of DR in MAPE is 0.14077739389387 %. On Lasso Regression get a MAPE of 0.14793925681569248 % and the role of DR in MAPE is 0.1370143839961479 %. On MARS get a MAPE of 0.16209808399129746 % and the role of DR in MAPE is 0.14528079908718253 %.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"90 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":"115868299","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/ICCoSITE57641.2023.10127803
Muhammad Ibadurrahman Arrasyid Supriyanto, R. Sarno, C. Fatichah, Aziz Fajar
Higher image reconstruction with excellent structural detail allows experts to perform accurate analysis, especially on the smallest organ details. The interpolation method that approaches the problem of medical image reconstruction, especially 3D, still causes serious problems. The medical image produced by the interpolation method produces blurred or smooth lines on some parts of the organ. This can cause errors in the medical analysis that will be carried out if the reconstruction results are problematic. For this reason, a method is needed that can reconstruct images well without producing blur but does not require very large computer resources. This study aims to evaluate and compare the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures in the DICOM data format. This study evaluates and compares the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures. The test scenario was performed using images from the ADNI dataset and comparing the output results using a variational autoencoder and a multi-level densely connected super-resolution network on 3D data with existing interpolation methods. The evaluation was done using two metrics, i.e., SSIM and PSNR. The results showed that the variational autoencoder method has the highest SSIM and PSNR values, indicating it has the highest image quality among the three methods, while the mDCSRN method has the lowest SSIM and PSNR values, meaning it has the lowest image quality.
{"title":"A Comparison Between Interpolation Method and Neural Network Approach in 3D Digital Imaging and Communications in Medicine","authors":"Muhammad Ibadurrahman Arrasyid Supriyanto, R. Sarno, C. Fatichah, Aziz Fajar","doi":"10.1109/ICCoSITE57641.2023.10127803","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127803","url":null,"abstract":"Higher image reconstruction with excellent structural detail allows experts to perform accurate analysis, especially on the smallest organ details. The interpolation method that approaches the problem of medical image reconstruction, especially 3D, still causes serious problems. The medical image produced by the interpolation method produces blurred or smooth lines on some parts of the organ. This can cause errors in the medical analysis that will be carried out if the reconstruction results are problematic. For this reason, a method is needed that can reconstruct images well without producing blur but does not require very large computer resources. This study aims to evaluate and compare the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures in the DICOM data format. This study evaluates and compares the quality of 3D magnetic resonance imaging medical images reconstructed using interpolation methods and artificial neural network architectures. The test scenario was performed using images from the ADNI dataset and comparing the output results using a variational autoencoder and a multi-level densely connected super-resolution network on 3D data with existing interpolation methods. The evaluation was done using two metrics, i.e., SSIM and PSNR. The results showed that the variational autoencoder method has the highest SSIM and PSNR values, indicating it has the highest image quality among the three methods, while the mDCSRN method has the lowest SSIM and PSNR values, meaning it has the lowest image quality.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"27 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":"116744136","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}
Citrus fruit is a fruit that has good vitamins and is popular with the public. This fruit also has various types with different benefits. Each type of orange also has a variety of colors. Types of oranges can be checked manually by looking directly at the color and texture of the fruit. This manual method is very simple but also very subjective because of the different understanding of each person about the types of oranges. Therefore, this research discusses and explains how to determine the type of fruit by comparing several methods, namely using the SVM method (Support Vector Machine), the CNN method (Convolutional Neural Network), the K-NN method (K-Nearest Neighbor), and the Naïve Bayes method by taking several samples of citrus fruit images consisting of sweet oranges, lemons and limes using a mobile phone camera. The total dataset used in this study is 90 datasets consisting of 30 sweet orange images, 30 lime images and 30 lemon images. Of the 90 datasets are divided into training data and test data. From the results of the study, it was obtained that the accuracy of compatibility with a percentage of 100% using the CNN method (Convolutional Neural Network).
{"title":"Classification of Orange Fruit Using Convolutional Neural Network, Support Vector Machine, K-Nearest Neighbor and Naive Bayes Methods Based on Color Analysis","authors":"Widhi Ersa Pratiwi, Mhd Arief Hasan, Gusyella Mustika, Siti Sarah, Dwi Suci Ramadhani, Fadli Julizar, Ferry","doi":"10.1109/ICCoSITE57641.2023.10127775","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127775","url":null,"abstract":"Citrus fruit is a fruit that has good vitamins and is popular with the public. This fruit also has various types with different benefits. Each type of orange also has a variety of colors. Types of oranges can be checked manually by looking directly at the color and texture of the fruit. This manual method is very simple but also very subjective because of the different understanding of each person about the types of oranges. Therefore, this research discusses and explains how to determine the type of fruit by comparing several methods, namely using the SVM method (Support Vector Machine), the CNN method (Convolutional Neural Network), the K-NN method (K-Nearest Neighbor), and the Naïve Bayes method by taking several samples of citrus fruit images consisting of sweet oranges, lemons and limes using a mobile phone camera. The total dataset used in this study is 90 datasets consisting of 30 sweet orange images, 30 lime images and 30 lemon images. Of the 90 datasets are divided into training data and test data. From the results of the study, it was obtained that the accuracy of compatibility with a percentage of 100% using the CNN method (Convolutional Neural Network).","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"67 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":"122811234","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}
Indoor positioning system determine the position of objects in a closed room or story building. This system can determine not only the position but also the orientation and direction of a person's movement. This research uses Wi-Fi (Wireless Fidelity) a network technology that utilizes wireless technology and can work at frequencies of 2.4 GHz and 5.8 GHz. The aims to produce a system that can monitor the presence of employees. This makes the supervisor's work more effective because it can unify based on the information displayed on the android application. Based on observation and testing that has been done, the proposed system can display BSSID as MAC address and SSID from user data by authentication by admin. The system can monitor the user's position in the faculty office area with the application of the K-Nearest Neighbor (KNN) algorithm and the calculation of Received Signal Strength Indication (RSSI) and using the Fingerprinting method with an average Euclidean distance accuracy of 2.37 meters and able to display the user's position with a 100% success percentage. Then, the system is able to read the value of RSSI with 2.08% error.
{"title":"Indoor Positioning System Based on BSSID on Office Wi-Fi Network","authors":"Ratna Aisuwarya, Rian Ferdian, Indah Hestina Yulianti","doi":"10.1109/ICCoSITE57641.2023.10127734","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127734","url":null,"abstract":"Indoor positioning system determine the position of objects in a closed room or story building. This system can determine not only the position but also the orientation and direction of a person's movement. This research uses Wi-Fi (Wireless Fidelity) a network technology that utilizes wireless technology and can work at frequencies of 2.4 GHz and 5.8 GHz. The aims to produce a system that can monitor the presence of employees. This makes the supervisor's work more effective because it can unify based on the information displayed on the android application. Based on observation and testing that has been done, the proposed system can display BSSID as MAC address and SSID from user data by authentication by admin. The system can monitor the user's position in the faculty office area with the application of the K-Nearest Neighbor (KNN) algorithm and the calculation of Received Signal Strength Indication (RSSI) and using the Fingerprinting method with an average Euclidean distance accuracy of 2.37 meters and able to display the user's position with a 100% success percentage. Then, the system is able to read the value of RSSI with 2.08% error.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"70 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132570062","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/ICCoSITE57641.2023.10127782
A. Suraji, A. Sudjianto, R. Riman, Candra Aditya, Aviv Yuniar Rahman, Rangga Pahlevi Putra
Identification of road surface infrastructure defects is a very important requirement and requires fast and accurate information. The purpose of this study is to identify road surface defects using recording technology with GPS video. The data collection method was carried out by surveying road defects using GPS video with moving car observation. Furthermore, the image data from the video recording is compiled to determine the condition of the road surface damage in accordance with the coordinates of the road segment. The method of analyzing the types of road damage used the Pavement Condition Index (PCI) method, then a roadmap of road damage conditions was made. The research results using GPS video obtained that the percentage of road surface defects for each type of damage is good 10 %, fair 45%, light poor 35% and heavy poor 10%. The results of the identification of road surface defects with GPS video are generally in accordance with the conditions in the field. From the results of this study, it can be recommended that a road defect survey using GPS video can be used as an alternative survey method and has the advantage of being faster.
{"title":"Moving Car Observation (MCO) for Road Surface Defect Identification Using GPS Video","authors":"A. Suraji, A. Sudjianto, R. Riman, Candra Aditya, Aviv Yuniar Rahman, Rangga Pahlevi Putra","doi":"10.1109/ICCoSITE57641.2023.10127782","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127782","url":null,"abstract":"Identification of road surface infrastructure defects is a very important requirement and requires fast and accurate information. The purpose of this study is to identify road surface defects using recording technology with GPS video. The data collection method was carried out by surveying road defects using GPS video with moving car observation. Furthermore, the image data from the video recording is compiled to determine the condition of the road surface damage in accordance with the coordinates of the road segment. The method of analyzing the types of road damage used the Pavement Condition Index (PCI) method, then a roadmap of road damage conditions was made. The research results using GPS video obtained that the percentage of road surface defects for each type of damage is good 10 %, fair 45%, light poor 35% and heavy poor 10%. The results of the identification of road surface defects with GPS video are generally in accordance with the conditions in the field. From the results of this study, it can be recommended that a road defect survey using GPS video can be used as an alternative survey method and has the advantage of being faster.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"116 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":"134313509","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/ICCoSITE57641.2023.10127774
D. Purwitasari, D. A. Navastara, Y. Findawati, Kresna Adhi Pramana, Agus Budi Raharjo
Dangerous speech is a strong hate speech that causes negative impacts, such as violence, crime, social pressure, trauma, and despair, and can lead to conflicts between groups. Raw data of Twitter texts need the necessary preprocess to obtain the proper training datasets for hate speech or even dangerous one. One reason is how to express hate speech related to mentions or hashtags. Because of the variants of context messages in raw Twitter posts which could be hate speech or not, the problem here is hierarchical and multi-label classification with three label types of hate speech status, issues, and dangerous levels. The issues in this work are about religion, ethnicity, and others. After handling preprocess, the word embedding includes data under-sampling because the dataset is not balanced. Additional stop-word dictionaries to overcome language-related vocabularies are also incorporated. To observe the preprocess effects in the classification problem, some methods of machine learning and deep learning, such as SVM, Bi-LSTM, and BERT are explored. Then we examined after hyper-parameter settings with performance indicators of subset accuracy and Hamming lost for imbalanced, in addition to F1 scores of micro and macro averages.
{"title":"Feature Extraction in Hierarchical Multi-Label Classification for Dangerous Speech Identification on Twitter Texts","authors":"D. Purwitasari, D. A. Navastara, Y. Findawati, Kresna Adhi Pramana, Agus Budi Raharjo","doi":"10.1109/ICCoSITE57641.2023.10127774","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127774","url":null,"abstract":"Dangerous speech is a strong hate speech that causes negative impacts, such as violence, crime, social pressure, trauma, and despair, and can lead to conflicts between groups. Raw data of Twitter texts need the necessary preprocess to obtain the proper training datasets for hate speech or even dangerous one. One reason is how to express hate speech related to mentions or hashtags. Because of the variants of context messages in raw Twitter posts which could be hate speech or not, the problem here is hierarchical and multi-label classification with three label types of hate speech status, issues, and dangerous levels. The issues in this work are about religion, ethnicity, and others. After handling preprocess, the word embedding includes data under-sampling because the dataset is not balanced. Additional stop-word dictionaries to overcome language-related vocabularies are also incorporated. To observe the preprocess effects in the classification problem, some methods of machine learning and deep learning, such as SVM, Bi-LSTM, and BERT are explored. Then we examined after hyper-parameter settings with performance indicators of subset accuracy and Hamming lost for imbalanced, in addition to F1 scores of micro and macro averages.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"31 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":"115326834","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/ICCoSITE57641.2023.10127814
Muhammad Yusuf Firdaus, Septi Andryana
Employee ranking is an activity carried out by companies to rank employees based on the results of criteria that have been assessed. This is done to give an idea to the company how the value results from the criteria that have been obtained by employees. Related to this research, a Decision Support System is needed to rank the best employees, which uses a combination of 2 methods, namely the Analytical Hierarchy Process (AHP) method is used to weight each criterion and to test the consistency between criteria and Višekriterijumsko Kompromisno Rangiranje (VIKOR) is used to solve complex multi-criteria system problems that focus on ranking and selection of an alternative and determining the ideal solution. The criteria used in this research are Work Behavior Value (C1), SKP value (C2) and Work Performance Value (C3). For alternative data, employee data is used. The results of this study indicate that the employee with the highest rank is Hanung Harimba (KR1) with a value of Q = 0 and the employee with the lowest rank is Christina Thiveny (KR8) with a value of Q = 1.
{"title":"Employee Ranking Based On Work Performance Using AHP and VIKOR Methods","authors":"Muhammad Yusuf Firdaus, Septi Andryana","doi":"10.1109/ICCoSITE57641.2023.10127814","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127814","url":null,"abstract":"Employee ranking is an activity carried out by companies to rank employees based on the results of criteria that have been assessed. This is done to give an idea to the company how the value results from the criteria that have been obtained by employees. Related to this research, a Decision Support System is needed to rank the best employees, which uses a combination of 2 methods, namely the Analytical Hierarchy Process (AHP) method is used to weight each criterion and to test the consistency between criteria and Višekriterijumsko Kompromisno Rangiranje (VIKOR) is used to solve complex multi-criteria system problems that focus on ranking and selection of an alternative and determining the ideal solution. The criteria used in this research are Work Behavior Value (C1), SKP value (C2) and Work Performance Value (C3). For alternative data, employee data is used. The results of this study indicate that the employee with the highest rank is Hanung Harimba (KR1) with a value of Q = 0 and the employee with the lowest rank is Christina Thiveny (KR8) with a value of Q = 1.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"27 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":"114317360","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/ICCoSITE57641.2023.10127838
Ghadeer Ismail Khalil, Hafsa Mohammad Sajjad, Manal Sohail, Zahra Ishfaq
Machines can learn through experience, adapt to new input information, and carry out the necessary human-like duties thanks to artificial intelligence (AI). AI adaptation in the education industry has become more significant. This research aimed to determine the role of Artificial Intelligence (AI) on education in the Kingdom of Bahrain from a student-teacher perspective and examine its factors by adapting Technology Acceptance Model (TAM). To fulfil the objectives of this research, efficiency and convenience of implementing AI within education has been examined to further investigate the challenges faced by students and educators. A quantitative and qualitative approach was used to gather data from the universities in Bahrain, with a sample size of 383 determined by the Stratified Sampling method and Purposive Sampling. The analysis of the responses to the conducted survey resulted in a total of 501 responses. The results analysis revealed that both students and instructors believe security and privacy issues to be the most prevalent obstacle to the use of AI in education. Although AI tools and applications cover most of the ethical aspects, data privacy and security issues remain to be important concerns for users. Furthermore, both students and instructors agree that AI supports self- dependent learning, but it might be complex to use without a set of skills and some experience. In addition, the main limitation was the time consumed in collecting data. The research suggests methods to improve the results and overcome future challenges.
{"title":"Role of AI in the Education Sector in the Kingdom of Bahrain","authors":"Ghadeer Ismail Khalil, Hafsa Mohammad Sajjad, Manal Sohail, Zahra Ishfaq","doi":"10.1109/ICCoSITE57641.2023.10127838","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127838","url":null,"abstract":"Machines can learn through experience, adapt to new input information, and carry out the necessary human-like duties thanks to artificial intelligence (AI). AI adaptation in the education industry has become more significant. This research aimed to determine the role of Artificial Intelligence (AI) on education in the Kingdom of Bahrain from a student-teacher perspective and examine its factors by adapting Technology Acceptance Model (TAM). To fulfil the objectives of this research, efficiency and convenience of implementing AI within education has been examined to further investigate the challenges faced by students and educators. A quantitative and qualitative approach was used to gather data from the universities in Bahrain, with a sample size of 383 determined by the Stratified Sampling method and Purposive Sampling. The analysis of the responses to the conducted survey resulted in a total of 501 responses. The results analysis revealed that both students and instructors believe security and privacy issues to be the most prevalent obstacle to the use of AI in education. Although AI tools and applications cover most of the ethical aspects, data privacy and security issues remain to be important concerns for users. Furthermore, both students and instructors agree that AI supports self- dependent learning, but it might be complex to use without a set of skills and some experience. In addition, the main limitation was the time consumed in collecting data. The research suggests methods to improve the results and overcome future challenges.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"40 2 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":"123737592","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/ICCoSITE57641.2023.10127851
Gerry Samhari Ramadhan, Budhi Irawan, C. Setianingsih, Figo Plambudi Dwigantara
A song is a unity of sound that contains a tone and lyrics. A song can contain a variety of emotions. Emotions in the song can arise because of the combination of lyrics and tones that create a beautiful sound and harmony. This research is about the emotional content of the song lyrics. This research began with collecting datasets in the form of song lyrics from kapanlagi.com, liriklaguindonesia.net, and liriklaguanak.com as a provider of song lyrics. Then preprocessing data consists of case folding, tokenizing, stop removal, and stemming. After that, the part of speech (POS) tagging process automatically labels the word in the text according to the word class. Labeling a word, whether it's a verb, adjective, or description, to be able to determine the song's emotional lyrics according to what we listen to takes the right method. The method used is the Naive Bayes Classifier and Particle Swarm Optimization methods, as methods used in performing text classification. In some studies, it was mentioned that the Naive Bayes Classifier method shows good results in the case of the classification of Indonesian text information, with an accuracy of 90%–96% using an inertia weight score of 1.0.
{"title":"Classification of Emotions on Song Lyrics using Naïve Bayes Algorithm and Particle Swarm Optimization","authors":"Gerry Samhari Ramadhan, Budhi Irawan, C. Setianingsih, Figo Plambudi Dwigantara","doi":"10.1109/ICCoSITE57641.2023.10127851","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127851","url":null,"abstract":"A song is a unity of sound that contains a tone and lyrics. A song can contain a variety of emotions. Emotions in the song can arise because of the combination of lyrics and tones that create a beautiful sound and harmony. This research is about the emotional content of the song lyrics. This research began with collecting datasets in the form of song lyrics from kapanlagi.com, liriklaguindonesia.net, and liriklaguanak.com as a provider of song lyrics. Then preprocessing data consists of case folding, tokenizing, stop removal, and stemming. After that, the part of speech (POS) tagging process automatically labels the word in the text according to the word class. Labeling a word, whether it's a verb, adjective, or description, to be able to determine the song's emotional lyrics according to what we listen to takes the right method. The method used is the Naive Bayes Classifier and Particle Swarm Optimization methods, as methods used in performing text classification. In some studies, it was mentioned that the Naive Bayes Classifier method shows good results in the case of the classification of Indonesian text information, with an accuracy of 90%–96% using an inertia weight score of 1.0.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"169 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":"124890137","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/ICCoSITE57641.2023.10127829
Ni Wayan Parwati Septiani, Hendy Agung Setiawan, Mei Lestari, Irwan Agus, Rayung Wulan, A. Irawan, Sutrisno
In Indonesia, batik was not popular among all socio-economic groups until the 20th century. Recently, batik has been considered an essential part of Indonesian culture and heritage. Geometric batik patterns are recognized by their symmetry, horizontal repetition, and vertical and diagonal angles between shapes. Sade is one village located south of Lombok island. Woven fabrics typical of Sade Village have distinctive motifs that differ from those of Sukarara Village, Central Lombok. Sade's batik mostly has geometric patterns that are almost similar. There are 5 motifs in Sade, namely Selolot, kembang komak, tapok kamalo, ragi genep and batang empat. The Sade village’s economy, which mostly relied on the sales of its fabric production, has been placed under an enormous burden by the COVID-19 pandemic. There must be a new and creative way in order to sustain its market penetration. One possible approach is by linking the community of Sade village fabric producers to the nationwide established marketplace. We propose an ML-based mobile web application that is supposed to be used by ordinary users, not only the tourists who visited Sade village. This mobile web main feature is to do the image classification of the aforementioned motifs and to provide a list of Sade village fabric sellers on the marketplace so that interested users may purchase the product. Models were created using the CNN algorithm to classify batik-sade images. CNN is one frequently used deep learning algorithm for image classification. Image datasets consist of training, testing, and validation datasets. The training datasets contain 2398 photos, while the testing and validation datasets each have 480 data. Ten epochs of experimental data revealed that the suggested CNN model has a training loss of 0.0560 and a training accuracy of 0.9805.
在印度尼西亚，直到20世纪，蜡染才在所有社会经济群体中流行起来。最近，蜡染被认为是印尼文化和遗产的重要组成部分。几何蜡染图案是通过它们的对称、水平重复以及形状之间的垂直和对角角来识别的。萨德是位于龙目岛南部的一个村庄。Sade村典型的梭织织物具有与龙目岛中部Sukarara村不同的独特图案。萨德的蜡染大多有几乎相似的几何图案。沙德有5个主题，分别是Selolot, kembang komak, tapok kamalo, ragi genep和batang empat。萨德村的经济主要依赖于面料的销售，新冠肺炎疫情给该村庄带来了巨大的负担。必须有一个新的和创造性的方式来维持它的市场渗透。一种可能的方法是将Sade村的织物生产商社区与全国范围内建立的市场联系起来。我们提出了一个基于ml的移动web应用程序，它应该被普通用户使用，而不仅仅是访问Sade村的游客。这个移动网站的主要功能是对上述图案进行图像分类，并提供市场上萨德村面料卖家的列表，以便感兴趣的用户可以购买产品。使用CNN算法创建模型对蜡染色图像进行分类。CNN是一种常用的深度学习图像分类算法。图像数据集包括训练、测试和验证数据集。训练数据集包含2398张照片，而测试和验证数据集各有480张照片。10个epoch的实验数据表明，本文提出的CNN模型的训练损失为0.0560，训练精度为0.9805。
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