Pub Date : 2024-07-01DOI: 10.33395/sinkron.v8i3.13631
Habib Mirza Alfansuri, Perdana Suteja Putra, R. A. Zunaidi
Including ease of accessing the internet through mobile devices. The emergence of social media applications, such as virtual friend applications, has also played a role in increasing the number of Internet users, primarily through mobile devices. In addition to functioning as a forum for virtual friends, social media also acts as a means of promotion, one of which is to promote online shopping applications, which contribute to an increase in online shopping transactions in Indonesia. One of the strategic choices taken is to use online shopping platforms to market educational institutions' products in the hope that they can make it easier for customers to shop and stimulate significant growth. Design thinking is used in idea formulation and problem-solving. As for creating applications that describe the emotional desires of users, this research uses the Kansei Engineering approach. Data collection was conducted through questionnaires, interviews, and literature studies. Later, it will generate several selected Kansei Words. Furthermore, to determine the best design that suits user needs, application prototypes are tested through Performance Metrics tests to determine the level of Effectiveness, efficiency, and errors, as well as performance and usability evaluations using System Usability Scale (SUS) questionnaires.
{"title":"Innovative Design of ITTS Mart Application with Design Thinking & System Usability Scale Method","authors":"Habib Mirza Alfansuri, Perdana Suteja Putra, R. A. Zunaidi","doi":"10.33395/sinkron.v8i3.13631","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13631","url":null,"abstract":"Including ease of accessing the internet through mobile devices. The emergence of social media applications, such as virtual friend applications, has also played a role in increasing the number of Internet users, primarily through mobile devices. In addition to functioning as a forum for virtual friends, social media also acts as a means of promotion, one of which is to promote online shopping applications, which contribute to an increase in online shopping transactions in Indonesia. One of the strategic choices taken is to use online shopping platforms to market educational institutions' products in the hope that they can make it easier for customers to shop and stimulate significant growth. Design thinking is used in idea formulation and problem-solving. As for creating applications that describe the emotional desires of users, this research uses the Kansei Engineering approach. Data collection was conducted through questionnaires, interviews, and literature studies. Later, it will generate several selected Kansei Words. Furthermore, to determine the best design that suits user needs, application prototypes are tested through Performance Metrics tests to determine the level of Effectiveness, efficiency, and errors, as well as performance and usability evaluations using System Usability Scale (SUS) questionnaires. ","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840679","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13735
Adinda Aulia Hafizha, Nurfarah Nidatya
In recent years, relations between Indonesia and China have become increasingly cordial. However, a potential source of tension is emerging in the form of a heightened dispute in the South China Sea. The government of Indonesia is considered an ally, however there has been a long-standing negative opinion among Indonesians regarding China, which has influenced the way both the general public and the political elite have perceived the relations between Indonesia and China. This research has two objectives. The first is to examine Indonesian perceptions regarding the South China Sea conflict. The second is to compare the performance of Support Vector Machine (SVM) and Multinomial Naïve Bayes as a method of sentiment analysis. Using 7.051 Indonesian-language posts from social media X as a dataset, the result shows that a significant portion of Indonesians view the dispute negatively, fearing potential escalation and threats to national security. Despite these concerns, there is reason to believe that Indonesia can play a proactive role in resolving the conflict through ASEAN and UNCLOS frameworks. Meanwhile, SVM has been demonstrated to be an effective method for handling sentiment analysis data, achieving an accuracy of 87.95%. This work contributes to the field of sentiment analysis by highlighting social media as a valuable platform and by demonstrating the effectiveness of SVM. Furthermore, the study offers new insights for the field of international relations by analyzing the South China Sea dispute through a machine learning lens, which may lead to the development of novel perspectives.
{"title":"Indonesians Perception on the South China Sea Dispute: Support Vector Machine and Naïve Bayes Approach","authors":"Adinda Aulia Hafizha, Nurfarah Nidatya","doi":"10.33395/sinkron.v8i3.13735","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13735","url":null,"abstract":"In recent years, relations between Indonesia and China have become increasingly cordial. However, a potential source of tension is emerging in the form of a heightened dispute in the South China Sea. The government of Indonesia is considered an ally, however there has been a long-standing negative opinion among Indonesians regarding China, which has influenced the way both the general public and the political elite have perceived the relations between Indonesia and China. This research has two objectives. The first is to examine Indonesian perceptions regarding the South China Sea conflict. The second is to compare the performance of Support Vector Machine (SVM) and Multinomial Naïve Bayes as a method of sentiment analysis. Using 7.051 Indonesian-language posts from social media X as a dataset, the result shows that a significant portion of Indonesians view the dispute negatively, fearing potential escalation and threats to national security. Despite these concerns, there is reason to believe that Indonesia can play a proactive role in resolving the conflict through ASEAN and UNCLOS frameworks. Meanwhile, SVM has been demonstrated to be an effective method for handling sentiment analysis data, achieving an accuracy of 87.95%. This work contributes to the field of sentiment analysis by highlighting social media as a valuable platform and by demonstrating the effectiveness of SVM. Furthermore, the study offers new insights for the field of international relations by analyzing the South China Sea dispute through a machine learning lens, which may lead to the development of novel perspectives.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"23 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841001","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13731
M. Marsono, Asyahri Hadi Nasyuha, Yohanni Syahra
The global pandemic that has hit the world recently has forced educational institutions to adopt online teaching methods. However, choosing an effective online teaching method is a major challenge. This research develops a Decision Support System (DSS) that uses the Fuzzy Multi-Criteria Decision Making (FMCDM) Algorithm to select the best online teaching method. This system is designed to assist decision making in educational institutions by considering various criteria such as learning effectiveness, technology affordability, ease of use, and user satisfaction. This research uses data collection methods that involve surveys from lecturers and students to obtain their preferences and experiences with various online teaching platforms. The data collected is then processed using the FMCDM model to evaluate and rank teaching methods based on predetermined criteria. Fuzzy systems are used to overcome uncertainty and subjectivity in criteria assessment. The results of this research show that the system developed is able to effectively assess and rank various online teaching methods. From the analysis carried out, interactive teaching methods using videos and real-time quizzes received the highest ranking based on predetermined criteria. This suggests that the combination of engaging visual content and high interactivity is highly valued in online teaching contexts
{"title":"Decision Support System for Selecting Online Teaching Methods Using the Fuzzy MCDM Algorithm","authors":"M. Marsono, Asyahri Hadi Nasyuha, Yohanni Syahra","doi":"10.33395/sinkron.v8i3.13731","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13731","url":null,"abstract":"The global pandemic that has hit the world recently has forced educational institutions to adopt online teaching methods. However, choosing an effective online teaching method is a major challenge. This research develops a Decision Support System (DSS) that uses the Fuzzy Multi-Criteria Decision Making (FMCDM) Algorithm to select the best online teaching method. This system is designed to assist decision making in educational institutions by considering various criteria such as learning effectiveness, technology affordability, ease of use, and user satisfaction. This research uses data collection methods that involve surveys from lecturers and students to obtain their preferences and experiences with various online teaching platforms. The data collected is then processed using the FMCDM model to evaluate and rank teaching methods based on predetermined criteria. Fuzzy systems are used to overcome uncertainty and subjectivity in criteria assessment. The results of this research show that the system developed is able to effectively assess and rank various online teaching methods. From the analysis carried out, interactive teaching methods using videos and real-time quizzes received the highest ranking based on predetermined criteria. This suggests that the combination of engaging visual content and high interactivity is highly valued in online teaching contexts","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"19 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853951","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13722
Ichsan Alam Fadillah, Z. Baizal
Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.
{"title":"Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules","authors":"Ichsan Alam Fadillah, Z. Baizal","doi":"10.33395/sinkron.v8i3.13722","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13722","url":null,"abstract":"Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"15 58","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843345","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13716
Sava Irhab Atma Jaya, Junta Zeniarja
Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.
现在，"元心冲击 "已成为许多人日常生活中不可或缺的一部分，可能会对心理健康产生影响。情感分析是了解这些情感影响的窗口，特别是考虑到游戏对心理健康影响的不同研究结果。鉴于支持向量机在情感分析中的有效性，使用支持向量机分析 X 回应 "玄心影响 "至关重要。这项研究旨在加深我们对游戏的心理影响的理解，并为开发游戏玩家心理健康干预措施提供支持。SVM 分类报告显示了良好的精确度：负面情绪为 0.68，中性情绪为 0.63，正面情绪为 0.72。不过，召回率方面，正面评论（0.87）优于负面评论（0.56）和中性评论（0.51），这反映在 F1 分数上，正面情感的 F1 分数最高，为 0.79。通过 174 个负面、216 个中性和 333 个正面支持计数，模型的总体准确率达到了 0.69，有效地根据情感对源信 Impact 评论进行了分类。分析结果表明，正面意见普遍存在，表明玩家对游戏的满意度普遍较高。
{"title":"Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine","authors":"Sava Irhab Atma Jaya, Junta Zeniarja","doi":"10.33395/sinkron.v8i3.13716","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13716","url":null,"abstract":"Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845113","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}
Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.
{"title":"Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification","authors":"Adya Zizwan Putra, Reynaldi Prayugo, Rizki Mudrika Alfanda Siregar, Rizky Syabani, Allwin M. Simarmata","doi":"10.33395/sinkron.v8i3.13723","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13723","url":null,"abstract":"Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"2013 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851765","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13686
Ivfa Tut Tazkiyah, Ari Eko Wardoyo, B. S. Rintyarna
Forecasting the supply of goods is one of the company's planning strategies to increase sales. However, there are several obstacles in forecasting the supply of goods in one of the boutiques in Jember Regency such as manual sales data collection, namely by recording clothing sales data in the sales book. So that there can be errors in predicting the supply of goods in the future. The purpose of this study is to apply a clothing sales forecasting system using the moving average method to forecast the supply of goods. This study applies the waterfall model to build a system with stages of analysis, design, implementation and testing. Analysis will be carried out by collecting data related to system requirements through observation, interviews and literature studies. While at the design stage there are usecase diagrams and system flow diagrams. Furthermore, the implementation stage was carried out in boutiques in Jember Regency by piloting the boutique owners. System testing uses black box testing to ensure there are no system functional errors. The findings show that the system in the form of a website can be run properly and can be accessed as long as there is an internet network. In addition, our system is already running well based on the results of black box testing. So that this system can be used by companies as forecasting considerations in providing inventory of goods.
{"title":"Implementing Moving Average Forecasting System for Apparel Sales: Predicting Inventory Needs with Enhanced Accuracy","authors":"Ivfa Tut Tazkiyah, Ari Eko Wardoyo, B. S. Rintyarna","doi":"10.33395/sinkron.v8i3.13686","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13686","url":null,"abstract":"Forecasting the supply of goods is one of the company's planning strategies to increase sales. However, there are several obstacles in forecasting the supply of goods in one of the boutiques in Jember Regency such as manual sales data collection, namely by recording clothing sales data in the sales book. So that there can be errors in predicting the supply of goods in the future. The purpose of this study is to apply a clothing sales forecasting system using the moving average method to forecast the supply of goods. This study applies the waterfall model to build a system with stages of analysis, design, implementation and testing. Analysis will be carried out by collecting data related to system requirements through observation, interviews and literature studies. While at the design stage there are usecase diagrams and system flow diagrams. Furthermore, the implementation stage was carried out in boutiques in Jember Regency by piloting the boutique owners. System testing uses black box testing to ensure there are no system functional errors. The findings show that the system in the form of a website can be run properly and can be accessed as long as there is an internet network. In addition, our system is already running well based on the results of black box testing. So that this system can be used by companies as forecasting considerations in providing inventory of goods.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"72 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842773","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13734
Yohanes Maria Jonathan Glenn Paskalis, K. O. Bachri
Student organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal results
{"title":"Student Organization Website with E-Voting Feature by Using Student Card Verification Concept Design","authors":"Yohanes Maria Jonathan Glenn Paskalis, K. O. Bachri","doi":"10.33395/sinkron.v8i3.13734","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13734","url":null,"abstract":"Student organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal results","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"88 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838579","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13706
Anita Loi, Ruth N Panjaitan, S. Siregar, Allwin M. Simarmata
A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.
{"title":"Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN)","authors":"Anita Loi, Ruth N Panjaitan, S. Siregar, Allwin M. Simarmata","doi":"10.33395/sinkron.v8i3.13706","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13706","url":null,"abstract":"A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"76 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838738","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 : 2024-07-01DOI: 10.33395/sinkron.v8i3.13721
Rasyid Ihsan Putra Selian, Anik vega Vitianingsih, Slamet Kacung, Anastasia Lidya Maukar, Jack Febrian Rusdi
This study examines the use of Satire Joke as a humorous communication style in conveying criticism of the government through social media. Satire Joke is often used to depict the government's inability to address important social issues, such as slow bureaucratic processes and unfulfilled political promises. The aim of this research is to analyze public sentiment towards Satire Joke expressed on the YouTube social media platform. The methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN) due to their effectiveness in data classification. The results of this study are expected to help gain an understanding of social issues for the community and public knowledge. This research is also expected to contribute to the development of sentiment analysis methods in the future. The analysis results show that 400 data have neutral sentiment, 850 data have negative sentiment, and 947 data have positive sentiment. Based on testing, both Naive Bayes and KNN methods show good performance. The Naive Bayes method achieved the best accuracy of 90.29%, while the KNN method achieved an accuracy of 60.75%.
{"title":"Sentiment Analysis of Public Responses on Social Media to Satire Joke Using Naive Bayes and KNN","authors":"Rasyid Ihsan Putra Selian, Anik vega Vitianingsih, Slamet Kacung, Anastasia Lidya Maukar, Jack Febrian Rusdi","doi":"10.33395/sinkron.v8i3.13721","DOIUrl":"https://doi.org/10.33395/sinkron.v8i3.13721","url":null,"abstract":"This study examines the use of Satire Joke as a humorous communication style in conveying criticism of the government through social media. Satire Joke is often used to depict the government's inability to address important social issues, such as slow bureaucratic processes and unfulfilled political promises. The aim of this research is to analyze public sentiment towards Satire Joke expressed on the YouTube social media platform. The methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN) due to their effectiveness in data classification. The results of this study are expected to help gain an understanding of social issues for the community and public knowledge. This research is also expected to contribute to the development of sentiment analysis methods in the future. The analysis results show that 400 data have neutral sentiment, 850 data have negative sentiment, and 947 data have positive sentiment. Based on testing, both Naive Bayes and KNN methods show good performance. The Naive Bayes method achieved the best accuracy of 90.29%, while the KNN method achieved an accuracy of 60.75%.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"334 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839255","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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