Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13559

Nurul Zalza Bilal Jannah, Kusnawi Kusnawi

{"title":"Comparison of Naïve Bayes and SVM in Sentiment Analysis of Product Reviews on Marketplaces","authors":"Nurul Zalza Bilal Jannah, Kusnawi Kusnawi","doi":"10.33395/sinkron.v8i2.13559","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13559","url":null,"abstract":"At this time more and more people are switching to shopping online in existing marketplaces such as Shopee. Marketplaces provide various advantages and disadvantages to customers such as lower costs and goods sent not according to orders. Product reviews from customers greatly affect the sales level of business people so that sentiment analysis is carried out. The importance of conducting sentiment analysis of product reviews in the marketplace is to add an overview of how the product is received by users. This research uses Naïve Bayes and SVM algorithms for sentiment analysis of beauty care product review datasets obtained from Shopee scraping results. This research implements k fold cross validation for data splitting process of 10 folds. The Naïve Bayes algorithm obtained the highest accuracy value of 85.53% on fold 2 and the lowest accuracy value of 77.16% on fold 3. While the SVM algorithm obtained the highest accuracy value of 88.58% on fold 2 and the lowest accuracy value of 82.99% on fold 7. With this it is stated that SVM can work better for sentiment analysis of beauty care product reviews on the Shopee marketplace because it gets a higher average accuracy value of 86.14% compared to the Naïve Bayes algorithm.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"28 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360700","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}

引用次数: 0

Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods 使用尺度不变特征变换和 Haar 级联分类器方法在复杂背景中检测人脸

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13556

Dyah Kartika Damarsiwi, Elindra Ambar Pambudi, Maulida Ayu Fitriani, Feri Wibowo

{"title":"Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods","authors":"Dyah Kartika Damarsiwi, Elindra Ambar Pambudi, Maulida Ayu Fitriani, Feri Wibowo","doi":"10.33395/sinkron.v8i2.13556","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13556","url":null,"abstract":"Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"36 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358085","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}

引用次数: 0

Hybrid Analysis of Road Service Level Determination Decision Support System 道路服务水平确定决策支持系统的混合分析

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13499

Dodi Guswandi, Maiyozzi Chairi

引用次数: 0

Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments 纪元值对 RNN-LSTM 算法在 Lazada 应用程序评论情感分类中的性能的影响

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13368

Maswan Pratama Putra, Y. Sibaroni

引用次数: 0

Web Program Testing Using Selenium Python: Best Practices and Effective Approaches 使用 Selenium Python 进行网络程序测试：最佳实践和有效方法

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13569

Rusdiansyah Rusdiansyah, N. Suharyanti, Hendra Supendar, Tuslaela Tuslaela

{"title":"Web Program Testing Using Selenium Python: Best Practices and Effective Approaches","authors":"Rusdiansyah Rusdiansyah, N. Suharyanti, Hendra Supendar, Tuslaela Tuslaela","doi":"10.33395/sinkron.v8i2.13569","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13569","url":null,"abstract":"A critical component of software development is testing programs on websites to make sure they run, are secure, and meet user expectations. This article explores efficient methods and best practices for utilizing Selenium with the Python programming language to perform program testing on websites. This study shows the outcomes of Selenium WebDriver-based automated testing on two webpages: https://demoqa.com/text-box and https://demoqa.com/login. Testing was done to assess Selenium's dependability and performance when it comes to completing text fields and completing the login procedure. The goal of the study is to evaluate Selenium WebDriver's dependability and effectiveness in basic testing jobs while spotting any issues that may arise. Using the appropriate locator to fill in text boxes efficiently using Selenium WebDriver ensures that the operation proceeds without major hiccups. The effective completion of the login procedure on the https://demoqa.com/login page further demonstrates the dependability of Selenium WebDriver for handling increasingly complicated interactions, including login. According to the analysis's findings, Selenium WebDriver is a dependable and efficient solution for test automation that performs consistently and steadily under a range of conditions. The research's conclusions highlight how crucial it is to use automation technologies in order to ensure software quality and boost testing effectiveness. Software engineers can detect issues more rapidly and completely test the functionality of applications with Selenium WebDriver, which enhances the overall quality of software development","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"110 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360151","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}

引用次数: 0

Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter 推特上对 2024 年印尼总统大选的情感分析

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13379

Lisyana Damayanti, K. Lhaksmana

{"title":"Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter","authors":"Lisyana Damayanti, K. Lhaksmana","doi":"10.33395/sinkron.v8i2.13379","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13379","url":null,"abstract":"This analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"25 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359038","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}

引用次数: 0

Natural Language Processing-based Summary Algorithm for Understanding Online News 基于自然语言处理的摘要算法用于理解在线新闻

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13600

Duta Pramudya Ramadhan, Djarot Hindarto

{"title":"Natural Language Processing-based Summary Algorithm for Understanding Online News","authors":"Duta Pramudya Ramadhan, Djarot Hindarto","doi":"10.33395/sinkron.v8i2.13600","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13600","url":null,"abstract":"System development that can supply the right content at the right moment is necessary due to the internet news industry's ever-growing volume of information. In this context, this study investigates how Python programming language-based text summarizing methods are used on news platforms. The combination of summarization algorithms with Natural Language Processing techniques is recommended by this study. The primary objective is to automatically shorten news items while keeping the essential details. Several experiments are conducted to test the proposed summary algorithm on various news item kinds. This algorithm places emphasis on maintaining critical information while minimizing duplication and guaranteeing consistency and fluidity in summaries. The outcomes of the experiment demonstrate that the summary algorithm in place is capable of efficiently extracting significant information from the news and creating lucid, understandable summaries. The summary provided a high degree of authenticity to the news material, accurately and succinctly summarizing significant facts from the original piece, according to analysis. The accessibility and convenience of reading news can be increased by using summary algorithms for news, a Python-based news platform. It provides readers with a time-saving solution that enables them to swiftly obtain crucial information. In addition to furthering the development of automated tools for news summaries, this study emphasizes the significance of summary algorithm technology in enabling effective and accessible information consumption in the digital age.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"118 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359906","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}

引用次数: 0

Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients 利用 K 近邻和旋律频率倒频谱系数进行音乐流派分类

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.12912

Tika Pratiwi, Andi Sunyoto, Dhani Ariatmanto

{"title":"Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients","authors":"Tika Pratiwi, Andi Sunyoto, Dhani Ariatmanto","doi":"10.33395/sinkron.v8i2.12912","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.12912","url":null,"abstract":"Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360909","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}

引用次数: 0

Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine 使用 Naive Bayes 算法和支持向量机对移动供应商应用程序评论进行情感分析

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13469

Tiara Sari Ningsih, Teguh Iman Hermanto, Imam Ma'ruf Nugroho

引用次数: 0

Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods 建议采用 MCDM-AHP 和 WASPAS 方法综合遴选长期任用教师

Sinkron Pub Date : 2024-03-31 DOI: 10.33395/sinkron.v8i2.13063

A. Akmaludin, E. G. Sihombing, R. Rinawati, Linda Sari Dewi, E. Arisawati

{"title":"Integrated Selection of Permanent Teacher Appointments Recommended MCDM-AHP and WASPAS Methods","authors":"A. Akmaludin, E. G. Sihombing, R. Rinawati, Linda Sari Dewi, E. Arisawati","doi":"10.33395/sinkron.v8i2.13063","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13063","url":null,"abstract":"The teacher's role is very important in improving the national learning system. Many honorary teachers are empowered in curriculum development in a number of schools who want to collaborate in improving the quality of their students. The purpose of this research is to provide rewards to honorary teachers who have long served for the progress of the nation in the world of education to be appointed as permanent teachers. The selection method was carried out through a criteria weighting technique with the MCDM-AHP method which was integrated with the WASPAS method. The technique of developing the MCDM-AHP method as an eigenvector measurement concept with proof of optimization through mathematical algebra matrices that is correlated with the Expert Choice application to get optimal values. The result optimization value is integrated with the WASPAS method as a determinant of the ranking system for permanent teacher candidates. This method is a unification of the concepts of the weight product model and weight sum model methods, so that it has special stages to support decision making with the WASPAS method. The results of selecting twelve honorary teachers for appointment as permanent teachers can be seen from the acquisition of the Qi optimization value as a ranking. The results of support for decision making for permanent teacher appointments with the highest optimization value were given to TC04 with a weight of 0.878; followed by a significant difference in the next rank. The findings of this study provide evidence that the integration of the MCDM-AHP and WASPAS methods provides continuous optimization results for decision-making support.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"27 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360756","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}

引用次数: 0

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