Pub Date : 2024-03-31DOI: 10.33395/sinkron.v8i2.13412
Mukhsin Nuzula, Y. Away, Kahlil Kahlil, Andri Novandri
The protection of data security is crucial, particularly when dealing with the transmission of sensitive information through communication networks. This article explores the Advanced Encryption Standard 128-bit (AES-128) algorithm as an effective and secure cryptographic solution. The paper proposes the dynamic development of the AES-128 cryptography method by implementing a dynamic key to enhance the security of employee attendance data. The dynamic key involves changing the encryption key every minute, providing an additional security layer and reducing the risk of decryption by unauthorized parties. Test results indicate that the dynamic AES-128 encryption algorithm demonstrates optimal performance. The consecutive encryption and decryption speeds for sending attendance data are 14656.78 bit/s and 21898.21 bit/s, respectively. The consistent duration of the encryption and decryption processes, at 6.66ms and 2.44ms, along with an Avalanche Effect rate of 50.73% and an Entropy of 6.67 bit/symbol, emphasizes the algorithm’s efficiency and stability. This research not only reinforces the desired level of security but also outperforms several previous studies. Analyzed performance data indicates that this method is not only efficient but also stable in maintaining data security, addressing significant variations in data length. Thus, the implementation of dynamic AES-128 cryptography in attendance systems provides a significant advantage in addressing information security challenges in the current digital era.
{"title":"Optimizing Attendance Data Security by Implementing Dynamic AES-128 Encryption","authors":"Mukhsin Nuzula, Y. Away, Kahlil Kahlil, Andri Novandri","doi":"10.33395/sinkron.v8i2.13412","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13412","url":null,"abstract":"The protection of data security is crucial, particularly when dealing with the transmission of sensitive information through communication networks. This article explores the Advanced Encryption Standard 128-bit (AES-128) algorithm as an effective and secure cryptographic solution. The paper proposes the dynamic development of the AES-128 cryptography method by implementing a dynamic key to enhance the security of employee attendance data. The dynamic key involves changing the encryption key every minute, providing an additional security layer and reducing the risk of decryption by unauthorized parties. Test results indicate that the dynamic AES-128 encryption algorithm demonstrates optimal performance. The consecutive encryption and decryption speeds for sending attendance data are 14656.78 bit/s and 21898.21 bit/s, respectively. The consistent duration of the encryption and decryption processes, at 6.66ms and 2.44ms, along with an Avalanche Effect rate of 50.73% and an Entropy of 6.67 bit/symbol, emphasizes the algorithm’s efficiency and stability. This research not only reinforces the desired level of security but also outperforms several previous studies. Analyzed performance data indicates that this method is not only efficient but also stable in maintaining data security, addressing significant variations in data length. Thus, the implementation of dynamic AES-128 cryptography in attendance systems provides a significant advantage in addressing information security challenges in the current digital era.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"9 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359295","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13561
Kartika Mariskhana, Ita Dewi Sintawati, Widiarina Widiarina
Many local governments now prioritize human development when trying to raise the standard of living and welfare of their citizens. Developing effective development policies in West Java, one of Indonesia's most populous provinces, requires a thorough understanding of human development patterns in various districts and cities. Using the Human Development Index (HDI) as the primary indicator, we examine regional development patterns in this study using machine learning techniques, specifically clustering analysis. This study's scope includes an HDI analysis for each of West Java's 27 districts and cities from 2017 to 2022. Finding clusters of districts or cities with comparable human development traits and comparing and contrasting them are our primary goals. We provide a solution that allows for improved mapping and comprehension of human development patterns in West Java by utilizing the Python programming language as the primary tool and the K-Means clustering algorithm. The study's findings indicate that there are three major categories of districts and cities, each with a distinct human development pattern. By using clustering analysis, we can determine which districts or cities within each group have the highest and lowest levels of human development. This information helps policymakers plan more inclusive and sustainable development. In conclusion, a clustering analysis approach based on machine learning can be a helpful tool for understanding and creating more focused and efficient regional development policies in West Java and other areas.
{"title":"Exploring Regional Development Patterns using Machine Learning: A Python-based Clustering Analysis of Human Development Index in West Java","authors":"Kartika Mariskhana, Ita Dewi Sintawati, Widiarina Widiarina","doi":"10.33395/sinkron.v8i2.13561","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13561","url":null,"abstract":"Many local governments now prioritize human development when trying to raise the standard of living and welfare of their citizens. Developing effective development policies in West Java, one of Indonesia's most populous provinces, requires a thorough understanding of human development patterns in various districts and cities. Using the Human Development Index (HDI) as the primary indicator, we examine regional development patterns in this study using machine learning techniques, specifically clustering analysis. This study's scope includes an HDI analysis for each of West Java's 27 districts and cities from 2017 to 2022. Finding clusters of districts or cities with comparable human development traits and comparing and contrasting them are our primary goals. We provide a solution that allows for improved mapping and comprehension of human development patterns in West Java by utilizing the Python programming language as the primary tool and the K-Means clustering algorithm. The study's findings indicate that there are three major categories of districts and cities, each with a distinct human development pattern. By using clustering analysis, we can determine which districts or cities within each group have the highest and lowest levels of human development. This information helps policymakers plan more inclusive and sustainable development. In conclusion, a clustering analysis approach based on machine learning can be a helpful tool for understanding and creating more focused and efficient regional development policies in West Java and other areas.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"29 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358188","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 : 2024-03-31DOI: 10.33395/sinkron.v9i2.13370
A. D. A. Putra, Z. Baizal
In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.
{"title":"Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering","authors":"A. D. A. Putra, Z. Baizal","doi":"10.33395/sinkron.v9i2.13370","DOIUrl":"https://doi.org/10.33395/sinkron.v9i2.13370","url":null,"abstract":"In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"32 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358283","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13418
Eko Aziz Apriadi, S. Sriyanto, Sri Lestari, Suhendro Yusuf Irianto
This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments
{"title":"Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction","authors":"Eko Aziz Apriadi, S. Sriyanto, Sri Lestari, Suhendro Yusuf Irianto","doi":"10.33395/sinkron.v8i2.13418","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13418","url":null,"abstract":"This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"25 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359876","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13540
RZ Abdul Aziz, Anas Ikhsanudin, M. Hasibuan
As we know, the role of the security system has a very important role for a state institution to provide security and comfort in carrying out its functions, such as the ABC central bank. A good security system is a security system that is supported by a reliable electronic security system and is composed of several components such as a CCTV monitoring system, Access Control System (ACS), Security Alarm System (SAS), and Fire Alarm System (FAS). This system is very necessary to provide support for the duties of these state institutions to protect devices, data and electronic infrastructure from potential threats and security risks. The main functions of electronic security systems include prevention, detection, response to incidents, and recovery after disturbances/disasters. For this reason, efforts are needed to provide an evaluation of the system maturity level and information security management as a form of risk management to maintain the continuity of system use. This research uses the INDEKS KAMI 4.1 to map ESS governance maturity and the OCTAVE Allegro method to analyze information security management. From the analysis carried out, it has been concluded that the ESS implementation has been operated well in accordance with the security system requirements and has reached a good level of governance maturity. Information security management analysis carried out using the OCTAVE Allegro method has succeeded in identifying information security management with the result that information security management has been implemented well. This is proven by the existence of indicators, namely CCTV recording data, log systems as information assets that have been managed and distributed according to authority
{"title":"GOVERNANCE EVALUATION ELECTRONIC SECURITY SYSTEM (ESS) (Case Study: ABC Central Bank)","authors":"RZ Abdul Aziz, Anas Ikhsanudin, M. Hasibuan","doi":"10.33395/sinkron.v8i2.13540","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13540","url":null,"abstract":"As we know, the role of the security system has a very important role for a state institution to provide security and comfort in carrying out its functions, such as the ABC central bank. A good security system is a security system that is supported by a reliable electronic security system and is composed of several components such as a CCTV monitoring system, Access Control System (ACS), Security Alarm System (SAS), and Fire Alarm System (FAS). This system is very necessary to provide support for the duties of these state institutions to protect devices, data and electronic infrastructure from potential threats and security risks. The main functions of electronic security systems include prevention, detection, response to incidents, and recovery after disturbances/disasters. For this reason, efforts are needed to provide an evaluation of the system maturity level and information security management as a form of risk management to maintain the continuity of system use. This research uses the INDEKS KAMI 4.1 to map ESS governance maturity and the OCTAVE Allegro method to analyze information security management. From the analysis carried out, it has been concluded that the ESS implementation has been operated well in accordance with the security system requirements and has reached a good level of governance maturity. Information security management analysis carried out using the OCTAVE Allegro method has succeeded in identifying information security management with the result that information security management has been implemented well. This is proven by the existence of indicators, namely CCTV recording data, log systems as information assets that have been managed and distributed according to authority","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"103 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360188","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13275
Sayyid Muh. Raziq Olajuwon, Kusrini Kusrini, Kusnawi Kusnawi
This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .
{"title":"Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting","authors":"Sayyid Muh. Raziq Olajuwon, Kusrini Kusrini, Kusnawi Kusnawi","doi":"10.33395/sinkron.v8i2.13275","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13275","url":null,"abstract":"This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140361145","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13574
Hariono Ponco Adi, Ari Eko Wardoyo, Habibatul Azizah
This study comprehensively analyzes the performance and usability of the SIMPKB website in the context of teacher professional development. This research carries a qualitative descriptive approach with the aim of deeply understanding the performance and usability of the SIMPKB website. This research consists of two complementary stages, the first involves performance testing using GT Metrix software, and the second phase focuses on in-depth interviews with 5 driving teachers in Kabupaten Jember by applying the concept of the Five Dimensions of Usability (5E) model. Through performance testing using GT Metrix and 5E interviews with driving teachers, significant findings have been revealed. Although SIMPKB shows relatively good response speeds, there are areas of improvement that can be improved, especially in terms of loading times and Largest Contentful Paint (LCP). The 5E evaluation of the mobilizing teacher provides an in-depth perspective on the effectiveness, efficiency, engagement, errors, and ease of learning on the platform. The test and interview results complement each other, providing a holistic picture of SIMPKB's condition and potential improvement. Improvement recommendations, which involve improving response speed and improving usability, can be a foothold for improving the user experience. Further research is recommended to explore optimizing technical performance, implementing more intuitive interface designs, and evaluating the impact of implementing improvements on user effectiveness. By adopting these recommendations, SIMPKB can continue to develop as an effective, efficient, and user-friendly platform in supporting teacher professional development.
本研究全面分析了 SIMPKB 网站在教师专业发展方面的表现和可用性。本研究采用定性描述方法，旨在深入了解 SIMPKB 网站的性能和可用性。本研究包括两个互补的阶段，第一阶段是使用 GT Metrix 软件进行性能测试，第二阶段是通过应用可用性五维度（5E）模型的概念，与 5 名在 Kabupaten Jember 开课的教师进行深入访谈。通过使用 GT Metrix 软件进行性能测试和对驾驶教师进行 5E 访谈，得出了重要结论。尽管 SIMPKB 的响应速度相对较好，但仍有可以改进的地方，特别是在加载时间和最大内容油漆（LCP）方面。对动员教师的 5E 评估深入透视了该平台的有效性、效率、参与度、错误和学习难易程度。测试和访谈结果相辅相成，全面反映了 SIMPKB 的状况和潜在的改进空间。改进建议涉及提高响应速度和可用性，可作为改善用户体验的立足点。建议进一步开展研究，探索如何优化技术性能，实施更直观的界面设计，并评估实施改进措施对用户效率的影响。通过采纳这些建议，SIMPKB 可以继续发展成为一个有效、高效、用户友好的支持教师专业发展的平台。
{"title":"An In-Depth Analysis of SIMPKB: Revealing Performance Tests and Efficiency from a User Experience","authors":"Hariono Ponco Adi, Ari Eko Wardoyo, Habibatul Azizah","doi":"10.33395/sinkron.v8i2.13574","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13574","url":null,"abstract":"This study comprehensively analyzes the performance and usability of the SIMPKB website in the context of teacher professional development. This research carries a qualitative descriptive approach with the aim of deeply understanding the performance and usability of the SIMPKB website. This research consists of two complementary stages, the first involves performance testing using GT Metrix software, and the second phase focuses on in-depth interviews with 5 driving teachers in Kabupaten Jember by applying the concept of the Five Dimensions of Usability (5E) model. Through performance testing using GT Metrix and 5E interviews with driving teachers, significant findings have been revealed. Although SIMPKB shows relatively good response speeds, there are areas of improvement that can be improved, especially in terms of loading times and Largest Contentful Paint (LCP). The 5E evaluation of the mobilizing teacher provides an in-depth perspective on the effectiveness, efficiency, engagement, errors, and ease of learning on the platform. The test and interview results complement each other, providing a holistic picture of SIMPKB's condition and potential improvement. Improvement recommendations, which involve improving response speed and improving usability, can be a foothold for improving the user experience. Further research is recommended to explore optimizing technical performance, implementing more intuitive interface designs, and evaluating the impact of implementing improvements on user effectiveness. By adopting these recommendations, SIMPKB can continue to develop as an effective, efficient, and user-friendly platform in supporting teacher professional development.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"35 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140358111","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13498
Farhan Dardiri
In an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%.
{"title":"Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection","authors":"Farhan Dardiri","doi":"10.33395/sinkron.v8i2.13498","DOIUrl":"https://doi.org/10.33395/sinkron.v8i2.13498","url":null,"abstract":"In an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%.","PeriodicalId":34046,"journal":{"name":"Sinkron","volume":"15 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359575","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 : 2024-03-31DOI: 10.33395/sinkron.v8i2.13264
Muhammad Yiko Satriyawibawa, P. Andono, Lim Way Soong, Ng Poh Kiat
Steganography functions as a technique for embedding messages or data in various forms of media, such as images, audio, video, or text, with the aim of avoiding detection by unauthorized parties. Steganography techniques can be used as a solution to hide and protect data. In this research, steganography will be carried out using images as the transmission object. This research was conducted to offer a modification of the Least Significant Bit (LSB) steganography technique using the LSB-2 method with Brotli compression and base64 encoding. Modification and use of Brotli compression and base64 coding aims to increase the message capacity that can be embedded in a transmission object while maintaining the quality of the transmission object. Experiments using small data and big data. The experimental results will be presented in tabular form by comparing the original image with the steganographically processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as a comparison. The experiments carried out resulted in an increase in image capacity by reducing capacity usage with an average of 47.13% for small data and an average of 71.34%. The big data experiment resulted in an increase in the PSNR value of around 3.49%, accompanied by a decrease in the average MSE value of 33.85%, and a constant SSIM value of 0.9999, thus proving that the proposed method was successful in increasing image capacity and improving stego-image quality. when embedding big data.
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Pub Date : 2024-03-31DOI: 10.33395/sinkron.v8i2.13514
Muhammad Isomul Irfan
Pesantren is an Islamic educational institution that plays a central role in the development of education in Indonesia. Although originally established for Islamic religious education (Pendidikan Agama Islam or PAI), pesantren has evolved into an educational institution that contributes to both scholarly and community service aspects. According to the regulations set by the Ministry of Religious Affairs of the Republic of Indonesia under Number 31 of 2020, pesantren is a community-based institution that upholds the teachings of Islam rahmatan lil'alamin (Islam as a blessing for all) and the noble values of the Indonesian nation. Pesantren education is efficient because it is conducted in a boarding school setting, which shapes the character of its students or 'santri.' However, the current method of determining the grade levels of santri is often inaccurate, relying solely on the average scores of entrance exams without considering essential aspects of subjects. This leads to a decrease in students' interest in learning and delays in achieving higher levels of education. By utilizing data mining techniques, such as the C4.5 algorithm based on Forward Selection, it is possible to address this issue and enhance the accuracy of placing santri into their appropriate grade levels at the Nurul Jadid Paiton Probolinggo pesantren. This improvement can make the pesantren education system more effective in managing student learning
长老会是一个伊斯兰教育机构，在印度尼西亚的教育发展中发挥着核心作用。尽管长老会最初是为伊斯兰宗教教育（Pendidikan Agama Islam 或 PAI）而设立的，但现在已发展成为一个同时为学术和社区服务做出贡献的教育机构。根据印度尼西亚共和国宗教事务部 2020 年第 31 号条例的规定，长老会是一个以社区为基础的机构，维护伊斯兰教的教义 rahmatan lil'alamin（伊斯兰教是所有人的祝福）和印度尼西亚民族的崇高价值观。寄宿学校的教育环境塑造了学生（"santri"）的性格，因此非常高效。然而，目前确定 "学生 "年级的方法往往不准确，仅仅依靠入学考试的平均分，而不考虑科目的基本方面。这导致学生学习兴趣下降，迟迟无法达到更高的教育水平。通过利用数据挖掘技术，如基于前向选择的 C4.5 算法，可以解决这一问题，并提高努鲁尔-贾迪德-派顿-普罗波林戈学校（Nurul Jadid Paiton Probolinggo pesantren）将学生安排到相应年级的准确性。这一改进可使长老会教育系统更有效地管理学生的学习。
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