Spam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89.
{"title":"Email spam detection: a comparison of svm and naive bayes using bayesian optimization and grid search parameters","authors":"Dzaky Budiman, Zayyan Zayyan, Ainun Mardiana, Alfira Aulia Mahrani","doi":"10.52465/josre.v2i1.260","DOIUrl":"https://doi.org/10.52465/josre.v2i1.260","url":null,"abstract":"Spam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"661 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140479390","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}
Rina Amalia Putri Rina, D. Lusianti, Faridhatun Faidah
This research analyses the influence of Sales Promotion and product Quality on Purchase Interest through Brand Awareness. This is a significant concern because product quality is an important thing that every company must strive for if it wants to compete in the market. The object of this research is Zoya Kudus. The sampling technique used purposive sampling with the rule of thumb formula to produce a sample of 120. The analysis tool in this research used SEM-AMOS. This research shows that Sales Promotion and Product Quality have a positive and significant effect on Brand Awareness. Sales Promotion and product quality have influenced purchase Intention. Sales promotion on purchasing interest through brand awareness influences partial mediation. Product quality on purchase intention through brand awareness has a mediating influence, but the influence is weak.
{"title":"Impact of sales promotion and product quality on zoya customer purchase interest","authors":"Rina Amalia Putri Rina, D. Lusianti, Faridhatun Faidah","doi":"10.52465/josre.v2i1.243","DOIUrl":"https://doi.org/10.52465/josre.v2i1.243","url":null,"abstract":"This research analyses the influence of Sales Promotion and product Quality on Purchase Interest through Brand Awareness. This is a significant concern because product quality is an important thing that every company must strive for if it wants to compete in the market. The object of this research is Zoya Kudus. The sampling technique used purposive sampling with the rule of thumb formula to produce a sample of 120. The analysis tool in this research used SEM-AMOS. This research shows that Sales Promotion and Product Quality have a positive and significant effect on Brand Awareness. Sales Promotion and product quality have influenced purchase Intention. Sales promotion on purchasing interest through brand awareness influences partial mediation. Product quality on purchase intention through brand awareness has a mediating influence, but the influence is weak.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488671","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}
Yuniar Rahma Adisti, D. Lusianti, Faridhatun Faidah
The needs of society and developing lifestyles will result in hobbies also developing, such as the hobby of adventuring in the wild. One brand of outdoor equipment is Eiger. This research analyzes the influence of Product Design and sales Promotion on Customer Loyalty through Consumer Satisfaction. The object of this research is the Eiger product in Kudus. The data used in this research was by distributing questionnaires via online form. The instrument test in this study used a reliability test and validity test. The analytical tool in this research uses SEM-AMOS. This research shows that product design has no effect on consumer satisfaction. Sales promotions have a positive and significant effect on consumer satisfaction. Product design has a positive and significant effect on customer loyalty. Sales promotions do not affect customer loyalty. Consumer satisfaction does not affect customer loyalty. Product design and sales promotions on customer loyalty through consumer satisfaction have a weak mediating influence. Product design, sales promotions, and consumer satisfaction are important in shaping consumer perceptions of loyalty. This perception will influence customer attitudes and behavior. Therefore, companies must design good strategies so that consumers can behave and behave as expected.
{"title":"Impact of product design and sales promotion on eiger customer loyalty","authors":"Yuniar Rahma Adisti, D. Lusianti, Faridhatun Faidah","doi":"10.52465/josre.v2i1.242","DOIUrl":"https://doi.org/10.52465/josre.v2i1.242","url":null,"abstract":"The needs of society and developing lifestyles will result in hobbies also developing, such as the hobby of adventuring in the wild. One brand of outdoor equipment is Eiger. This research analyzes the influence of Product Design and sales Promotion on Customer Loyalty through Consumer Satisfaction. The object of this research is the Eiger product in Kudus. The data used in this research was by distributing questionnaires via online form. The instrument test in this study used a reliability test and validity test. The analytical tool in this research uses SEM-AMOS. This research shows that product design has no effect on consumer satisfaction. Sales promotions have a positive and significant effect on consumer satisfaction. Product design has a positive and significant effect on customer loyalty. Sales promotions do not affect customer loyalty. Consumer satisfaction does not affect customer loyalty. Product design and sales promotions on customer loyalty through consumer satisfaction have a weak mediating influence. Product design, sales promotions, and consumer satisfaction are important in shaping consumer perceptions of loyalty. This perception will influence customer attitudes and behavior. Therefore, companies must design good strategies so that consumers can behave and behave as expected.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"16 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140497501","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}
Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.
{"title":"Detection and prediction of rice plant diseases using convolutional neural network (CNN) method","authors":"Reyhan Dzaki Sheva Pahlawanto, Halimah Salsabila, Kusuma Ratna Pratiwi","doi":"10.52465/josre.v2i1.254","DOIUrl":"https://doi.org/10.52465/josre.v2i1.254","url":null,"abstract":"Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"123 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140501602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this research is to increase the estimated service process time by creating an online-based car service ordering application at the Sinar Jaya repair shop and introducing information about Sinar Jaya car service services to the wider public. In this information systems research, the author of this research software development method uses the waterfall model development method. By implementing a waterfall, the research stages carried out by researchers start from data analysis, system analysis, system design, implementation, and testing. Creating a website-based car service ordering application at the Sinar Jaya Workshop can help customers find out the information available at the Sinar Jaya Workshop and the car service ordering process. Before there was an application, customers had to come to the location to place an order, so it took a long time to arrive at the location. So, with the online booking application, you can save time in the service process and get a queue number online. The data processing process for ordering car services becomes more practical so that it can be processed properly by the admin.
{"title":"Application design for web-based car services to increase work time estimates","authors":"Shinta Febriyanti, Solehatin Solehatin","doi":"10.52465/josre.v2i1.231","DOIUrl":"https://doi.org/10.52465/josre.v2i1.231","url":null,"abstract":"The aim of this research is to increase the estimated service process time by creating an online-based car service ordering application at the Sinar Jaya repair shop and introducing information about Sinar Jaya car service services to the wider public. In this information systems research, the author of this research software development method uses the waterfall model development method. By implementing a waterfall, the research stages carried out by researchers start from data analysis, system analysis, system design, implementation, and testing. Creating a website-based car service ordering application at the Sinar Jaya Workshop can help customers find out the information available at the Sinar Jaya Workshop and the car service ordering process. Before there was an application, customers had to come to the location to place an order, so it took a long time to arrive at the location. So, with the online booking application, you can save time in the service process and get a queue number online. The data processing process for ordering car services becomes more practical so that it can be processed properly by the admin.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"17 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511542","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}
In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.
{"title":"Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm","authors":"Putri Susi Sundari, Mahardika Khafidz Putra","doi":"10.52465/josre.v2i1.176","DOIUrl":"https://doi.org/10.52465/josre.v2i1.176","url":null,"abstract":"In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled \"Ames Housing dataset\" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135490184","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}
Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
{"title":"Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset","authors":"Nadhifa Reska, Khansa Tsabita","doi":"10.52465/josre.v1i2.170","DOIUrl":"https://doi.org/10.52465/josre.v1i2.170","url":null,"abstract":"Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130426775","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}
This research aims to classify the academic performance of students who are successful and who have dropped out of school with high accuracy so that these matters can be addressed quickly. Things like this need fast handling to find out what factors influence it. In addition, this research was conducted to test how good the random forest algorithm is in classifying a problem. Random forest, which includes an algorithm that is commonly used for classifying a problem. By using the random forest algorithm, the accuracy results will be better than a single decision tree. This algorithm is quite good at handling and managing large datasets. From this study it can be concluded that this method can provide good prediction accuracy with a fairly high level of accuracy, namely 89%. Utilization of this random forest can be an alternative in classifying student academic achievement. This algorithm can work well in handling large datasets. This study discusses how the use of Random Forest can work to classify students' academic performance.
{"title":"Increased accuracy in predicting student academic performance using random forest classifier","authors":"Aditya Fajar Mulyana, Wiyanda Puspita, J. Jumanto","doi":"10.52465/josre.v1i2.169","DOIUrl":"https://doi.org/10.52465/josre.v1i2.169","url":null,"abstract":"\u0000\u0000\u0000\u0000This research aims to classify the academic performance of students who are successful and who have dropped out of school with high accuracy so that these matters can be addressed quickly. Things like this need fast handling to find out what factors influence it. In addition, this research was conducted to test how good the random forest algorithm is in classifying a problem. Random forest, which includes an algorithm that is commonly used for classifying a problem. By using the random forest algorithm, the accuracy results will be better than a single decision tree. This algorithm is quite good at handling and managing large datasets. From this study it can be concluded that this method can provide good prediction accuracy with a fairly high level of accuracy, namely 89%. Utilization of this random forest can be an alternative in classifying student academic achievement. This algorithm can work well in handling large datasets. This study discusses how the use of Random Forest can work to classify students' academic performance.\u0000\u0000\u0000\u0000","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273830","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}
Rofik Rofik, Tasya Fitria Anggraini, Budi Prasetiyo, C. Ka
Sport is a physical and mental activity that is beneficial for people to maintain the body and develop the quality of health. This makes exercise an activity that needs to be done for everyone to maintain their stamina. However, the lack of information about places, facilities, partners, and sports events is a strong reason in terms of reducing student motivation in carrying out sports activities themselves. The purpose of this research is none other than to design an application that can help students get all sports information. These things are none other than to foster a strong desire to do sports activities. Through technology smartphone which has been owned by the wider community, this research creates a solution by designing an application called "Go-Sport". This study uses the "Design Thinking" method, which focuses on finding and understanding user needs to obtain an optimal solution in the form of the results of the features to be made. From this research, a design or prototype of the "Go-Sport" application was produced which is ready to be implemented and tested on users.
{"title":"Application go-sport as a solution to search information on facilities, places, partners, and sports events for students","authors":"Rofik Rofik, Tasya Fitria Anggraini, Budi Prasetiyo, C. Ka","doi":"10.52465/josre.v1i2.164","DOIUrl":"https://doi.org/10.52465/josre.v1i2.164","url":null,"abstract":"Sport is a physical and mental activity that is beneficial for people to maintain the body and develop the quality of health. This makes exercise an activity that needs to be done for everyone to maintain their stamina. However, the lack of information about places, facilities, partners, and sports events is a strong reason in terms of reducing student motivation in carrying out sports activities themselves. The purpose of this research is none other than to design an application that can help students get all sports information. These things are none other than to foster a strong desire to do sports activities. Through technology smartphone which has been owned by the wider community, this research creates a solution by designing an application called \"Go-Sport\". This study uses the \"Design Thinking\" method, which focuses on finding and understanding user needs to obtain an optimal solution in the form of the results of the features to be made. From this research, a design or prototype of the \"Go-Sport\" application was produced which is ready to be implemented and tested on users.","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134029288","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}
In this fully digital age, a lot of individuals require an internet connection. A reliable network must be able to handle this need. Therefore, a stable network needs to establish and maintained correctly. A reliable internet connection is required at Muhammadiyah University of Surakarta's Faculty of Health Sciences to enhance student and lecturer activities in the educational process. This study will analyze the University of Muhammadiyah Surakarta's Faculty of Health Sciences internet network quality. Using Quality of Service (QOS) methods, the study estimated the quality performance of the existing network. The test measures the throughput, jitter, delay, and packet loss parameters using Wireshark. The result revealed that the Faculty of Health Sciences at University of Muhammadiyah Surakarta had a very good internet network, with a throughput value of 403.487 kbit/s with an index of 4 indicates an Outstanding category, a packet loss value of 6.2% with an index of 3 indicating a good category, a delay value of 16.691 ms with an index of 4 indicates an Outstanding category, and the last is the jitter value of 0.04913 ms with an index of 3 indicating an Outstanding category. Overall, the QoS value of internet network services at the Faculty of Health Sciences, University of Muhammadiyah Surakarta, is 3,5 or 87.5% in the satisfactory category.
在这个完全数字化的时代，很多人都需要互联网连接。一个可靠的网络必须能够满足这种需求。因此，需要正确地建立和维护一个稳定的网络。泗水穆罕默迪亚大学卫生科学学院需要可靠的互联网连接，以加强学生和讲师在教育过程中的活动。本研究将分析穆罕默迪亚苏拉塔大学健康科学学院的互联网网络质量。利用服务质量(QOS)方法，对现有网络的质量性能进行了估计。测试使用Wireshark检测吞吐量、抖动、时延、丢包等参数。结果显示,穆罕默迪亚大学健康科学学院苏拉卡尔塔有一个非常良好的互联网络,与吞吐量值403.487 kbit / s 4的一个索引显示一个杰出的类别,6.2%的丢包值的指数3表明一个好的类别,16.691毫秒的延迟值4的一个索引显示一个杰出的类别,最后是0.04913毫秒的抖动值的指数3显示一个杰出的类别。总体而言，穆罕默迪亚苏拉塔大学健康科学学院的互联网网络服务的QoS值为3.5或87.5%，属于令人满意的类别。
{"title":"Performance and quality measurement of internet network services at muhammadiyah university of surakarta's faculty of health sciences with QOS parameter","authors":"Firasyana Lathifah, Ariq Fadhil Musyaffa","doi":"10.52465/josre.v1i2.148","DOIUrl":"https://doi.org/10.52465/josre.v1i2.148","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000In this fully digital age, a lot of individuals require an internet connection. A reliable network must be able to handle this need. Therefore, a stable network needs to establish and maintained correctly. A reliable internet connection is required at Muhammadiyah University of Surakarta's Faculty of Health Sciences to enhance student and lecturer activities in the educational process. This study will analyze the University of Muhammadiyah Surakarta's Faculty of Health Sciences internet network quality. Using Quality of Service (QOS) methods, the study estimated the quality performance of the existing network. The test measures the throughput, jitter, delay, and packet loss parameters using Wireshark. The result revealed that the Faculty of Health Sciences at University of Muhammadiyah Surakarta had a very good internet network, with a throughput value of 403.487 kbit/s with an index of 4 indicates an Outstanding category, a packet loss value of 6.2% with an index of 3 indicating a good category, a delay value of 16.691 ms with an index of 4 indicates an Outstanding category, and the last is the jitter value of 0.04913 ms with an index of 3 indicating an Outstanding category. Overall, the QoS value of internet network services at the Faculty of Health Sciences, University of Muhammadiyah Surakarta, is 3,5 or 87.5% in the satisfactory category. \u0000 \u0000 \u0000 \u0000","PeriodicalId":105983,"journal":{"name":"Journal of Student Research Exploration","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127190137","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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